Advances in Child Development and Behavior

VOLUME FORTY EIGHT

ADVANCES IN CHILD DEVELOPMENT AND BEHAVIOR

ADVANCES IN CHILD DEVELOPMENT AND BEHAVIOR Series Editor

JANETTE B. BENSON Morgridge College of Education, Department of Psychology, University of Denver, Denver, Colorado, USA

VOLUME FORTY EIGHT

ADVANCES IN CHILD DEVELOPMENT AND BEHAVIOR Edited by

JANETTE B. BENSON Morgridge College of Education, Department of Psychology, University of Denver, Denver, Colorado, USA

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CONTENTS Contributors Preface

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1. Brains for All the Ages: Structural Neurodevelopment in Infants and Children from a Life-Span Perspective

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John E. Richards and Wanze Xie 1. Structural Neurodevelopment and Behavior 2. Neurodevelopmental MRI Database 3. Applications to MRI 4. Relation to Brain–Behavior Development Acknowledgments References

2. The Importance of Puberty for Adolescent Development: Conceptualization and Measurement

2 5 22 42 43 44

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Sheri A. Berenbaum, Adriene M. Beltz, and Robin Corley 1. 2. 3. 4.

Puberty and Adolescent Development Defining and Measuring Puberty Evidence for Pubertal Influences on Adolescent Development Assumptions, Strengths, and Limitations of Work on Puberty–Behavior Links 5. Conclusions and Future Directions References

3. Foundations of Children's Numerical and Mathematical Skills: The Roles of Symbolic and Nonsymbolic Representations of Numerical Magnitude

54 55 66 73 82 85

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Ian M. Lyons and Daniel Ansari 1. 2. 3. 4.

Introduction An Approximate System for the Representation of Numerical Magnitude The Symbolic Representation of Numerical Magnitude The Relationship Between Symbolic and Nonsymbolic Representations of Numerical Magnitude 5. Summary and Conclusions References

94 94 99 103 109 112

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4. Developmental Origins of the Face Inversion Effect

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Cara H. Cashon and Nicholas A. Holt 1. The Face Inversion Effect 2. Inversion Effects Over the First Year of Life 3. Making Sense of It All References

5. Early Testimonial Learning: Monitoring Speech Acts and Speakers

118 120 140 145

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Elizabeth Stephens, Sarah Suarez, and Melissa Koenig 1. Epistemic Vigilance: Can Testimonial Learning Exist Without It? 2. Children's Evaluations of Speaker Messages 3. Children's Evaluations of Speakers 4. Concluding Thoughts Acknowledgment References

6. Beyond Sally's Missing Marble: Further Development in Children's Understanding of Mind and Emotion in Middle Childhood

152 154 166 177 178 178

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Kristin Hansen Lagattuta, Hannah J. Kramer, Katie Kennedy, Karen Hjortsvang, Deborah Goldfarb, and Sarah Tashjian 1. Introduction 2. Age-Related Improvements in Theory of Mind in Middle Childhood 3. Individual Differences in Theory of Mind in Middle Childhood 4. Conclusions References

7. Television and Children's Executive Function

186 187 200 206 208

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Angeline S. Lillard, Hui Li, and Katie Boguszewski 1. 2. 3. 4. 5. 6.

Introduction Executive Function Children and Television Media Long-Term Media Influences on Executive Function Short-Term Studies of Television and Executive Function Processing of Television

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7. Our Studies 8. Modeling How Fantastical Television Might Influence Executive Function 9. Conclusion Acknowledgments References

8. Moral Judgments and Emotions in Contexts of Peer Exclusion and Victimization

vii 228 237 242 243 243

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Melanie Killen and Tina Malti 1. Overview: The Centrality of Morality 2. Intergroup Exclusion and Interpersonal Victimization 3. Moral Judgments and Moral Emotions 4. Social Reasoning Developmental Model 5. Developmental Theories of Social and Group Identity 6. Moral Emotions Clinical-Developmental Theory 7. Interventions for Reducing Prejudice and Victimization 8. Integrating Group-Level and Individual-Level Models 9. Implications and Conclusions Acknowledgments References Author Index Subject Index Contents of Previous Volumes

250 251 253 254 256 260 264 266 270 272 272 277 297 303

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CONTRIBUTORS Daniel Ansari Numerical Cognition Laboratory, Department of Psychology & Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada Adriene M. Beltz Department of Psychology, and Department of Human Development and Family Studies, The Pennsylvania State University, University Park, Pennsylvania, USA Sheri A. Berenbaum Department of Psychology, and Department of Pediatrics, The Pennsylvania State University, University Park, Pennsylvania, USA Katie Boguszewski Department of Psychology, University of Virginia, Charlottesville, Virginia, USA Cara H. Cashon Department of Psychological and Brain Sciences, University of Louisville, Louisville, Kentucky, USA Robin Corley Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado, USA Deborah Goldfarb Department of Psychology and Center for Mind and Brain, University of California, Davis, California, USA Karen Hjortsvang Department of Psychology and Center for Mind and Brain, University of California, Davis, California, USA Nicholas A. Holt Department of Psychological and Brain Sciences, University of Louisville, Louisville, Kentucky, USA Katie Kennedy Department of Psychology and Center for Mind and Brain, University of California, Davis, California, USA Melanie Killen Department of Human Development and Quantitative Methodology, University of Maryland, College Park, Maryland, USA Melissa Koenig Institute of Child Development, University of Minnesota, 51 E. River Pkwy, Minneapolis, MN 55455 Hannah J. Kramer Department of Psychology and Center for Mind and Brain, University of California, Davis, California, USA

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Kristin Hansen Lagattuta Department of Psychology and Center for Mind and Brain, University of California, Davis, California, USA Hui Li Department of Psychology, University of Virginia, Charlottesville, Virginia, USA, and School of Psychology, Central China Normal University, Wuhan, Hubei, PR China Angeline S. Lillard Department of Psychology, University of Virginia, Charlottesville, Virginia, USA Ian M. Lyons Numerical Cognition Laboratory, Department of Psychology & Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada Tina Malti Department of Psychology, University of Toronto, Mississauga, Ontario, Canada John E. Richards Department of Psychology, Institute for Mind and Brain, University of South Carolina, Columbia, SC, USA Elizabeth Stephens Institute of Child Development, University of Minnesota, 51 E. River Pkwy, Minneapolis, MN 55455 Sarah Suarez Institute of Child Development, University of Minnesota, 51 E. River Pkwy, Minneapolis, MN 55455 Sarah Tashjian Department of Psychology and Center for Mind and Brain, University of California, Davis, California, USA Wanze Xie Department of Psychology, Institute for Mind and Brain, University of South Carolina, Columbia, SC, USA

PREFACE Since the founding of this series in 1963, each volume has comprised an interesting and wide-ranging assortment of theoretical and research perspectives that typify the important issues in developmental psychology at the time. I am honored to continue the tradition of the Advances in Child Development and Behavior series as founded by coeditors Lew Lipsett and Charles Spiker and then subsequently nurtured for over 30 years under the editorship of Hayne Reese. In the preface to volume 1 of this series (1963), Lipsett and Spiker wrote, The serial publication of Advances in Child Development and Behavior is intended to provide scholarly reference articles in the field and to serve two purposes. On the one hand, it is hoped that teachers, research workers, and students will find these critical syntheses useful in the endless task of keeping abreast of growing knowledge in areas peripheral to their primary focus of interest. There is currently an indisputable need for technical, documented reviews which would facilitate this task by reducing the frequency with which original papers must be consulted, particularly in such secondary areas. On the other hand, the editors are also convinced that research in child development has progressed to the point that such integrative and critical papers will be of considerable usefulness to researchers within problem areas of great concern to their own research programs.

This vision and the original intent of this series are as relevant today as it was 60 years ago. Volume 48 contains eight invited chapters that underwent a rigorous review and revision before final publication. These chapters illustrate the current range and diversity of theory and research in developmental psychology. They also represent some interesting trends. The first two chapters focus on the importance of structural neurodevelopmental and physiological foundations of behavior. Richards and Xie describe a truly amazing new resource that they have created and are making available to all developmental researchers—a life-span neurodevelopmental MRI database that provides descriptive structural brain characteristics that span 2 weeks to 89 years of age. The utility of this database is far reaching. As just one example, they illustrate how the database was used in a study of the MRIs of Chinese children. Berenbaum and colleagues revisit the importance of the role of puberty in adolescent development. They review important new advances in our knowledge of the physiological processes underlying puberty with a focus on the interplay between biological and social influences on xi

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psychological development during the period of adolescence. They outline an interesting number of research questions that remain unanswered, especially in light of measurement and methodological issues. Together, these first two chapters highlight the importance of advances in the physiological foundations of psychological development at different points in the life span. The next two chapters describe empirical advances in the areas of symbolic and nonsymbolic representations of numerical magnitude and the face inversion effect. Lyons and Ansari review recent literature on whether there exists an approximate nonsymbolic representational system, shared by human infants and other species, that provides a foundation for children’s and adult’s symbolic representation of numerical magnitude, an important precursor to mathematical skills. To do so, they examine what is known about specific predictions, such as the correlation between children’s ability to discriminate nonsymbolic representations of numeric magnitude and symbolic numerical and mathematical skills. While their review concludes that there is little evidence to support predictions based on the presence of an approximate nonsymbolic representational system, they also outline key research questions and findings from brain imaging studies that are important for expanding our understanding of the early foundations for later mathematical abilities. Cashon and Holt are also interested in examining the early foundations of later skills as they explore the developmental origins (in the first year of life) of the face inversion effect, or the phenomenon that adults are better at recognizing upright faces compared to those that are presented in an inverted orientation. They discuss the research findings based on infant scanning of faces, face preference, recognition, and processing, along with neural responses to face stimuli, and use those findings to make a strong case that the developmental origins of the face inversion effect begin in the first few months of life and grow to near adult-like competence by the end of the first year. The remaining four chapters provide a glimpse into the state of the art in specific areas of behavioral development by offering new insights, methods, and intriguing possibilities for future research. Stephens, Suarez, and Koenig offer an engaging analysis of how young children learn from those who speak to them, especially when two types of conflicts arise having to do with what children already know (coherence checking) and what children do not know (source monitoring). These authors conclude that children encounter and detect messages from speakers that first conflict with what they know before they encounter and detect speakers with messages that convey conflicts of interest. In the chapter written by Kristen Hansen Lagattuta and

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colleagues, a compelling case is made that draws attention to the continued development of Theory of Mind (ToM) into middle childhood, well beyond the typical research focus of its emergence in infancy and early childhood. These researchers specifically point to advances in TOM during middle childhood in what they refer to as an “interpretive understanding” of other people’s minds that leads to the recognition that several factors (i.e., past experiences, personal characteristics) can contribute to how different individuals experience the same event in different ways. They end their chapter with a provocative section on the need for research on individual differences in ToM ability throughout development. Another chapter that also identifies an area of investigation in great need of additional research is that written by Lillard, Li, and Bogusweski that raises questions about the negative effects of television on children’s executive function. They focus on how some children’s television programming appears to deplete executive functioning. To guide future research, these authors present a new model that draws from both information processing theory, the research literature on adult television viewing, and also considers the role of arousal. The final chapter in this volume, written by Killen and Malti, puts forth an interesting framework, rooted in a moral development perspective, that attempts to integrate what is known about peer exclusion and victimization to better understand social phenomena such as bullying. Their careful review of the research literature focuses on two types of social exclusion— “intergroup exclusion” and “interpersonal victimization” that are not typically considered together. The result is a forceful argument for why researchers need to consider both group-level and individual-level factors, and their integration, in research and in the development of intervention strategies for these types of social transgressions. This eclectic collection of chapters reflects the broad range of topics, their critical synthesis and integration, and the sophisticated approaches of today’s developmental scholars. In the vision articulated 60 years ago by founding editors Lew Lipsett and Charles Spiker, these chapters continue to honor the intent of the Advances in Child Development and Behavior series. JANETTE B. BENSON University of Denver, Colorado, USA October 31, 2014


CHAPTER ONE

Brains for All the Ages: Structural Neurodevelopment in Infants and Children from a Life-Span Perspective John E. Richards1, Wanze Xie Department of Psychology, Institute for Mind and Brain, University of South Carolina, Columbia, SC, USA 1 Corresponding author: e-mail address: [email protected]

Contents 1. Structural Neurodevelopment and Behavior 2. Neurodevelopmental MRI Database 2.1 The Need for Pediatric MRI Templates 2.2 Average MRI Templates from 2 Weeks to 89 Years 2.3 Priors for MRI Tissue Segmentation 2.4 A Common Neurodevelopmental Stereotaxic Atlas 2.5 Access to the Neurodevelopmental MRI Database 3. Applications to MRI 3.1 Volumetric Analysis of Brain Structural Development 3.2 “Study-Specific” MRI Templates and Neurostructural Development in Chinese Children 3.3 Nonmyelinated Axon Tissue Segmentation in Infants 3.4 Contribution to Methods for Studying Brain Activity 4. Relation to Brain–Behavior Development Acknowledgments References

2 5 5 9 15 18 21 22 22 28 33 37 42 43 44

Abstract Magnetic resonance imaging (MRI) is a noninvasive method to measure brain structure and function that may be applied to human participants of all ages. This chapter reviews our recent work creating a life-span Neurodevelopmental MRI Database. It provides agespecific reference data in fine-grained age intervals from 2 weeks through 89 years. The reference data include average MRI templates, segmented tissue priors, and a common stereotaxic atlas for pediatric and adult participants. The database will be useful for neuroimaging research over a wide range of ages and may be used to make life-span comparisons. The chapter reviews the application of this database to the study of neurostructural development, including a new volumetric study of segmented brain

Advances in Child Development and Behavior, Volume 48 ISSN 0065-2407 http://dx.doi.org/10.1016/bs.acdb.2014.11.001

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2015 Elsevier Inc. All rights reserved.

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John E. Richards and Wanze Xie

tissue over the life span. We also show how this database could be used to create “studyspecific” MRI templates for special groups and apply this to the MRIs of Chinese children. Finally, we review recent use of the database in the study of brain activity in pediatric populations.

1. STRUCTURAL NEURODEVELOPMENT AND BEHAVIOR Brain development occurs over the life span. We know a lot about the changes in the structure of the brain, including global structural changes (size and shape), neural changes (synaptogenesis, myelination, and tract development), and genetic and epigenetic influences on brain development. The last two decades have seen the emergence of research that shows parallels and causal relations between brain structural development and psychological– behavioral development in participants across the life span. Of special interest in this regard is the emergence of studies that have examined brain functional activity with magnetic resonance imaging (MRI) neuroimaging in its relation to cognitive or emotional development (e.g., Braver et al., 1997; Casey et al., 1995, 1998, 1997). In more recent years, direct evidence for the effects of brain structural development has been shown on both cognitive development (Rice, Viscomi, Riggins, & Redcay, 2014) and emotional self-regulation (Fjell et al., 2012). This is an exciting time for those interested in brain–behavior relations across development. The study of brain structural development has been hampered by the lack of tools to measure brain structure in typically developing humans. For many years, the study of brain development was limited to autopsy studies (e.g., Conel, 1939–1967; Huttenlocher, 1990, 1994; Kinney, Brody, Kloman, & Gilles, 1988; Kinney, Karthigasan, Borenshteyn, Flax, & Kirschner, 1994), studies of nonhuman primates (e.g., comparative neurodevelopment, Bourgeois, 1997), or clinical populations (see Richards, 2009, for discussion). A technique that may be used to examine the brain of individual participants at all ages is MRI. The MRI has been described in several places (e.g., Huettel, Song, & McCarthy, 2004). The head’s materials (skull, cerebrospinal fluid (CSF), brain, and muscles) have magnetic properties that differ based on their chemical composition. The MRI uses these differences to identify the type and location of materials inside the head. This allows for the identification, visualization, and quantification of skull, skin, CSF, white and gray matter (GM), myelination, vascularization, and other head properties. The study of brain development using MRIs has a fairly “recent”

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history (e.g., Giedd et al., 1999, 1996; Jernigan et al., 1991) but is increasingly becoming an important tool for studying brain development in pediatric populations. There have been two limitations to the use of MRI for studying neurostructural development; one problem has been resolved and the other solution is “in progress” (Section 2). One limitation has been the lower age limit for the use of MRI; this problem has been solved. The MRI environment is noisy, often is in a hospital or clinical setting, and requires almost no head movement during the scan. The latter is especially true in “3D sequences.” The MRI sequence can be “two-dimensional” (2D) or “three-dimensional” (3D). The MRI emits a radio frequency (RF) pulse, which is an electromagnetic wave that excites protons which are aligned in the magnetic field. In a 2D scan, the RF pulse excites a narrow slice of the imaging volume (2D), and the magnetic changes are measured in a single 2D plane. In a 3D scan, the RF pulse excites the entire imaging volume and MRI encoding is used to distinguish the spatial areas. The 3D sequence has far greater resolution since the final scan represents the average of the entire sampled volume over the course of the sequence (Brunner & Ernst, 1979). However, the 3D sequences take longer time to complete. Any movement of the head relative to the scanner in any part of the sequence will affect the entire scan (cf., 2D sequences are shorter, and movement only affects the slice during which movement occurs). Typically, clinical pediatric participants who cannot remain still, such as infants and children, are given a mild sedation to help reduce head movements. However, this is not permitted for ethical reasons for typically developing children due to the slight risks associated with sedation. Fortunately, this problem has been solved with the use of MRI for neurostructural development. Infants and children who cannot remain still are scanned while sleeping (see Section 2.1). Children older than about 4 years are either given brief training in a mock scanner, or respond to verbal instructions. We have previously reviewed how MRIs may be done with infant participants (Richards, 2009). The problem of movement in the MRI remains a problem for functional MRI (fMRI). Young children have great difficulty with the scanner when in an alert, behaving state (Byars et al., 2002). The second limitation to the use of MRI for studying neurostructural development has been the lack of methodological tools for conducting MRIs on “pediatric” (infants, children, and adolescents) populations. The solution to this problem is “in progress” (Section 2). Both structural and fMRI studies require standardized reference MRI volumes, including

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MRI head or brain templates, tissue segmentation priors, and stereotaxic atlases (Section 2.1). Initially, this was done in Talairach space (Talairach and Tournoux, 1988; also see Talairach Atlas Database Daemon, Fox & Uecker, 2005; Lancaster, Summerlin, Rainey, Freitas, & Fox, 1997; Lancaster et al., 2000), though specific limits in the Talairach atlas (Talairach and Tournoux, 1988) limited its usefulness (Mandal, Mahajan, & Dinov, 2012). The contemporary reference system for most MRI work is based on the reference space in the “Montreal Neurological Institute” (MNI) standard (Montreal Neurological Institute brain atlas; Evans et al., 1993; Evans, Collins, & Milner, 1992; Mazziotta, Toga, Evans, Fox, & Lancaster, 1995). This reference system is based on MRIs collected from young adult participants, and their relevance for pediatric and aging populations has been questioned. The solution to this problem has been the construction of MRI reference volumes for participants across a wide range of ages and with sufficient age resolution to capture the neurostructural changes that happen during development. This chapter reviews our work on a “Neurodevelopmental MRI Database.” The database consists of average MRI templates, tissue segmentation priors, stereotaxic atlases, and over 4000 MRI volumes of individual participants. We cover the age range from 2 weeks through 89 years of age. The database answers the problems listed above by providing MRI reference volumes for participants over a wide range of ages. Additionally, the existence of a large database of individual MRI volumes allows the investigation of brain structural development over the life span. The “Neurodevelopmental MRI Database” is a unique resource for the study of brain development. It will be useful for quantitative studies of brain development, measurement of brain activity with techniques such as fMRI and psychophysiology, and provide a standardized norm for brain development across the life span. This chapter will do two things. First, we will spend time describing a database of reference MRI templates and accompanying materials. This “Neurodevelopmental MRI Database” covers the life span with common average MRI templates, tissue segmentation priors, and stereotaxic atlases. In addition to the reference materials, there are over 4000 MRI volumes from typically developing participants ranging in age from 2 weeks through 89 years of age. Second, we will show how this database may be used in the study of neurostructural development. We also will suggest some ways in which this database might be useful in the study of brain–behavior relations during development, though we will not review this latter topic extensively.


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2. NEURODEVELOPMENTAL MRI DATABASE 2.1. The Need for Pediatric MRI Templates The study of brain development in infants, children, and adolescents often uses MRI techniques to assess brain structure. However, many of the procedures used to analyze pediatric MRIs are based on reference data derived from adults. For example, MRI procedures often require that participant MRIs be combined into a single reference frame. Aligning the MRIs with linear or nonlinear registration to a standard MRI does this. Typically, pediatric brains have been normalized based on adult brain templates based on a single adult subject (Talairach & Tournoux, 1988) or the average of young adult participants (Evans et al., 1993; Mazziotta et al., 2001; see Mandal et al., 2012, for overview). The “MNI” template, known as the MNI-305, is one such young adult template (Collins, Neelin, Peters, & Evans, 1994; Evans, Brown, Kelly, & Peters, 1994; Joshi, Davis, Jomier, & Gerig, 2004). It was constructed with an iterative linear averaging technique based on 305 adult participants and is the de facto standard for defining the spatial orientation of the brain in MRI volumes. A more recent template is the “International Consortium for Brain Mapping” (ICBM) “ICBM-152.” This average MRI template was derived from 152 high-resolution 3D MRIs that were registered to the MNI-305 template (Mazziotta et al., 2001). The ICBM-152 template is distributed as the MNI-152 T1W volume with neuroimaging processing programs (e.g., FSL, Smith et al., 2004; SPM, Penny, Friston, Ashburner, Keibel, & Nichols, 2007). Such reference data include average MRI templates used for combining MRIs across participants, average-segmented GM and white matter (WM) MRI volumes used for analyzing the brain tissues, and stereotaxic atlases used to identify anatomical features in the brain. The use of adult reference MRIs to analyze pediatric MRIs is problematic. Studies have shown problems with aligning child brains to adult brains due to more variable contours of the cortex (Hoeksma, Kenemans, Kemner, & van Engeland, 2005; Muzik, Chugani, Juhasz, Shen, & Chugani, 2000), misclassification of brain tissue (Wilke, Schmithorst, & Holland, 2002), and local and global neurostructural changes (Gogtay et al., 2004; Lenroot & Giedd, 2006; Sowell, Thompson, & Toga, 2004). In addition to differences between adult brains and pediatric brains at a given age, differential brain growth during specific developmental periods (e.g., infant infancy and child childhood) creates large variability across ages both within and between children for brain size and shape, and brain tissue

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classes ( Joshi et al., 2004; Muzik et al., 2000; Prastawa, Gilmore, Lin, & Gerig, 2005; Wilke et al., 2002). These and other issues have led several to conclude that the use of adult reference MRIs is inappropriate for application to pediatric MRIs and for studying brain growth and development. A solution to this problem has been the creation of reference MRI data based on pediatric populations. For example, Altaye, Holland, Wilke, and Gaser (2008) created an infant MRI template from MRI scans that were obtained from infants from birth through 12 months of age. They reported that the use of adult templates in normalization and segmentation of infant MRIs resulted in misclassifications of tissue types and that these misclassifications were reduced when using the infant template. Similarly, we have created MRI templates based on infant participants (Sanchez, Richards, & Almli, 2011) and a series of stereotaxic atlases in 1.5-month increments from 3 to 12 months of age (Fillmore, Richards, Phillips-Meek, Cryer, & Stevens, 2014; Phillips, Richards, Stevens, & Connington, 2013). We showed that the fit of an average template atlas to manually segmented regions was better when the age of the average template’s participants matched the age of the infant. The fit of the template atlas to the manually segmented regions grew increasingly worse as the difference between the infant’s and template’s participants’ age increased. The problem of using reference data from young adults is not limited to infants. Although some studies have reported that children as young as 7 years of age can be adequately normalized with adult templates (Burgund et al., 2002; Kang, Burgund, Lugar, Petersen, & Schlaggar, 2003) or that the MNI-305 reference was suitable for use with spatial normalization for children at least 6 years of age (Muzik et al., 2000), the use of templates based on young adults for use with children and adolescents has been questioned. Structural variation in the brain across ages could result in spurious age differences based on an increasing disparity between the age of the participants upon which the template was based and the age of the participants in the study. Yoon, Fonov, Perusse, and Evans (2009) analyzed the brain tissue (GM and WM) distribution of young children (8 years). They found that using an age-specific template based on 8-year-old children for normalization resulted in a considerably different tissue distribution than using a template based on adult participants. These issues may also apply to using young adult MRI templates with aging populations. There are significant changes in brain volume (Fotenos, Snyder, Girton, Morris, & Buckner, 2005), GM and WM (Ge et al., 2002; Good et al., 2001; Sato, Taki, Fukuda, & Kawashima, 2003; Sullivan, Rosenbloom, Serventi, & Pfefferbaum, 2004;


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Taki et al., 2004), and overall structure during adulthood. Lemaıˆtre et al. (2005) found that beginning at age 20 there was a constant linear decline in percent of GM through the life span to late adulthood. Similar to the studies with children as participants, studies report that using an MRI template based on older adults have better results for elderly participants than using an MRI template based on young adults. For example, Huang et al. (2010) compared a study-specific template based on the older adults in their study with a template developed from young adult images for spatial normalization within an fMRI data analysis. Huang et al. (2010) found that more voxels were identified as functionally significant in older adults when the studyspecific template was used. Studies comparing a study-specific template to the MNI template for volumetric brain analysis have demonstrated that using a study-specific template reduced anatomical biases in the analysis (Ashburner & Friston, 2000; Good et al., 2001; Thompson et al., 2001). The last 10 years has seen a proliferation of MRIs of typically developing infants and children that would be useful for the construction of ageappropriate pediatric reference MRIs. A notable contribution in this regard is the NIH MRI Study of normal brain development (NIHPD). This was a multicenter study that acquired MRIs from over 400 healthy, typically developing participants 4.3–18 years of age, and participants from 2 weeks to 4 years of age (Almli, Rivkin, & McKinstry, 2007; Brain Development Cooperative Group, 2006, 2012; Lange, Froimowitz, Bigler, Lainhart, & Brain Development Cooperative Group, 2010; Leppert et al., 2009; Waber et al., 2007). This resulted in a large database of pediatric brain images, which were made widely available by the NIHPD project. A second notable contribution to the study of structural neurodevelopment has been the development of procedures that allow the scanning of typically developing young infants. The standards and procedures for this were set by the NIHPD (Almli et al., 2007; Evans, 2005; NIH, 1998). Participants are scanned without sedation, with infants and very young children being scanned during sleep, and children older than about age 4 being scanned while awake. We use this approach in our work on the development of infant attention (Richards, 2009). Infant participants who take part in psychophysiological studies of infant attention (Richards, 2012, 2013a; Zieber & Richards, 2013) also have MRI scanning. The infant and parent come to the MRI center in the evening at the infant’s typical bedtime. When the infant is asleep, it is placed on the MRI table, earplugs and headphones are put on, and then the MRI recording is done. Figure 1 shows an infant lying on the MRI bed—the headphones and cloths surrounding the

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Figure 1 An infant lying on the MRI bed going into the MRI tube. The infant is covered with a sheet and has a restraining strap lightly placed across his/her body. The headphones and cloths surrounding the infant can be seen in this picture. A research assistant (left side of picture) and the parent (right side of picture) are close to the baby during the scan. The right figure shows a 5-year-old child in the same scanner. Left figure adapted from Richards (2009).

infant can be seen. We do this recording in awake children from 4 years of age through young adulthood (Figure 1). We often use a mock scanner room for the younger children to have the child practice good scanner behavior before the actual MRI scan. Several labs are doing routine MRI recording of individual participants and then testing the participants in behavioral/psychological experiments (Akiyama et al., 2013; Lloyd-Fox, Wu, Richards, Elwell, & Johnson, 2013). Thus, scans for infant participants from the NIHPD study and from other sources have led to a large number of MRI scans for infants (birth through 1 year), and scans for child participants from the NIHPD study and other sources have led to a large number of MRI scans for children and adolescents. The availability of MRIs from typically developing infants, children, and adolescents has led to a proliferation of MRI reference data based on pediatric populations. Average MRI reference data have been constructed for infants (Akiyama et al., 2013; Altayeet al., 2008; Sanchez et al., 2011; Shi et al., 2010, 2011), children and adolescents (Fonov et al., 2011; Sanchez, Richards, & Almli, 2012; Wilke et al., 2008, 2002), and adults (Fillmore, Richards, et al., 2014; Phillips et al., 2013). Some of these reference data were based on a specific age or limited ages (infants: Akiyama et al., 2013; Altaye et al., 2008; Shi et al., 2010, 2011; 8-year-olds: Yoon et al., 2009). Average MRI templates have been constructed with the wider age ranges of the NIHPD database. These include: (1) Wilke, Holland, Altaye, and Caser (2008) created a template-building platform (Template-O-Matic) through which researchers could specify the age range and sex of the resulting templates, which were based on linear registration techniques and (2) Fonov


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et al. (2011) constructed age-appropriate atlases that provided templates with significant anatomical detail for six age ranges with a width of 4–6 years each that were grouped according to estimated pubertal status: 4.5–8.5 years, prepuberty; 7.0–11.0 years, pre- to early puberty; 7.5–13.5 years, pre- to midpuberty; 10.0–14.0 years, early-to-advanced puberty; and 13.0–18.5 years, mid- to postpuberty. Section 2.2 describes a life-span Neurodevelopmental MRI Database that we have constructed.

2.2. Average MRI Templates from 2 Weeks to 89 Years Our contribution to this work has been the acquisition of MRI volumes from typically developing participant across the life span and construction of age-specific templates; we call this the “Neurodevelopmental MRI Database.” The MRI volumes consist of MRIs collected from over 4000 participants who ranged in age from 2 weeks through 89 years at the time of the scan. These data consist at least of whole-head T1-weighted MRI scans. Several participants also had other scans (e.g., T2-weighted MRI scans), diffusion tensor imaging (DTI, for axon pathways), and MRI spectroscopy scans (for relative concentrations of brain metabolites), though we have included only the T1W and T2W scans in the database. Each MRI scan is processed in a pipeline to do brain extraction (“skullstripping”; Smith et al., 2004; Woolrich et al., 2009), tissue segmentation (GM, WM, and other materials), skull and scalp identification, and linear and nonlinear registration to a number of MRI reference templates. The MRI volumes came from several sources: (1) locally collected data from the McCausland Center for Brain Imaging (MCBI; http://www. mccauslandcenter.sc.edu) with ages from 3 months through about 34 years; all are 3T strength, 3D scans, with both T1W and T2W sequences; (2) NIHPD Objective 2 data (Almli et al., 2007; http://www.bic.mni.mcgill. ca/nihpd/info/data_access.html), with ages from 2 weeks through 4.4 years; all are 1.5T strength, 2D scans, with both T1W and T2W sequences; (3) NIHPD Objective 1 data (Waber et al., 2007) with ages from 4.5 years through about 18 years; all are 1.5T strength, with both T1W and T2W scans, most of the scans from 4.5 through 6 years are 2D sequences, and the older scans are 3D sequences; (4) Autism Brain Imaging Data Exchange (ABIDE; Di Martino et al., 2013; http://fcon_1000.projects.nitrc.org/indi/ abide/); 3T strength, 3D scans, T1W sequences; (5) Information Extracted from Medical Images database (IXI; Ericsson, Alijabar, & Rueckert, 2008; Heckemann et al., 2003; http://biomedic.doc.ic.ac.uk/brain-development/

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index.php?n¼Main.Datasets); with scans from both 3T and 1.5T scanners, 3D sequences, T1W and T2W sequences; and (6) Open Access Series of Imaging Studies (OASIS; Marcus, Fotenos, Csernansky, Morris, & Buckner, 2010; Marcus et al., 2007; http://www.oasis-brains.org); with 1.5T strength scans, 3D sequences, T1W sequences. We also have obtained a number of scans from other sites, which are used in collaborative studies (e.g., Center for Brain and Cognitive Development, Lloyd-Fox et al., 2014). A subset of the MRI volumes has been used in studies to create reference MRI data for a wide range of ages. (1) Sanchez et al. (2011) used MCBI and NIHPD Objective 2 data to create infant and preschool average MRI templates. These were done in 1.5-month intervals in the first 9 month, 3-month intervals from 9 to 18 months, then at 2, 2.5, 3, and 4.0 years. (2) Sanchez et al. (2012) used MCBI and NIHPD Objective 1 data to create average MRI templates from 4.5 through young adulthood. These templates exist for age groups of 6-months (e.g., 4.5, 5.0, and 5.5 years) through 19.5 years of age, and a single “young adult” template generated from participants from 20 to 24 years of age. The 20- to 24-year-old average was constructed to create an adult comparison template similar to the ages of the MNI and ICBM templates (Collins et al., 1994; Evans et al., 1994, 1993; Joshi et al., 2004; Mazziotta et al., 2001. (3) Phillips et al. (2013; also see Fillmore, Richards, et al., 2014) created adult templates in 5-year increments from 20 years of age through 89 years of age (e.g., 20–24 years, 30–34 years, through 85–89 years). These templates were compiled from MCBI data (20–34 years), the NIHPD Objective 1 (about 25 in 20–24 year group), IXI data (20–89 years), and OASIS data (20–89 years). The details for the construction of the average MRI templates are found in the original articles (Fillmore, Richards, et al., 2014; Phillips et al., 2013; Sanchez et al., 2011, 2012). The procedure used a tentative average volume from the MRIs of the participants of a specific age range. The initial volumes were oriented to the ICBM-152 template. Due to the initial orientation, the templates are loosely oriented to the ICBM-152 volume. The individual volumes were registered to this tentative average with nonlinear registration (ANTS, “Advanced Normalization Tools”; Avants, Epstein, Grossman, & Gee, 2008; Avants et al., 2011), and then the average was reconstructed. Nonlinear registration preserves fine details in the average MRI template when compared with linear registration techniques (Ashburner & Friston, 2000). We used an iterative averaging procedure (see Fonov et al., 2011; Guimond, Meunier, & Thirion, 2000; Yoon et al., 2009, for examples of similar iterative routine; see Mandal et al., 2012, for a discussion of MRI


11

template construction methods). The iterative procedure avoids biasing the templates to adult reference data. Our database is unique in providing finegrained age intervals with sufficient numbers of participants in each age group to provide reliable averages. It provides consistent methods and format for a database of normative age-appropriate average MRI templates across the life span. The MRI volumes in the database differ in scanning sequence details that affected the quality of some of our average templates. The MCBI sequences are from a Siemens Tim Trio 3T scanner. All T1W sequences at this site are 3D scans. This is important because the 3D sequence has far greater resolution than 2D scans, which is critical for average MRI template construction. The T2W scans are either 2D (infants) or 3D (children through adults) sequences. The NIHPD Objective 2 study used 1.5T, 2D sequences. They did this due to predicted time constraints for the entire set of sequences and to insure that they had a short duration T1-weighted scan for the youngest participants. This provides scans of inferior resolution to 3T–3D scans. Thus, when creating the database, we have created separate 1.5T averages, combined 1.5T and 3.0T scans, and separate 3.0T scans. The ABIDE data are all 3.0T, 3D scans; the OASIS and IXI have 3D scans and a mix of 1.5T and 3.0T strengths. We selected age intervals for each average scan to provide fine-grained age-selective MRI templates while keeping sufficient numbers of participants for the average. Table 1 shows the age intervals, numbers of participants for each average, and the numbers of participants for 1.5T and 3.0T scans in the average. The “Combined” templates include both 1.5T and 3.0T averages and cover the entire age range. We prefer the averages made of 3.0T participant MRIs for our own work (Fillmore, Phillips-Meek, & Richards, 2014; Phillips et al., 2013; Richards, 2012, 2013b; Xie, Richards, Lei, Kang, & Gong, 2014a, 2014b; Zieber & Richards, 2013). For infants, the 1-5T scans come from the NIHPD open source database and are 2D scans. Our MCBI 3T–3D scans of infants have higher spatial resolution and better signal-tonoise ratio than the NIHPD scans. For children (e.g., Xie et al., 2014a, 2014b), the spatial resolution of the NIHPD 1-5T scans is adequate given the size of the children head, whereas the signal resolution (signal-to-noise ratio) is better in the 3T than in the 1.5T scans. For each age, we constructed a whole-head MRI average, separately for T1-weighted and T2-weighted scans. We used the extracted brain from the whole-head MRIs from the individual participants to create a separate average MRI template for the brain, separately for T1- and T2-weighted scans.

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Table 1 Age and Number of Participants for the 1.5T, 3.0T, and Combined Average MRI Templates Pediatric Populations Infants

1.5T

2–0 Weeks

23

3–0 Months

22

4–5 Months 6–0 Months

32

7–5 Months

3.0T

Combined

23 14

36

12

12

14

46

11

11

9–0 Months

29

10

39

12–0 Months

25

10

35

Preschool

1.5T

3.0T

Total

15–0 Months

32

32

18–0 Months

32

32

2–0 Years

27

27

2–5 Years

31

31

3–0 Years

22

22

4–0 Years

19

4

19

Children

1.5T

3.0T

Total

4–5 Years

9

9

5–0 Years

14

14

5–5 Years

17

17

6–0 Years

27

6–5 Years

36

36

7–0 Years

27

27

7–5 Years

44

44

8–0 Years

46

19

56

8–5 Years

40

12

40

9–0 Years

46

9–5 Years

41

10

41

10–0 Years

62

16

72

10–5 Years

52

10

37

46

52

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Table 1 Age and Number of Participants for the 1.5T, 3.0T, and Combined Average MRI Templates—cont'd Pediatric Populations Adolescents

1.5T

3.0T

Total

11–0 Years

31

31

11–5 Years

40

40

12–0 Years

37

12–5 Years

30

13–0 Years

34

11

34

13–5 Years

29

19

29

14–0 Years

32

30

42

14–5 Years

30

1

31

15–0 Years

32

32

15–5 Years

23

23

16–0 Years

34

13

44

16–5 Years

28

1

29

17–0 Years

25

25

17–5 Years

25

25

15

47 30

Adults Adults

1.5T

3.0T

Total

18–0 Years

18

20

28

18–5 Years

12

23

29

19–0 Years

10

17

23

19–5 Years

5

21

22

20–24 Years

157

117

244

25–29 Years

86

24

101

30–34 Years

63

34

79

35–39 Years

50

50

40–44 Years

61

61

45–49 Years

65

65

50–54 Years

57

57 Continued

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Table 1 Age and Number of Participants for the 1.5T, 3.0T, and Combined Average MRI Templates—cont'd Adults

55–59 Years

73

73

60–64 Years

83

83

65–69 Years

89

89

70–74 Years

101

101

75–79 Years

61

61

80–84 Years

62

62

85–89 Years

36

36

The “Combined” column represents the total number of participants in the combined (1.5T + 3.0T) atlas, which includes all 1.5T MRIs and part or all of the 3.0T MRIs, as in the original publications (Fillmore, Richards, et al., 2014; Phillips et al., 2013; Sanchez et al., 2011, 2012). The templates based on the 3T MRIs differ from those in the original publications because we continue to add 3T MRIs to our database and update the 3T templates.

The resulting MRI templates are illustrated in Figures 2 and 3. Figure 2 shows the mid-sagittal slice of the whole-head average MRI template for selected ages. This is shown preserving the relative size of the heads across the ages. The rapid head size increase may be seen from about birth through 18 months of age, which then continues at a slower pace through adolescence. Small detailed changes may be seen in this figure in the cortical and subcortical anatomy in the shape and topological arrangement of brain features across the age range. The full set of whole-head average templates is given in the original articles (Fillmore, Richards, et al., 2014; Phillips et al., 2013; Sanchez et al., 2011, 2012). Figure 3 shows an axial slice of the brain average MRI template for selected ages at the level of the anterior commissure. The averages are shown as the same size irrespective of actual template size. The average templates show regional patterns of myelination in the first 2 years. The posterior limb of the internal capsule is fully myelinated at 3 months; posterior regions of the hemispheres (e.g., occipital and temporal lobes) show myelination at 6 months, and seemingly full coverage of myelination by about 12 or 15 months of age. We know that myelination continues throughout the period of childhood and well into adolescence (Toga, Thompson, & Sowell, 2006). At the youngest ages, the WM tracts have lower MRI voxel values and thus appear darker than GM nuclei in a T1W scan. The lower voxel values reflect the fact that unmyelinated axons have a faster


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Figure 2 Whole-head average MRI templates for selected ages from the Neurodevelopmental MRI Database. This is a midsagittal slice, and heads are oriented approximately with the MNI template orientation. The size of the figures is proportional to the size of the average template for that age.

T1-relaxation time and thus have less magnetic energy than GM on the scan. Through childhood and into adulthood, the WM tracts become more intense (higher voxel values) than GM nuclei, since the T1-relaxation time of myelin is longer than that of GM. The relative thickness of the GM appears to decline from the youngest templates through about young adulthood (20–24-year-old template) with a corresponding increase in the amount of WM. Gradual brain atrophy becomes noticeable at the oldest ages. Despite the wide variety of ages, sources of the MRI data collection, and varieties in scanning sequence, the average MRI templates are consistent for level of detail and clarity. These developmental changes in the brain are consistent with previously established patterns of brain development and provide quantitative assessment tools for this analysis (see Section 3).

2.3. Priors for MRI Tissue Segmentation A common task in brain structural analysis is to determine the location and quantity of “GM” and “WM”. GM consists of neuronal cell bodies and nuclei (groups of neuronal cell bodies), and WM consists of myelinated

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Figure 3 Brain average MRI templates for selected ages from the Neurodevelopmental MRI Database. This is an axial slice at the level of the anterior commissure. The 2–0 years template is based entirely on 1.5T MRI volumes, the mid- to late-adults are based primarily on 1.5T MRI volumes, and the rest are based on 3T volumes. The brains from different ages are shown as the same size, though they differ in size for the templates (cf., Figure 2).

axons. The evaluation of the volume and location of the GM and WM is critical in neurostructural developmental research. Segmentation procedures use the MRI to classify brain tissue into GM, WM, and “other matter” (OM). This is often done in MRI analysis with the use of “segmented priors.” Segmented priors are patterns of GM and WM that are found in the participants who were used to construct reference MRI data. In segmenting analyses, the individual participant MRI is registered to the reference MRI template, the GM and WM segmenting priors are transformed into the participant space, and the priors are used as the first approximation of the GM and WM extent and distribution for that participant. Computer programs then use the priors and the participant MRI to segment the GM and WM tissue. Finally, tissue volume and location are then assessed for the individual participant. This analysis is done on a “T1-weighted” MRI sequence, which is designed to show maximum differentiation of GM and WM and distinguish GM/WM from other tissues in the brain (OM).


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Tissue segmentation has been a major issue for neurostructural research with pediatric populations. There are major changes in the overall pattern of myelination in the first 2 years (e.g., Figure 3). This is extremely obvious in the infancy period, particularly in the first 9 months when major lobes have no myelinated axons (e.g., Figure 3). From 2 years through adolescence, there is a continuing increase in amount of myelination and changes in the structure and location of myelinated axonal pathways. The rapid growth occurring during birth through preschool years creates large variability within and between children for brain size, shape, and tissue classes ( Joshi et al., 2004; Muzik et al., 2000; Prastawa et al., 2005; Wilke et al., 2002). Misclassification of brain tissue is a commonly cited problem when using adult reference data (average MRI templates and segmented priors) in pediatric populations (Altaye et al., 2008; Yoon et al., 2009; Wilke et al., 2002). For example, Yoon et al. (2009) found that the distribution of brain tissue was different in 8-year-olds using an age-specific or an adult template. A resolution to this issue is to construct segmented priors on pediatric data. Segmented priors were constructed for the Neurodevelopmental MRI Database. Individual participants had patterns of GM and WM identified with the “FSL FAST” computer program (FMRIB’s Automated Segmentation Tool; Zhang, Brady, & Smith, 2001). Then, the participant’s brain MRI volume was registered to its age-appropriate average MRI template, and the individuals’ GM and WM probability volumes were normalized into the reference space. Finally, average GM and WM probability volumes were then constructed. Note here that some computer programs actually create a two-class segmenting volume, and label the rest of the brain as “CSF.” However, the non-GM/non-WM tissue consists of glia, CSF, meninges, and other materials that are inside the part of the MRI extracted as the brain. We prefer to call this “OM” when creating our segmenting priors. We use the T2-weighted MRI to identify CSF in the head. T2 relaxation times in structural MRIs are substantially different in water (CSF) and matter (WM, GM, and other materials) so that voxels with large T2 values in the T2-weighted volumes come from CSF. The CSF is identified by using extracting voxels based on a threshold value for the T2-weighted volume. We calculate a separate probability volume for T2W-derived CSF. These constitute the segmented priors for our reference data. Figure 4 shows the GM- and WM-segmented priors for selected ages. The changes in the segmented priors follow the changes in myelination seen in the average MRI templates (cf. Figures 3 and 4). There are extremely vivid regional patterns of myelination in the first 12 months, substantial

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Figure 4 Tissue segmentation priors for selected ages for average MRI templates. The brightness of the colors represent the probability that a voxel belongs to the GM (blue (dark gray in the print version)), WM (yellow (white in the print version)), or “other mater” (green (gray in the print version)) category. Note the large changes in WM over the first year, due to myelination of axons over this age. The brains from the different ages are shown as the same size.

changes over the first 2 years, and gradual changes through adolescence. There are decreases in WM volume that occur in adulthood, particularly after the middle adult years (Section 3.1). These decreases are more gradual and of lesser volume than those occurring in childhood and so are not apparent in these figures.

2.4. A Common Neurodevelopmental Stereotaxic Atlas A common stereotaxic atlas for the MRI reference data will aid the study of structural neurodevelopment. The identification of anatomical regions in MRI analysis is typically done with stereotaxic atlas MRI volumes in the same spatial system as average MRI templates. Stereotaxic atlas MRI volumes classify each voxel in the volume according to its anatomical name (e.g., macroanatomical names, cerebral lobes, axonal tracts, Brodmann numbers). For example, the ICBM152 average MRI template is accompanied by the


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Harvard–Oxford Cortical atlas (Desikan et al., 2006) and the MNI structural atlas (Mazziotta et al., 2001). Other atlases are based on specific average MRI templates (e.g. LONI Probabilistic Brain Atlas, LPBA40, Shattuck et al., 2008; Hammers atlases: Hammers et al., 2003; Heckemann, Hajnal, Aljabar, Rueckert, & Hammers, 2006; Heckemann et al., 2003). It is not surprising that stereotaxic atlases based on adult MRI reference data are unsatisfactory for pediatric populations. Similar to segmented priors, the use of stereotaxic atlas is done by registering the individual participant MRI to the reference average MRI template and then transforming the stereotaxic atlas for the reference volume into participant space. The registration (or misregistration) of the child to the reference volume may lead to spatial errors for the transformed stereotaxic atlas. The topological arrangement of the brain for a young child, particularly in the infancy period, is substantially different than the arrangement of the adult brain. The topological arrangement differs because the newborns brain is substantially smaller in volume and the skull bones are unsutured, likely so the head will be malleable when passing through the birth canal. As the brain grows and the skull bones merge, the brain expands against the skull and the brains topological arrangement relative to the skull changes. Also, there may be specific brain regions existing in adults that are may not even exist in infants! For example, axonal tracts are undefined in infants due to large areas of unmyelinated axons. Similarly, synaptogenesis, which is the primary cause of the rapid increases in GM in infancy, results in cytoarchitectural differentiation of brain regions that will be defined in adults. The lack of mylelination and synaptic configuration in infants results in some brain areas existing in adults being undefined or indisciminable in infants. This problem has been addressed by creating stereotaxic atlases based on pediatric reference data. For example, Shi et al. (2011) combined simple automatic registration with the majority vote method. They registered the brains of ninety-five 2-year-olds to the AAL atlas (automatic anatomical labeling atlas; Tzourio-Mazoyer et al., 2002) to create a representative 2-year-old atlas. They then propagated the 2-year-old atlases down to the brains of those same participants as 1-year-olds and as newborns. Akiyama et al. (2013) created an adaption of the AAL atlas for an average MRI template based on 6-month-old infants. Gousias et al. (2008) use a method to create a stereotaxic atlas for individual 2 year-old participants based on the Hammers atlas (Hammers et al., 2003). We have begun to address this issue by creating methods for constructing a lobar atlas, the LPBA40 atlas, and the Hammers atlas for pediatric

20


participants (Fillmore, Phillips-Meek, et al., 2014; Phillips et al., 2013). First, we have created a lobar atlas identifying the major cortical lobes, brainstem, cerebellum, and some subcortical and sublobar areas. This has been done on all infant reference data ages (3T average MRI volumes for 3, 4.5, 6, 7.5, 9, and 12 months), selected child/adolescent ages (8, 12, and 18 years), and for the young adults (20–24 years). Second, we have adapted the methods of Gousias et al. (2008) that take manually segmented brains from individual participants (adults) and create an atlas for an individual participant (infants, children, adolescents, and adults) based on the adult manual segmentations. We can generate a stereotaxic atlas for each participant MRI in the Neurodevelopmental MRI Database based on the LONI Probabilistic Brain Atlas project (56 manually delineated areas; LPBA40; Shattuck et al., 2008) or the Hammers adult brain atlas (83 manually delineated areas; Hammers atlases: Hammers et al., 2003; Heckemann et al., 2006, 2003). The automatic labeling procedure compares very well to manually segmented volumes for our infant participants and infant MRI reference data (Fillmore, Phillips-Meek, et al., 2014; Phillips et al., 2013). So in addition to the participant T1W, T2W, brain, and segmented tissue volumes, we also have two segmented stereotaxic atlases for each participant in the database. The two atlases for each individual were used to create stereotaxic atlas MRI volumes for selected average MRI volumes. Similar to the segmented prior reference volumes, the individual participants were registered to the relevant age-appropriate average MRI reference volume, and the individual participant atlas volumes were transformed to the reference volume. A procedure was then used to get the information from all individual participants to construct an MRI stereotaxic reference volume that has the probability of each voxel belonging to one of the atlas segments, i.e., tissue types, and a “majority vote” to classify each voxel into a corresponding segment We have stereotaxic atlas MRI volumes for all the infant ages (3T average MRI volumes, for 3, 4.5, 6, 7.5, 9, and 12 months), selected child/ adolescent ages (2, 3, 4, 8, 12, and 18 years), and for the young adults (20–24 years). We are in the process of applying this procedure to child and adolescent data (e.g., 4 years to 18 years in 2-year increments). We may pursue this procedure with our adult data. Figure 5 shows some examples of the stereotaxic atlas for infants, children, and adults. The top rows show the lobar and Hammers segmenting atlases for all infant ages on the axial brain slice at the level of the anterior commissure (i.e., corresponding to Figure 3). The third row shows the


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Figure 5 Stereotaxic atlas label set for the infant manual lobar atlas (first row), infant Hammers atlas (second row), and selected ages Hammers atlas (third row). A complete infant atlas set exists (lobar, Hammers, LPBA40), atlas sets exist for selected ages, and atlases for other age groups are being generated (e.g., child to adolescence from 4 to 18 years). The colors (different shades of gray in the print version) represent the label from the atlas in which a voxel is classified. Note the comparability of the segmented regions across the ages.

Hammers segmenting atlas for selected adult ages. The resulting atlases represent a common stereotaxic atlas from birth to young adulthood and should prove beneficial for neurostructural developmental work.

2.5. Access to the Neurodevelopmental MRI Database We are very interested in encouraging the developmental neuroscience community to use the average MRI templates, segmenting priors, and atlases. These three types of data and individual participant MRI volumes represent our “Neurodevelopmental MRI Database.” The age-specific neurodevelopmental reference MRI data are available on line (http://jerlab. psych.sc.edu/NeurodevelopmentalMRIDatabase/). We include the average MRI templates (e.g., Figures 2 and 3), the segmenting priors for each MRI template (Figure 4), and stereotaxic atlases for selected ages (Figure 5; e.g., infants: Fillmore, Phillips-Meek, et al., 2014; Phillips et al., 2013; 20- to 24-year-old age range). These are publicly available to

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researchers for clinical and experimental studies of normal and pathological brain development. Data access is limited to scientific professionals for research purposes. These data are available for instructional purposes to faculty or laboratory supervisors. We are considering putting these data on “Databrary” (databrary.org; Adolph, Gilmore, Freeman, Sanderson, & Millman, 2012), which is an open data library for development science whose goal is to provide a platform for a repository for data management, collaboration, and open sharing. We are in the process of evaluating the “Databrary” site to house these data. The individual MRI volumes are not available on the site. This is due to privacy concerns, treatment of human subjects issues, and database restrictions from our publically acquired volumes (ABIDE, IXI, NIHPD, and OASIS). We also receive MRIs from sites for collaborative work (e.g., Lloyd-Fox et al., 2013; Lloyd-Fox et al., 2014; Xie et al., 2014a, 2014b) that cannot be shared. Figure 6 shows a screen shot of the home screen for the reference data. Interested users should contact John E. Richards ([email protected]) for access (see Request tab), and instructions for access are included on the site (see Access tab). The template volumes are in compressed NIFTI format (http://nifti.nimh.nih.gov/). The data are on a file server that may be accessed with the Secure Shell (SSH) file transfer protocols (SCP or SFTP). The original, individual MR brain scans and behavioral data from the NIHPD can be obtained from their Web site (https://nihpd.crbs.ucsd.edu/nihpd/info/index. html). The original individual MR brain scans for the ABIDE (http://fcon_ 1000.projects.nitrc.org/indi/abide/), IXI (database http://biomedic.doc.ic.ac. uk/brain-development/index.php?n¼Main.Datasets), and OASIS (http:// www.oasis-brains.org) are available at those Web sites for public access.

3. APPLICATIONS TO MRI 3.1. Volumetric Analysis of Brain Structural Development The MRI technique provides a noninvasive method to study human brain development. Over the past two decades, a few studies using this technique have been conducted to study neurostructural development during infancy (Fan et al., 2011; Knickmeyer et al., 2008), childhood and adolescence (e.g., Giedd et al., 1999; Lenroot et al., 2007; see review, for Sowell et al., 2004), and adulthood (e.g., Fotenos et al., 2005; Lemaıˆtre et al., 2005; Taki et al., 2004). The analysis of brain development in these studies across the life span has been from different investigations that may have used different methods, limited participants age ranges, and MRI types. An investigation showing


23

Figure 6 Screen shot of the Neurodevelopmental MRI Database Web site (http://jerlab. psych.sc.edu/NeurodevelopmentalMRIDatabase/).

brain development across the entire life span would add to this body of knowledge. The first 2 years of life are the most dynamic period of human postnatal brain development. Knowledge regarding brain development in this period was limited until recent work applied MRI technique to investigate brain development in infancy. For instance, Knickmeyer et al. (2008) studied brain development in typically developing infants from birth to 2 years: 84 infants at 2–4 weeks, 35 at 1 year, and 26 at 2 years. They found that infant total brain volume increased by100% during the first year and by 15% in the second year. Brain GM changed dramatically by 149% in the first year, whereas WM development was slower (11% increase). These results implied that human brain volume developed substantially in the first year of life,

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driven primarily by the development of GM and cerebellum. In parallel with these brain volumetric developments, infant brain anatomical networks from different regions also developed rapidly during the first 2 years (Fan et al., 2011; Gao et al., 2009). Researchers have used structural MRI to study brain development in childhood and adolescence since the 1990s (e.g., Giedd et al., 1999, 1996; Jernigan et al., 1991; Lenroot et al., 2007; Sowell et al., 2004). A common finding across studies has been that total brain volume increased from early childhood to adolescence, peaking at about age 10.5 years for females and 14.5 years for males. Global GM development followed an inverted U-shape peaking at about 8- (females) to 9- (males) years-old; however, different cerebral lobes showed different developmental patterns. For example, GM in frontal and parietal lobes increased to a maximum amount at roughly 10–12 years, whereas GM in temporal lobes increased through childhood and adolescence with evidence of significant decline during late adolescence (Giedd et al., 1999). In contrast, WM development followed a linear pattern from early childhood to young adults. One reason for GM decrease is the synaptic elimination or pruning. The increase of WM may indicate the development of brain networks and communication between brain structures. These changes in brain structures have been linked to behavioral changes (e.g., the development of frontal lobe is related to behavioral inhibition in adolescence; see Section 4). Volumetric brain changes continue to occur during adulthood. Fotenos et al. (2005) measured whole-brain volume changes in participants from 18 to 97 years. They reported that whole-brain volume decline was detected by age 30, and the mean decline in total volume was constant after age 65. A common strategy is to distinguish the changes in partial volume estimates of GM, WM, and less often, CSF. The most consistent partial volume change is a reduction in GM volume. Several studies have reported GM decline in subjects beginning at age 20 with a constant linear reduction across the span of early-to-late adulthood (Ge et al., 2002; Lemaıˆtre et al., 2005; Sullivan et al., 2004; Taki et al., 2004). Changes in WM volume have been found but are not as consistent in the literature. Ge et al. (2002) reported WM changes in a quadratic pattern, with slight increases until age 40, and decreases thereafter. Salat et al. (2009) reported a quadratic relationship between WM volume and age, with relative preservation or rise in volume until the late 1950s, followed by a steep decline. An associated linear increase in CSF volume has also been reported. This finding is consistent across volumetric studies that report CSF results (Good et al., 2001; Lemaıˆtre et al., 2005; Smith, Chebrolu, Wekstein, Schmitt, & Markesbery, 2007).

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We used the individual volumes from the Neurodevelopmental MRI Database to analyze volumetric changes across the life span. We first analyzed global brain and head volume development across life-span. Figure 7 (left columns) shows the changes in brain, inner skull, outer skull, and head volume as a function of age from 2 weeks through 89 years. Brain and skull volume development both showed an inverted U-shape pattern peaking during adolescence with a gradual decline thereafter through adulthood. Total head volume, however, showed increasing levels through age 30 with little decline in volume during adulthood. The logarithmic scale (Figure 7, lower left panel) shows a gradual increase in all four volume measures as a function of the log2 (age) scale. This type of growth is typical of growth scales for nearly all human physiological systems. The GM and WM development of the participant MRIs was also measured. For this analysis, we used the segmented priors from the age-specific reference volume for each individual. We have found that using these priors resulted in the most accurate measure of partial volumes (Fillmore, Richards, et al., 2014; Sanchez et al., 2012). Our results are presented in Figure 7 (right columns). The changes in GM showed increases at a very rapid rate from Brain and head development

GM, WM, and OM development GM, WM, and CSF development across life span

Brain and head development across life span Brain Outer skull

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Figure 7 Neurostructural development across the life span. Changes in overall volume (head, skull, and brain) are shown on the left panels, and changes in segmented tissue volume (GM, WM, and “Other Matter”) are shown on the right panels. The top panels show volumes as a function of age, whereas the bottom panels show changes in volume as function of log(age). The error bars represent the standard error of the mean (SE).

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infancy through about 10–12 years, followed by a linear decline through late adulthood. Conversely, WM changes had a more gradual increase through childhood and adolescence, a plateau through most of the adult years, with declining volumes happening only after about 50 years of age. The “OM” category, which includes several types of tissue, showed a gradual increase over the entire life span. An interesting finding is that the decline in GM and WM volume during adulthood were offset by increases in OM (Figure 7, left figures). This resulted in less overall brain volume decline than would be expected from GM and WM alone (Figure 7, right figures). The increases in “OM” may be seen in our average MRI templates as well (Figure 4). Some of these changes in GM and WM can be specifically linked to cognitive development (see Section 4). The dramatic changes in infancy in GM volume are primarily due to synaptogenesis and have concomitant changes in brain plasticity reflected in cognitive processes, memory, and language development. Similarly, rapidly myelinated regions are believed to correspond to rapid changes in inter-area communication critical for integrated neurological or behavioral functioning (Casey, Giedd, & Thomas, 2000; Deoni et al., 2011). The more gradual changes in the childhood years are very closely related to developing cognitive processes (e.g., see Casey et al., 2000). We do not know if the changes in GM and WM have specific parallels in behavior in adult development. The changes in adulthood are more gradual and thus would likely be linked more loosely to changes in cognitive processes. The gradual changes in WM/GM volume in adults likely do not correspond to the more dramatic brain–behavior relations found in adult pathologies. Brain changes in later adult development often include pathological brain development, which are closely related to declines in several cognitive areas (e.g., memory loss, senile dementia, Alzheimer’s Disease, Parkinson’s). We focused in further on changes occurring during infancy. There are extremely rapid changes in WM volume in infancy due to rapid axonal myelination during this time. Changes in myelination in infants have been documented in several places (Conel, 1939–1967; Kinney et al., 1988, 1994; Yakovlev & Lecours, 1967). We have reviewed this topic previously (Richards, 2009). Myelin is a fatty substance that in adult brains covers the axons of many neurons. It appears “white” in autopsy slices; fatty tissue reflects light. The T1 relaxation time of the cells making up the myelinated sheath have a long T1 relaxation time compared to GM, CSF, bone, and other head tissue. Therefore, it appears as “bright voxels” in a

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T1-weighted MRI volume (T1-weighted volumes are designed to maximally discriminate WM, GM, and OM). Any MRI volume of infant brains shows obvious lack of myelination in the first few months (see examples in Richards, 2009; Sanchez et al., 2011). This is obvious in our average MRI templates (Figure 3) and in our segmented priors (Figure 4). The individual MRI volumes for infant participants were used to further examine changes in GM and WM. Recall that we have segmented MRI volumes with partial volume estimates for GM, WM, and OM for all of our infant participants, and lobar and stereotaxic atlases for infant participants and 2-, 3-, and 4-year-olds. We used the lobar segments to mask the GM and WM volumes for frontal, occipital, parietal, and temporal lobes. Figure 8 shows the segmented GM (top left panel) and WM (top right panel) separately for the four cortical lobes. The GM volume increased steadily in the frontal and temporal lobes across the entire age; the parietal lobe showed gradual increases in size through 4 years. The occipital lobe GM did not change appreciably during this age period. Frontal lobe WM increased across ages in this analysis (Figure 10, top left panel). All four lobes showed increases in WM volume from birth through 4.5 months of age. However, WM volume for the occipital, parietal, and temporal lobes showed no reliable change from 4.5 through 12 months, whereas frontal lobe WM volume continued to increase during this time. The WM volume increased from 12 months through 4 years in frontal, parietal, and temporal lobes but appeared to have little change in the occipital lobe after 4 months. It should be noted that GM and WM volume changed significantly in the frontal lobes for all the reported age comparisons. These findings advance our knowledge of human brain and head development across the life span. To our knowledge, this is the first analysis of Regional GM development Frontal

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Figure 8 Gray matter- and white matter-segmented tissue volume in infants and preschool children as a function of scan age for the frontal, occipital, parietal, and temporal lobes. The error bars represent the standard error of the mean (SE).

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MRI data from participants from 2 weeks to 89 years. We are missing only newborn participants! Our findings were consistent with previous reports that used participants from limited age ranges. The rapid development of total brain, GM, and WM volume during infancy found in our study was consistent with Knickmeyer et al. (2008) and other studies with infant participants. The inverted U-shape of GM and the linear increase pattern of WM throughout childhood and adolescence (Figure 9) agreed with previous reports with children and adolescents (e.g., Giedd et al., 1999; Lenroot et al., 2007). However, the development of GM shown in our study was inconsistent with some reports from the adult portion of the life span. Several reports have stated that the decline in GM volume begins about age 20 (e.g., Lemaıˆtre et al., 2005; Sullivan et al., 2004; Taki et al., 2004). Our findings suggested the peak in GM for the entire life span occurred in the late childhood or early adolescence, with decreases in GM volume across the rest of the life span. The OM measured in our study may represent the amount of CSF from infancy to childhood, but it likely contains increasing amounts of tissue other than CSF as adulthood progresses. The linear increase pattern of OM was consistent with previous findings for CSF development in adulthood (Good et al., 2001; Lemaıˆtre et al., 2005; Smith et al., 2007), which indicated that the OM development was highly associated with CSF change.

3.2. “Study-Specific” MRI Templates and Neurostructural Development in Chinese Children It is clear from our work that the age-specific reference data are important due to differences among pediatric, young adult, and aging adult brains. There may be other factors that influence brain anatomical features. These could include gender, racial or ethnic status, developmental status, or children with atypical development (e.g., neurodevelopmental disorders). One example of this is with participants from different racial/ethnic backgrounds. Studies with MRI scans from adult participants have revealed morphological and structural differences between Asian and North American brains (Lee et al., 2005; Tang et al., 2010). For instance, Tang et al. (2010) compared brain morphological features (length, width, height, and AC–PC distance) between Chinese and U.S. adults. Chinese adult brains were found to be shorter, wider, and larger height than U.S. adult brains. Lee et al. (2005) provided similar differences between Korean and North American adult brains. Anatomical differences between Asian and North American brains are not limited to morphological features. Tang et al. (2010) conducted a

Figure 9 Head and brain development in Chinese and North American children. Panels (A–F) show changes in head, brain, GM, and WM volume from ages 8–16 for a group of Chinese children and individual participants selected from the Neurodevelopmental MRI Database. Panel (G) shows GM age patterns in 50 cortical regions for Chinese children. Each cell shows the ratio of GM volume in that regions for that age group to the average for all groups, with significant differences occurring primarily at age 14. Column 1 has asterisks representing a nationality main effect across the five age groups between Chinese and North American children. Column 2 shows parallel results for Chinese and North American adults found in Tang et al. (2010).

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comparison of brain regional volume for 56 brain structures between the two populations. There were several differences in volume in a number of these brain structures (e.g., the left middle orbitofrontal gyrus, left gyrus rectus, and right insular cortex). The differences between brain morphological features in Asian and North American adults suggest that nationality-appropriate reference data would be useful. This could reduce potential inaccurate deformation of MR images during image registration, and potential misclassification of brain tissue (with segmented priors) or brain regions (with stereotaxic atlases) caused by nationality-inappropriate reference volumes. To this end, Lee et al. (2005) and Tang et al. (2010) created the average MRI templates for Korean and Chinese adults, respectively. Measurements of these Asian templates indicated morphological differences compared to the ICBM-152 template. These Asian adult templates were found to be shorter but wider, and their heights were notably smaller than the ICBM-152 template. Validity tests in the Tang et al. (2010) study confirmed the hypothesis that using nationality inappropriate template (ICBM-152) would lead to significantly more deformations of MR images coming from Chinese adult participants. Given the differences in brain morphology and regional distribution between Asian and North American adult participants, it could be expected that such differences were found in children. The patterns of brain development shown in the previous Section 3.1 may be dissimilar in children from Asian backgrounds. Very few studies have examined possible differences between neurostructural developmental trajectories of Asian and North American populations. We know of only two studies that examined Chinese children and adolescent brain development (Guo et al., 2007; Guo, Jin, Chen, Peng, & Yao, 2008). No study has directly compared neurostructural development between Chinese and North American pediatric participants. We conducted a study to fill this gap by directly comparing brain development between Chinese and North American children and adolescents from 8 to 16 years (Xie et al., 2014a, 2014b). The MRI scans were collected from 133 (82 M, 51 F) Chinese children and adolescents from Sichuan province, China. These were 3T, 3D MRI scans with resolution similar to that done at the MCBI. The MRI data preparation and preliminary processing were performed using similar procedures to the participants in the Neurodevelopmental MRI Database (e.g., Sanchez et al., 2011, 2012). Age- and gender-matched children were selected from the database. We compared brain and head morphological changes, brain and head volume


31

development, GM and WM development, and volumetric changes in 50 cortical regions between Chinese and their North American counterparts. There were several intriguing findings in this analysis. First, Chinese children and adolescents’ brain and head were shorter, wider, and taller than their North American cohorts. Both groups showed a linear increase in brain/head length over these ages, with the North American children’s head being about 5 mm longer than that of the Chinese children at the same age. Conversely, a similar pattern was found for changes in head width, but in this case, the average Chinese children head was about 5–7 mm wider than the North American children’s head. Second, brain and head volumes showed different developmental patterns for Chinese and North American children across these ages. Figure 9 shows the results of the volumetric analysis of head and brain changes over age for these two groups. Overall, there were increases in head volume for both groups (Figure 9A), but the rate of change and eventual volume was larger in the North American children. For brain volume, the pattern was different. Chinese and North American children showed invert U-shape patterns in brain volume, with an earlier peak for Chinese children (Figure 9B). In addition, Chinese children were found to have a larger brain volume than North American children from about 9 to 15 years. Third, we analyzed changes in GM and WM as a function of age. The pattern of change in GM and WM volume in both groups was similar to those found in our previous analysis. Overall, there was a gradual decline in total GM volume similar to those found in our prior analysis (cf. Figure 9C and D with Figure 9A). These patterns held when comparing cortical GM or WM over these ages, with some differences in overall volume between the two groups (Figure 9E and F). Finally, we used the LPBA40 (Shattuck et al., 2008) to mask regional volumes in the cortex separately for 50 brain regions (cf., Tang et al., 2010) and analyzed the volumes of those 50 regions. Figure 9G shows the volumetric changes in these regions for the Chinese children. Most of the brain regions showed increases in volume through 14 years and then decreases thereafter. An interesting comparison may be made between our results with children and those of Tang et al. (2010) with adults. The asterisks in Figure 9 show differences between Chinese and North American children in our analysis (Column 1) and Chinese and North American adults in Tang et al.’s analysis (Column 2). We found a very similar pattern of results for the children in which regions showed differences as Tang et al. reported for adults.

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There are several implications from these results. The regional volume differences in the 50 cortical regions found in adults were already dissociated in young children. This implies that these differences must exist before this period of childhood. Future work that examines the nationality effect on brain development in other ages will advance our understanding of these differences. These findings also suggest that morphological differences between these two groups could affect the use of standard age-appropriate reference data based on the templates we have created. This suggests that age-specific brain/head templates for Chinese children may reduce the deformations and misclassifications that would result from using templates constructed from populations that have these anatomical differences relative to Chinese children. Given the differences between the Chinese and North American children found in this study, we felt it would be useful to create “study-specific” reference data for Chinese children. The data from the Chinese children were used to create age- and nationality-specific reference data (Xie et al., 2014a, 2014b). The data were grouped in five age groups in two-year increments (7–8, 9–10, 11–12, 13–14, and 15–16 years) to approximately match the age groups found in our 3T averages (Table 1). Average MRI templates and segmented priors (GM and WM) were constructed separately for head and brain for these ages. Figure 10 shows the average MRI templates from this work. Similar to our other templates (Figures 2 and 3), these average MRI templates have excellent resolution and show fine detail for both brain and head. These templates and segmenting priors are publicly available at our database (http://jerlab.psych.sc.edu/neurodevelopmentalmridatabase/ chinesechildren). We tested whether these Chinese age-appropriate templates fit Chinese children MR images better than age-inappropriate (Chinese adult), nationality-inappropriate (North American children), and nationality- and age-inappropriate (North American young adult) templates. Both internal and external validity tests confirmed the fitness of our templates to Chinese children brain MR images. Using the Chinese age-appropriate templates as reference data for registration resulted in significantly less deformation of Chinese children MR images than using the templates from Chinese adults (Chinese 56, Tang et al., 2010), the U.S. age-specific children (Sanchez et al., 2012), or the North American adults (20–24 template from Sanchez et al., 2012). We suggest that these Chinese children brain and head MRI templates should be used in MRI research involving Chinese children and adolescents. These should reduce the potential misclassification of tissue


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Figure 10 Brain and whole-head average MRI templates for Chinese children. The top two rows contain a midsagittal slice for brain and whole-head averages, and the bottom two rows show an axial slice at the level of the anterior commissure (AC). The figures from the different ages are shown as the same size, though they differ in size for the templates. The average MRI templates are oriented approximately with the MNI template.

types and deformations of MR images resulting from using age or nationality inappropriate references.

3.3. Nonmyelinated Axon Tissue Segmentation in Infants Tissue segmentation of brain images from infants poses special challenges. The GM and WM contrast-to-noise ratio (CNR) for infant MRI is significantly lower than the CNR for adult brain MRI (Mewes et al., 2006). This results in poor resolution across the spatial aspects of the MRI volume and consequent difficulty in segmenting partial volume regions. During the first 2 years of life, the WM/GM contrast is reversed (as compared to adult contrast) on T1- and T2-weighted images and gradually changes toward the MRI contrast of adult brains (Leppert et al., 2009; Paus et al., 1999; Xue et al., 2007). At around 9 months of age, GM and WM demonstrate roughly the same intensities and cannot be segmented by the sole use of intensity

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differentiation (Barkovich, 2005; Paus et al., 1999). Additionally, the brain in infants consists of a large amount of nonmyelinated axons (NMA). The T1 relaxation times for NMA and GM are approximately equivalent, so that “neuronal cell bodies” and “nonmyelinated axons” appear the same on T1W scans (e.g., Figures 2 and 3, youngest ages). Through the identification of myelinated and NMA, regional changes of WM and important maturational processes can be distinguished and quantified (Aubert-Broche, Fonov, Leppert, Pike, & Collins, 2008; Barkovich, 2005; Weisenfeld & Warfield, 2009). By about 2 years of age, the contrast found in the developing brain more closely resembles that of an adult brain due to the progression of increasing myelination and decreasing water content (Leppert et al., 2009; Rutherford, 2002). Nonmyelinated and myelinated axons and cortical and subcortical GM have been analyzed separately in the neonatal brain (Anbeek et al., 2008; H€ uppi et al., 1998; Prastawa et al., 2005; Weisenfeld & Warfield, 2009). The different tissue types in the infant brain exhibit significant levels of intensity inhomogeneity and variability, in addition to overlapping intensity distributions (Prastawa et al., 2005; Shi et al., 2010). Some researchers have developed methods to distinguish myelinated and NMA in MRIs. Prastawa et al. (2005) treated myelination as a fractional property, such that the MRI intensities reflected the degree of myelination in partial volume estimates. This procedure was somewhat successful in differentiating myelinated and NMA in the newborn brain. However, the dividing boundaries between the two tissue types were generally ambiguous (Prastawa et al., 2005; Rutherford, 2002), and the results showed mislabeled partial volume voxels (Xue et al., 2007). Others have expanded on the segmentation methodology of Prastawa et al. (2005) through the use of priors or iterative algorithms (Gilmore et al., 2007; Weisenfeld & Warfield, 2009). H€ uppi et al. (1998) differentiated between myelinated and NMA in newborn brains and found a fivefold increase in the myelinated WM volume between 35 and 41 weeks postconception. Studies have demonstrated significant reductions (35%) of myelination in preterm infants when compared to term infants (Inder, Warfield, Wang, H€ uppi, & Volpe, 2005; Mewes et al., 2006). Neonatal studies showing early rapid developmental changes highlight the importance of delineating the complete progression of the myelination process. We are working on procedures to create segmented priors for the reference data with GM, WM, CSF, OM, and NMA in the MRI volumes across infant age groups. Our segmentation technique uses both the T1W and T2W classification (Shi et al., 2010) to aid in tissue discrimination.


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Myelinated axons appear as “white matter” in the T1W volumes and dark matter in T2W scans (adults, older children). NMA appear in the T2W volumes as slightly brighter intensity voxels than GM (young infants). Figure 11 shows our procedure applied to two infants and the average MRI template for infants. The top row shows the identification of WM from the two-class model for a MRI from a 2-week-old participant. The two-class model categorizes WM successfully but classifies NMA with GM (top row, second column, blue (black in the print version) color). We then use the T2-weighted scan (third column) to identify the NMA (fourth column, green (gray in the print version) color) and create a three-class model (GM, WM, and NMA; far right column). Figure 11 (second row) shows

Figure 11 Axial slices demonstrating the NMA segmentation. The top and middle rows show the segmentation for a 2-week-old and 6-month-old, respectively. The columns from left to right are the T1W brain, “GM/WM” segmentation, the T2Wand the NMA classified in the T2W, and the three-class segmentation (GM, WM, and NMA). The last row shows the change in the three-class model form 3 to 7.5 months for average MRI volumes and average probability values. The crosshairs on the coronal slices are centered on the anterior commissure. The brightness of the colors for the GM/WM and GM/ WM/NMA represent the probability that the voxel belongs to the category (GM, blue (black in the print version); WM, yellow (white in the print version); NMA, green (gray in the print version)).

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the results of this classification for a 6-month-old. Note the higher proportion of WM in the infant brain at this age. The third row shows the results of this analysis for the average MRI templates for infants ranging in age from 3 to 7.5 months of age. We examined the changes in the NMA volume across the first year. The identification of GM with two-class models is compromised since NMA and GM are classified in the same category with the two-class (GM and WM) segmentation. So the changes in GM over age in the infancy period (e.g., Figure 8) overestimate the “‘gray matter” (neuron cell bodies, nuclei). Figure 12 shows a similar analysis of the tissue volumes for infants from 3 to 12 months. The changes in WM are the same as before, since myelinated axons are correctly identified with the two-class model. The GM in Figure 12 represents the GM (NMA) and NMA in the same figure. There is a change in volume of the NMA through the first 6 months, likely due to overall changes in axonal growth and synaptogenesis. However, this begins to drop by 7.5–9 months. This should decrease further in the second year. The changes we report in WM volume are consistent with other reports, both from MRI analysis (Deoni et al., 2011) and other methods. The rapidly changing myelination likely affects integrated neurological or behavioral functions due to communication across different brain areas (Casey et al.,

Figure 12 Gray matter, white matter, and nonmyelinated axonssegmented tissue volume in infants as a function of age. The “GM” is from the GM/WM (OM) two-class segmentation, and the “GM (NMA)” is from the GM/NMA segmentation. The error bars represent the standard error of the mean (SE).


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2000; Deoni et al., 2011). The results of the NMA volume analysis are new. Changes in GM volume have been interpreted as being primarily due to synaptogenesis. This should have a direct influence on behavioral plasticity during this age range as the emergence and pruning of synaptic connections results in learning, language development, memory, and developmental canalization. However, our analysis shows a more gradual increase in GM volume. The measurement of GM development in the first few months is confounded with volumetric increases in nonmyelinated axonal growth, whereas when axonal myelination is reflected in more WM there is an apparent increase in GM that actually reflects NMA decreases. We cannot specifically detail what GM–NMA-behavioral relations would occur with the distinction between GM and NMA, but our methods should result in a refined model of brain–behavior changes over this time period.

3.4. Contribution to Methods for Studying Brain Activity One unique contribution of the “Neurodevelopmental MRI Database” is its applicability to the measurement of brain activity in pediatric populations. The quantitative analysis of brain function requires reference MRI volumes in order to normalize brain differences across participants (e.g., for fMRI analysis). Additionally, the study of brain activity with external measurement of scalp electrical activity (electroencephalogram (EEG) and ERP) requires age-appropriate scalp electrode measurement and age-appropriate head models. The “Neurodevelopmental MRI Database” is a unique resource for the study of such brain activity and should be useful in the study quantitative studies of developmental brain functioning. In this section, we will briefly review how this can be used to construct electrode placement locations and realistic head models for doing cortical source analysis of “eventrelated potentials” (ERPs) computed from the ongoing EEG of infants and young children. Second, we will mention two recent studies using the average MRIs to determine the probable generators of “near-infrared optical spectroscopy (NIRS)” recording on the scalp. Table 2 has a list of several publications that have used this database for work in electrode placement, realistic head models for cortical source analysis, and for determining the cortical generators of NIRS locations. The EEG is a measure of changing electrical activity on the scalp that is generated by neural activity. EEG is measured by placing recording electrodes on the head and measuring interelectrode electrical potential differences at each recording electrode. The electrical activity on the head is

Table 2 Example Uses of the Neurodevelopmental MRI Database for Measuring Brain Activity in Participants from 3 Months Through Adulthood References Age Usage Electrode placement

Richards et al. (2014)

Young adults

Average electrodes for highdensity recording

Near-infrared optical spectroscopsy (NIRS) Emberson, Palmeri, Cannon, Richards, and Aslin (2013)

Infants and adults

Repetition suppression

Lloyd-Fox et al. (2013)

Infants

Action-perception and action processing

Lloyd-Fox et al. (2014)

Infants 4–7 months

Method for scalp projection

Papademetriou et al. (2013)

Infants 4–7 months

NIRS mapping for fMRI in infants

Richards (2014)

3–12 months

Method for scalp projection

Richards (2014)

2 years to adults Method for scalp projection

Realistic head models for cortical source analysis

Bathelt, O’Reilly, Clayden, Cross, 2–5 years and de Haan (2013)

Functional brain networks

Henderson, Luke, Schmidt, and Richards (2013)

Young adults

Brain activity during reading

McCleery and Richards (2012)

4.5–7.5 months Comparing realistic and unrealistic head models

McCleery, Surtees, Graham, Richards, and Apperly (2011)

Young adults

Theory of mind

Reynolds, Courage, and Richards 4.5, 6, and (2010) 7.5 months

Visual preferences

Reynolds and Richards (2005)

4.5, 6, and 7.5 months

Familiarization and recognition memory

Richards (2005)

3, 4.5, and 6 months

Spatial cueing

Richards (2012)

3 and 4.5 months

Spatial cueing

Richards (2013a, 2013b)

Young adults

Antisaccades and prosaccades

Thorpe, Cannon, and Fox (2014) 1, 4 years, adults Mu rhythm development Zieber and Richards (2013)

4.5–7.5 months Preference for mother face


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generated by concurrent neural activity inside the head, probably coming from excitatory postsynaptic potentials (Reynolds & Richards, 2009). Therefore, EEG activity is a measure of brain activity recorded in real time. One use of EEG for studying functional brain activity is to synchronize the recording of the EEG with experimental events. Averages can then be made of the EEG activity at the onset of the event, i.e., “ERPs”. It is hypothesized that groups of neurons that are simultaneously active concurrent with the psychological processes surrounding the experiment stimulus event will produce electrical activity strong enough to be measured above the ongoing EEG. The relation between such ERPs activity (or ERP components) and behavior is the ability to produce a measure of functional brain activity that can serve as an important research tool for the field of cognitive neuroscience. EEG and ERP recording in pediatric populations has been an important tool because of the relative ease of use of the recording for infants and children, especially since other standard tools used in the study of adult brain activity are not as easily applied to younger participants. The neurodevelopmental database can help in this enterprise in two fashions. First, accurate knowledge of the placement of the electrodes on the scalp of individual participants is helpful in localizing the scalp potentials to be analyzed. Reynolds and Richards (2005), for example, present a compelling case that both the number of electrodes and the placement of the electrodes on the scalp are important for localizing the ERP activity on the scalp. This is due both to the need to have full coverage of the head for analyzing brain activity and to have sufficient resolution to cover the known resolution of EEG/ERP activity. Recently, we developed tools for adult participants to place electrode locations on the scalp locations of a structural MRI from individual participants with electrode recordings and structural MRIs (Richards et al., 2014; see use in adults in Henderson et al., 2013; Richards, 2013a, 2013b; Table 2). We are now in a position to create accurate electrode placement locations on an average MRI template, or on individual participants. The individual participant electrode placements are done by having participants who have a structural MRI and an EEG recording session (Richards et al., 2014). We use fiducial measurements of the placement of the EEG recording electrodes on the infants head during the EEG recording and the same locations in the structural MRI volume to create a set of 3D positions in the MRI volume that correspond to each electrode location. Figure 13 shows an example of an EGI “HGSN-128” electrode net placement map for one individual (upper left panel). After gathering enough participants for a specific age, we can create

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Figure 13 Examples of methods for individual participants brain activity measurement. Top row from left to right: EGI HGSN electrode recording locations on the 3D reconstruction of the head; midsaggital view of the T1-weighted scan; head segmentation in skin and muscle (white), gray matter (red (dark gray in the print version)), white matter (green (light gray in the print version)), CSF (cerebrospinal fluid, yellow (white in the print version)), dura (pink (gray in the print version)), skull (blue (black in the print version)), and nasal cavity (purple (dark gray in the print version); representation of the finite element method tetrahedral wireframe used in EEG source analysis programs. Second row from left to right: 10–10 electrode recording locations on the 3D reconstruction of an average MRI template; illustration of projections from frontal scalp areas in toward prefrontal cortex; 10–10 electrode projections to cortical locations with Hammers stereotaxic atlas regions; NIRS projections from an NIRS holder onto an individual participant structural MRI, with the inferior frontal gyrus (red (gray in the print version)) and temporal–parietal junction (green (light gray in the print version)) “Regions of Interest.”

age-appropriate average electrode placement location map for the participants making up the average MRI template. These could be used by researchers who do not routinely use structural MRI for accurate placement maps. We also have the 10–10 electrode placements for each participant and for the average MRI templates (Figure 12, lower right panel). We have average electrode placements for a wide variety of ages (Table 2) (see recent use by Bathelt et al., 2013). The use of age-appropriate electrode placement maps should be extremely important in the accurate identification of scalp areas over which EEG and ERP activity occurs. This age-appropriate accuracy is critical when using electrode placements on realistic head models for ERP/EEG cortical source analysis (Reynolds & Richards, 2009).


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A second way in which both structural recordings of participants and average MRI templates are becoming useful for EEG/ERP recording is in constructing realistic head models for cortical source analysis (see Michel et al., 2004; Reynolds & Richards, 2009; also Richards, 2009; Slotnick, 2004). Cortical source analysis is a quantitative technique that takes the surface electrical recordings on the scalp (EEG or ERP) and localizes/quantifies the sources of the scalp electrical activity with generators located inside the head. This method is based on estimating the location and magnitude of electrical generators inside the head, which propagate current to the head through the various media of the head (e.g., GM, WM, CSF, skull, skin, muscle, and eyes). Accurate source analysis depends on having a realistic description of the materials inside the head (Michel et al., 2004). This is especially true for infant participants, whose head materials differ greatly from adult participants (e.g., GM, WM, and CSF) and whose brain topological arrangement substantially differs from adults (Lew et al., 2013; McCleery & Richards, 2012; Reynolds & Richards, 2009; Richards, 2009). Figure 13 shows the MRI from the infant photographed in Figure 1. The middle section shows the materials identified in this infant, and the right figure shows a wireframe model that is used in computer programs for source analysis. Using realistic models of the head for source analysis may be especially important for very young children, where the topology, quantity, and type of head materials differs significantly from adult participants (Lew et al., 2013; Reynolds & Richards, 2005, 2009). We are now in a position to accurately identify the materials inside the head and provide realistic models for participants of all ages. (For further information, see Section 2, “What’s Inside a Baby’s Head,” Richards, 2009.) Incidentally, the accurate identification of head models is also used in the analysis of “magnetoencephalogram” data. A similar approach to a realistic head model for 6-month-old infants is covered in Akiyama et al. (2013) (also see Lew et al., 2013). Table 2 has a list of references using these procedures for cortical source analysis of EEG and ERP with realistic head models. A third contribution of the Neurodevelopmental MRI Database to the measurement of brain activity in pediatric populations is recent work using average MRIs and individual participant MRIs to determine the probability of cortical generators of “NIRS” recording on the scalp. The NIRS is used to measure brain activity and has proved useful for pediatric populations (Vanderwert & Nelson, 2014). An infrared emitter placed on the scalp sends an infrared signal that penetrates several millimeters (2–3 cm) into the skull through to the cortex. Different wavelengths of light reflect off oxygenated and deoxygenated hemoglobin are measured at the scalp with a detector

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placed near the emitter. This procedure is applied to infants routinely (Vanderwert & Nelson, 2014). It is standard practice to use the 10–10 recording system to standardize the regions of the scalp for sensor placement. These coordinates have been localized to underlying cortical areas for adult participants (Homan, Herman, & Purdy, 1987; Okamoto et al., 2004; Singh, Okamoto, Dan, Jurcak, & Dan, 2005; Tsuzuki et al., 2012, 2007). Recently, researchers have begun to coregister NIRS sensor positions located on the scalp with underlying cortical anatomy. However, these have been with standard brain space (Blume, Buza, & Okazaki, 1974), on a single infant (Matsui et al., 2014), or with a limited range of ages (Kabdebon et al., 2014). Recently, the data from the Neurodevelopmental MRI Database were used to resolve this issue for infants (Richards, 2014). Methods were developed that measured both the 10–10 recording system electrode positions and the EGI HGSN and GSN positions on the scalp of individual infant participant structural MRIs. For example, Figure 13 (lower right panel) shows the location of the 10–10 electrode system on an average MRI. A line is then projected from the positions on the scalp inward until it reaches the cortex (Figure 13, second lower right panel). A line was then projected from this position on the scalp inward until it reached the cortex. Then the stereotaxic atlases (Section 2.4) were used to determine the cortical regions representing those voxels in the MRI. Figure 13 (third lower right panel) shows the 10–10 recording positions projected onto the cortex and the stereotaxic atlas regions for the Hammers atlas for this MRI. A comprehensive database of scalp-to-cortical anatomy was constructed for infants from 3 to 12 months of age. We are currently working on a similar database for children and adolescents based on the 3T MRI participant data and average MRI templates (Richards, 2014). We also applied this technique recently to a group of infant participants who had both a structural MRI and an NIRS recording (Lloyd-Fox et al., 2014). We were able to reconstruct the NIRS holder on the head of individual participants, do the inward cortical projection, and determine both cortical locations for the individual and “region-of-interest” methods for specific areas. Figure 13 (lower left panel) shows the results from one participant with the inferior frontal gyrus and temporal–parietal junction highlighted. Table 2 has a list of recent references using the Neurodevelopmental MRI Database in NIRS recordings in infants.

4. RELATION TO BRAIN–BEHAVIOR DEVELOPMENT Section 2 presented the Neurodevelopmental MRI Database, and in Section 3, we showed how it could be applied to the study of neurostructural


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development and measurement of brain activity. We have only briefly addressed the relation between brain development and behavior development. In the last two decades, there has been a growing interest in the relation between structural and functional changes in the developing human brain. In recent years, direct evidence has been shown for the effects of brain structural changes on cognitive development in pediatric populations. For instance, Rice et al. (2014) examined the role of amygdala in the development of theory of mind (i.e., mental state inferences) in children from 4 to 6 years. They looked at the relation between children amygdala MRI volume and children’s performance on face-based and story-based false-belief tasks. They found that amygdala MRI volume was related to face-based mental state inference and that larger amygdala volume was related to better performance on face-based cognitive inference. This relation was not found in their adult control group. Another study showing a close relation between brain structural development and behavior was conducted by Fjell et al. (2012). They addressed how brain structural maturation leads to improvement in self-regulation ability during childhood. The flanker task was employed to measure children’s cognitive control. Structural MRI scans were used for the quantification of cortical thickness and surface area and DTI was used for quantification of the quality of the major fiber connections between brain regions. Their results showed that the surface area of the anterior cingulate cortex, which plays an important role in impulse control and attention in adults, explained a significant proportion of children’s cognitive performance. In addition, properties of large fiber connections accounted for a certain amount of variance in self-regulation. We do not know of studies that have used the Neurodevelopmental MRI Database to study structure–behavior relations directly. The studies listed in Table 2 have used the database to advance the study of brain function development as it relates to behavioral and psychological development in pediatric and adult populations. We hope that future work will use the structural measurement capability of the database to examine structural development in individual participants and relate that development to measures of overt behavioral and psychological development. We believe the database will be extremely useful for such work.

ACKNOWLEDGMENTS This research was supported by Grants from the National Institute of Child Health and Human Development, R37-HD18942. The Chinese children MRIs were obtained from Qiyong Gong, Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University,

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Chendong, Sichuan, China, whose work was supported by National Natural Science Foundation (Nos. 81030027, 81227002, and 81220108013) and National Key Technologies R&D Program (No. 2012BAI01B03) of China. Paul Fillmore created Figure 5 based on his work with infant stereotaxic atlases for the average MRI templates (Fillmore, Richards, et al., 2014). Figures 8, 11, and 12 were adapted from preliminary analyses in work done by Sanchez et al. (2011) but were recalculated based on our infant and preschool atlases (Figure 11) and new data (Figures 8 and 12). The MRIs for the Neurodevelopmental MRI Database came from several sources. First, locally collected data come from the McCausland Center for Brain Imaging (McCausland Center for Brain Imaging, MCBI (http://www.mccauslandcenter.sc.edu). This includes all the infant 3T MRI’s, MRIs from children ranging from 6 to 18 years, and adult MRIs for participants from 18.5 through 34 years of age. Second, infant and child MRI were obtained from the NIH MRI Study of Normal Brain Development (NIHPD (http:// www.bic.mni.mcgill.ca/nihpd/info/data_access.html). This includes 2D, 1.5T scans for infants and children from 2 weeks through 4 years of age (Objective 2) and 2D and 3D, 1.5T scans for children from 4.5 to 18 years of age (Objective 1), and some adult participants. Third, child data came from age- and gender-matched typically developing controls of the Autism Brain Imaging Data Exchange (ABIDE (Di Martino et al., 2013; http://fcon_1000. projects.nitrc.org/indi/abide/). Fourth, adult data for participants from 20 to 89 years were obtained from the Information Extracted from Medical Images database (IXI: http:// biomedic.doc.ic.ac.uk/brain-development/index.php?n¼Main.Datasets). Finally, the Open Access Series of Imaging Studies (OASIS: http://www.oasis-brains.org) cross-sectional and longitudinal image sets were used for adults from 20 to 89 years of age.

REFERENCES Adolph, K. E., Gilmore, R. O., Freeman, C., Sanderson, P., & Millman, D. (2012). Toward open behavioral science. Psychological Inquiry, 23(3), 244–247. Akiyama, L. F., Richards, T. R., Imada, T., Dager, S. R., Wroblewski, L., & Kuhl, P. K. (2013). Age-specific average head template for typically developing 6-month-old infants. PLoS One, 8(9), e73821. Almli, C. R., Rivkin, M. J., & McKinstry, R. C. (2007). The NIH MRI study of normal brain development (objective-2): Newborns, infants, toddlers, and preschoolers. NeuroImage, 35(1), 308–325. Altaye, M., Holland, S. K., Wilke, M., & Gaser, C. (2008). Infant brain probability templates for MRI segmentation and normalization. NeuroImage, 43, 721–730. Anbeek, P., Vincken, K. L., Groenendaal, F., Koeman, A., Van Osch, M. J., & Van der Grond, J. (2008). Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging. Pediatric Research, 63(2), 158–163. Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—The methods. NeuroImage, 11(6), 805–821. Aubert-Broche, B., Fonov, V., Leppert, I., Pike, G. B., & Collins, D. L. (2008). Human brain myelination from birth to 4.5 years. In Medical Image Computing and ComputerAssisted Intervention–MICCAI 2008 (pp. 180–187). Berlin/Heidelberg: Springer. Avants, B. B., Epstein, C. L., Grossman, M., & Gee, J. C. (2008). Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26–41. http://dx.doi.org/ 10.1016/j.media.2007.06.004.


45

Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage, 54(3), 2033–2044. Barkovich, A. J. (2005). Pediatric neuroimaging. Philadelphia, PA: Lippincott Williams & Wilkins. Bathelt, J., O’Reilly, H., Clayden, J. D., Cross, J. H., & de Haan, M. (2013). Functional brain network organisation of children between 2 and 5years derived from reconstructed activity of cortical sources of high-density EEG recordings. NeuroImage, 82, 595–604. Blume, W. T., Buza, R. C., & Okazaki, H. (1974). Anatomic correlates of the ten–twenty electrode placement system in infants. Electroencephalography and Clinical Neurophysiology, 36, 303–307. Bourgeois, J. P. (1997). Synaptogenesis, heterochrony and epigenesis in the mammalian neocortex. Acta Paediatrica. Supplement, 422, 27–33. Brain Development Cooperative Group. (2006). The NIH MRI study of normal brain development. NeuroImage, 30(1), 184–202. Brain Development Cooperative Group. (2012). Total and regional brain volumes in a population-based normative sample from 4 to 18 years: The NIH MRI study of normal brain development. Cerebral Cortex, 22(1), 1–12. Braver, T. S., Cohen, J. D., Nystrom, L. E., Jonides, J., Smith, E. E., & Noll, D. C. (1997). A parametric study of prefrontal cortex involvement in human working memory. NeuroImage, 5, 49–62. Brunner, P., & Ernst, R. R. (1979). Sensitivity and performance time in NMR imaging. Journal of Magnetic Resonance (1969), 33(1), 83–106. Burgund, E. D., Kang, H. C., Kelly, J. E., Buckner, R. L., Snyder, A. Z., Petersen, S. E., et al. (2002). The feasibility of a common stereotactic space for children and adults in fMRI studies of development. NeuroImage, 17(1), 184–200. Byars, A. W., Holland, S. K., Strawsburg, R. H., Bommer, W., Dunn, R. S., Schmithorst, V. J., et al. (2002). Practical aspects of conducting large-scale functional magnetic resonance imaging studies in children. Journal of Child Neurology, 17(12), 885–889. Casey, B. J., Cohen, J. D., Jezzard, P., Turner, R., Noll, D. C., Trainor, R. J., et al. (1995). Activation of prefrontal cortex in children during a nonspatial working memory task with functional MRI. NeuroImage, 2, 221–229. Casey, B. J., Cohen, J. D., O’Craven, K., Davidson, R., Irwin, W., Nelson, C. A., et al. (1998). Reproducibility of fMRI results across four institutions using a working memory task. NeuroImage, 8, 249–261. Casey, B. J., Giedd, J. N., & Thomas, K. M. (2000). Structural and functional brain development and its relation to cognitive development. Biological Psychology, 54(1), 241–257. Casey, B. J., Trainor, R. J., Orendi, J. L., Schubert, A. B., Nystrom, L. E., Giedd, J. N., et al. (1997). A developmental functional MRI study of prefrontal activation during performance of a go-no-go task. Journal of Cognitive Neuroscience, 9(6), 835–847. Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994). Automatic 3D intersubject registration of MR volumetric data into standardized Talairach space. Journal of Computer Assisted Tomography, 18, 192–205. Conel, J. L. (1939–1967). Postnatal development of the human cerebral cortex (Vols. 1–8) Cambridge, MA: Harvard University Press. Deoni, S. C., Mercure, E., Blasi, A., Gasston, D., Thomson, A., Johnson, M., et al. (2011). Mapping infant brain myelination with magnetic resonance imaging. Journal of Neuroscience, 31(2), 784–791. Desikan, R. S., Segonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980.

46


Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., et al. (2013). The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659–667. http://dx.doi.org/ 10.1038/mp.2013.78. Emberson, L., Palmeri, H., Cannon, G., Richards, J. E., & Aslin, R. N. (2013). Differences in repetition suppression across sensory systems in 6-month-olds: Using NIRS to compare infant and adult neural function. Poster presented at the Cognitive Neuroscience Society, San Francisco, April 2013. Ericsson, A., Alijabar, P., & Rueckert, D. (2008). Construction of a patient-specific atlas of the brain: Application to normal aging. In ISBI (pp. 480–483): IEEE. Evans, A. C. (2005). Large-scale morphometric analysis of neuroanatomy and neuropathology. Anatomy and Embryology, 210(5–6), 439–446. Evans, A. C., Brown, E., Kelly, R. L., & Peters, T. (1994). 3D statistical neuroanatomical models from 305 MRI volumes. Piscataway, NJ: IEEE. Evans, A. C., Collins, D. L., Mills, S. L., Brown, E. D., Kelly, R. L., & Peters, T. M. (1993). 3D statistical neuroanatomical models from 305 MRI volumes. In Proceedings of the IEEEnuclear science symposium and medical imaging conference (pp. 1813–1817). Evans, A. C., Collins, D. L., & Milner, B. (1992). An MRI-based stereotactic atlas from 250 young normal subjects. Journal of the Society for Neuroscience. Abstracts, 18, 408. Fan, Y., Shi, F., Smith, J. K., Lin, W., Gilmore, J. H., & Shen, D. (2011). Brain anatomical networks in early human brain development. NeuroImage, 54(3), 1862–1871. Fillmore, P., Phillips-Meek, M., & Richards, J. (2014). Age-specific MRI brain and head templates for healthy adults from 20 through 89 years of age. Unpublished manuscript, submitted for publication. Fillmore, P., Richards, J. E., Phillips-Meek, M. C., Cryer, A., & Stevens, M. (2014). Stereotaxic MRI brain atlases for infants from 3 to 12 months. Unpublished manuscript, submitted for publication. Fjell, A. M., Walhovd, K. B., Brown, T. T., Kuperman, J. M., Chung, Y., Hagler, D. J., et al. (2012). Multimodal imaging of the self-regulating developing brain. Proceedings of the National Academy of Sciences of the United States of America, 109(48), 19620–19625. Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011). Unbiased average age-appropriate atlases for pediatric studies. NeuroImage, 54, 313–327. Fotenos, A. F. S., Snyder, A. Z. P. M. D., Girton, L. E. B., Morris, J. C. M., & Buckner, R. L. P. (2005). Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology, 64(6), 1032–1039. Fox, M., & Uecker, A. (2005). Talairach daemon client. San Antonio, TX: University of Texas Health Sciences Center.http://ric.uthscsa.edu/projects/talairachdaemon.html. Gao, W., Zhu, H., Giovanello, K. S., Smith, J. K., Shen, D., Gilmore, J. H., et al. (2009). Evidence on the emergence of the brain’s default network from 2-week-old to 2-year-old healthy pediatric subjects. Proceedings of the National Academy of Sciences of the United States of America, 106(16), 6790–6795. Ge, Y., Grossman, R. I., Babb, J. S., Rabin, M. L., Mannon, L. J., & Kolson, D. L. (2002). Age-related total gray matter and white matter changes in normal adult brain. Part I: Volumetric MR imaging analysis. AJNR. American Journal of Neuroradiology, 23(8), 1327–1333. Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., et al. (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2(10), 861–863. http://dx.doi.org/10.1038/13158. Giedd, J. N., Vaituzis, A. C., Hamburger, S. D., Lange, N., Rajapakse, J. C., Kaysen, D., et al. (1996). Quantitative MRI of the temporal lobe, amygdala, and hippocampus in


47

normal human development: Ages 4–18 years. Journal of Comparative Neurology, 366(2), 223–230. Gilmore, J. H., Lin, W., Prastawa, M. W., Looney, C. B., Vetsa, Y. S., Knickmeyer, R. C., et al. (2007). Regional gray matter growth, sexual dimorphism, and cerebral asymmetry in the neonatal brain. Journal of Neuroscience, 27(6), 1255–1260. Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., et al. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences of the United States of America, 101(21), 8174–8179. Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14(1), 21–36. Gousias, I. S., Rueckert, D., Heckemann, R. A., Dyet, L. E., Boardman, J. P., Edwards, A. D., et al. (2008). Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest. NeuroImage, 40(2), 672–684. Guimond, A., Meunier, J., & Thirion, J. P. (2000). Average brain model: A convergence study. Computer Vision and Image Understanding, 77, 192–210. Guo, X., Chen, C., Chen, K., Jin, Z., Peng, D., & Yao, L. (2007). Brain development in Chinese children and adolescents: A structural MRI study. Neuroreport, 18(9), 875–880. http://dx.doi.org/10.1097/WNR.0b013e328152777e. Guo, X., Jin, Z., Chen, K., Peng, D., & Yao, L. (2008). Gender differences in brain development in Chinese children and adolescents: A structural MRI study. Proceedings of SPIE Medical Imaging, 6916, 1–11. Hammers, A., Allom, R., Koepp, M. J., Free, S. L., Myers, R., Lemieux, L., et al. (2003). Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Human Brain Mapping, 19(4), 224–247. Heckemann, R. A., Hajnal, J. V., Aljabar, P., Rueckert, D., & Hammers, A. (2006). Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage, 33(1), 115–126. Heckemann, R. A., Hartkens, T., Leung, K., Hill, D. L. G., Hajnal, J. V., & Rueckert, D. (2003). Information extraction from medical images (IXI): Developing an e-Science application based on the globus toolkit. In Proceedings of the 2nd UK e-Science All Hands Meeting, Nottingham, UK. Henderson, J. M., Luke, S. G., Schmidt, J., & Richards, J. E. (2013). Co-registration of eye movements and event-related potentials in connected-text paragraph reading. Frontiers in Systems Neuroscience, 7, 28. Hoeksma, M. R., Kenemans, J. L., Kemner, C., & van Engeland, H. (2005). Variability in spatial normalization of pediatric and adult brain images. Clinical Neurophysiology, 116(5), 1188–1194. Homan, R. W., Herman, J., & Purdy, P. (1987). Cerebral location of international 10–20 system electrode placement. Electroencephalography and Clinical Neurophysiology, 66, 376–382. Huang, C. M., Lee, S. H., Hsiao, I. T., Kuan, W. C., Wai, Y. Y., Ko, H. J., et al. (2010). Study-specific EPI template improves group analysis in functional MRI of young and older adults. Journal of Neuroscience Methods, 189(2), 257–266. Huettel, S. A., Song, A. W., & McCarthy, G. (2004). Functional magnetic resonance imaging: Vol. 1. Sunderland, MA: Sinauer Associates. H€ uppi, P. S., Warfield, S., Kikinis, R., Barnes, P. D., Zientara, G. P., Jolesz, F. A., et al. (1998). Quantitative magnetic resonance imaging of brain development in premature and mature newborns. Annals of Neurology, 43(2), 224–235. Huttenlocher, P. R. (1990). Morphometric study of human cerebral cortex development. Neuropsychologia, 28, 517–527.

48


Huttenlocher, P. R. (1994). Synaptogenesis, synapse elimination, and neural plasticity in human cerebral cortex. In C. A. Nelson (Ed.), Threats to optimal development, the Minnesota symposia on child psychology: Vol. 27 (pp. 35–54). Hillsdale, NJ: Lawrence Erlbaum. Inder, T. E., Warfield, S. K., Wang, H., H€ uppi, P. S., & Volpe, J. J. (2005). Abnormal cerebral structure is present at term in premature infants. Pediatrics, 115(2), 286–294. Jernigan, T. L., Archibald, S. L., Berhow, M. T., Sowell, E. R., Foster, D. S., & Hesselink, J. R. (1991). Cerebral structure on MRI. Part I: Localization of age-related changes. Biological Psychiatry, 29(1), 55–67. Joshi, S., Davis, B., Jomier, M., & Gerig, G. (2004). Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage, 23, S151–S160. Kabdebon, C., Leroy, F., Simonnet, H., Perrot, M., Dubois, J., & Dehaene-Lambertz, G. (2014). Anatomical correlations of the international 10–20 sensor placement system in infants. NeuroImage, 99(1), 342–356. Kang, H. C., Burgund, E. D., Lugar, H. M., Petersen, S. E., & Schlaggar, B. L. (2003). Comparison of functional activation foci in children and adults using a common stereotactic space. NeuroImage, 19(1), 16–28. Kinney, H., Brody, B., Kloman, A., & Gilles, F. (1988). Sequence of central nervous myelination in human infancy: Pattern of myelination in autopsied infants. Journal of Neuropathology and Experimental Neurology, 47, 217–234. Kinney, H., Karthigasan, J., Borenshteyn, N., Flax, J., & Kirschner, D. (1994). Myelination in the developing human brain: Biochemical correlates. Neurochemical Research, 19, 983–996. Knickmeyer, R. C., Gouttard, S., Kang, C., Evans, D., Wilber, K., Smith, J. K., et al. (2008). A structural MRI study of human brain development from birth to 2 years. Journal of Neuroscience, 28(47), 12176–12182. Lancaster, J. L., Summerlin, J. L., Rainey, L., Freitas, C. S., & Fox, P. T. (1997). The Talairach Daemon, a database server for Talairach atlas labels. NeuroImage, 5, S633. Lancaster, J. L., Woldorff, M. G., Parsons, L. M., Liotti, M., Freitas, C. S., Rainey, L., et al. (2000). Automated Talairach atlas labels for functional brain mapping. Human Brain Mapping, 10, 120–131. Lange, N., Froimowitz, M. P., Bigler, E. D., Lainhart, J. E., & Brain Development Cooperative Group. (2010). Associations between IQ, total and regional brain volumes, and demography in a large normative sample of healthy children and adolescents. Developmental Neuropsychology, 35(3), 296–317. Lee, J. S., Lee, D. S., Kim, J., Kim, Y. K., Kang, E., Kang, K. W., et al. (2005). Development of Korean standard brain templates. Journal of Korean Medical Science, 20, 483–488. Lemaıˆtre, H., Crivello, F., Grassiot, B., Alpe´rovitch, A., Tzourio, C., & Mazoyer, B. (2005). Age- and sex-related effects on the neuroanatomy of healthy elderly. NeuroImage, 26(3), 900–911. Lenroot, R. K., & Giedd, J. N. (2006). Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neuroscience and Biobehavioral Reviews, 30(6), 718–729. Lenroot, R. K., Gogtay, N., Greenstein, D. K., Wells, E. M., Wallace, G. L., & Giedd, J. N. (2007). Sexual dimorphism of brain development trajectories during childhood and adolescents. NeuroImage, 36(4), 1065–1073. Leppert, I. R., Almli, C. R., McKinstry, R. C., Mulkern, R. V., Pierpaoli, C., Rivkin, M. J., et al. (2009). T2 relaxometry of normal pediatric brain development. Journal of Magnetic Resonance Imaging, 29(2), 258–267. Lew, S., Sliva, D. D., Choe, M. S., Grant, P. E., Okada, Y., Wolters, C. H., et al. (2013). Effects of sutures and fontanels on MEG and EEG source analysis in a realistic infant head model. NeuroImage, 76, 282–293.


49

Lloyd-Fox, S., Richards, J. E., Blasi, A., Murphy, D., Elwell, C. E., & Johnson, M. H. (2014). Co-registering NIRS with underlying cortical areas in infants. Neurophotonics, 1(2), 020901. Lloyd-Fox, S., Wu, R., Richards, J. E., Elwell, C. E., & Johnson, M. H. (2013). Cortical activation to action perception is associated with action production abilities in young infants. Cerebral Cortex, bht207. http://dx.doi.org/10.1093/cercor/bht207. Mandal, P. K., Mahajan, R., & Dinov, I. D. (2012). Structural brain atlases: Design, rationale, and applications in normal and pathological cohorts. Journal of Alzheimer’s Disease, 31, 1–20. Marcus, D. S., Fotenos, A. F., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2010). Open access series of imaging studies: Longitudinal MRI data in nondemented and demented older adults. Journal of Cognitive Neuroscience, 22(12), 2677–2684. Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007). Open access series of imaging studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience, 19(9), 1498–1507. Matsui, M., Homae, F., Tsuzuki, D., Watanabe, H., Katagiri, M., Uda, S., et al. (2014). Referential framework for transcranial anatomical correspondence for fNIRS based on manually traced sulci and gyri of an infant brain. Neuroscience Research, 80, 55–68. Mazziotta, J. C., Toga, A. W., Evans, A., Fox, P., & Lancaster, J. (1995). A probablistic atlas of the human brain: Theory and rationale for its development. NeuroImage, 2, 89–101. Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., et al. (2001). A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 356(1412), 1293–1322. McCleery, J. P., & Richards, J. E. (2012). Comparing realistic head models for cortical source locationization of infant event-related potentials. Developmental Medicine and Child Neurology, 54(Suppl. 2), 10, abstract. McCleery, J. P., Surtees, A., Graham, K. A., Richards, J. E., & Apperly, I. A. (2011). The neural and cognitive time-course of theory of mind. Journal of Neuroscience, 31, 12849–12854. Mewes, A. U., Hueppi, P. S., Als, H., Rybicki, F. J., Inder, T. E., McAnulty, G. B., et al. (2006). Regional brain development in serial magnetic resonance imaging of low-risk preterm infants. Pediatrics, 118(1), 23–33. Michel, C. M., Murray, M. M., Lantz, G., Gonzalez, S., Spinelli, L., & Grave de Peralta, R. (2004). EEG source imaging. Clinical Neurophysiology, 115(10), 2195–2222. Muzik, O., Chugani, D. C., Juhasz, C., Shen, C. G., & Chugani, H. T. (2000). Statistical parametric mapping: Assessment of application in children. NeuroImage, 12(5), 538–549. NIH. (1998). Pediatric study centers (PSC) for a MRI study of normal brain development. NIH RFP NIHNINDS- 98–13, sponsored by National Institute of Neurological Disorders and Stroke, National Institute of Mental Health, National Institute of Child Health and Human Development. Okamoto, M., Dan, H., Sakamoto, K., Takeo, K., Shimizu, K., Kohno, S., et al. (2004). Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping. NeuroImage, 21(1), 99–111. Papademetriou, M. D., Richards, J. E., Correira, T., Blasi, A., Lloyd-fox, S., Johnson, M., et al. (2013). Cortical mapping of 3D optical topography in infants. Advances in Experimental Medicine and Biology, 985, 455–461. Paus, T., Zijdenbos, A., Worsley, K., Collins, D. L., Blumenthal, J., Giedd, J. N., et al. (1999). Structural maturation of neural pathways in children and adolescents: In vivo study. Science, 283(5409), 1908–1911.

50


Penny, W., Friston, K., Ashburner, J., Keibel, S., & Nichols, J. (2007). Statistical parametric mapping: The analysis of functional brain images. New York: Academic Press. Phillips, M. C., Richards, J. E., Stevens, M., & Connington, A. (2013). A stereotaxic MRI brain atlas for infant participants. In Paper presented at the SRCD, Seattle, WA, April 2013. http:// jerlab.psych.sc.edu/richardsinfo/conferencepresentations.php. Prastawa, M., Gilmore, J. H., Lin, W., & Gerig, G. (2005). Automatic segmentation of MR images of the developing newborn brain. Medical Image Analysis, 9, 457–466. Reynolds, G. D., Courage, M. L., & Richards, J. E. (2010). Infant attention and visual preferences: Converging evidence from behavior, event-related potentials, and cortical source localization. Developmental Psychology, 46, 886–904. Reynolds, G. D., & Richards, J. E. (2005). Familiarization, attention, and recognition memory in infancy: An ERP and cortical source localization study. Developmental Psychology, 41, 598–615. Reynolds, G., & Richards, J. (2009). Cortical source localization of infant cognition. Developmental Neuropsychology, 3, 312–329. Rice, K., Viscomi, B., Riggins, T., & Redcay, E. (2014). Amygdala volume linked to individual differences in mental state inference in early childhood and adulthood. Developmental Cognitive Neuroscience, 8, 153–163. Richards, J. E. (2005). Localizing cortical sources of event-related potentials in infants’ covert orienting. Developmental Science, 8(3), 255–278. Richards, J. E. (2009). Attention in the brain and early infancy. Neoconstructivism: The new science of cognitive development: Vol. 1. New York, NY: Oxford University Press. Richards, J. E. (2012). Cortical source analysis of ERP in infant spatial cueing. In Poster presented at the international conference on infant studies, Minneapolis, MN, June 2012. Richards, J. E. (2013a). Cortical sources of ERP in the prosaccade and antisaccade eye movements using realistic source models. Frontiers in Systems Neuroscience, 7, 27. http://dx.doi. org/10.3389/fnsys.2013.00027. Richards, J. E. (2013b). Cortical source analysis of ERP in infant spatial cueing. In Poster presented at the society for research in child development, Seattle, WA, April 2013. Richards, J. E. (2014). Scalp locations projected to cortical anatomy for infant NIRS. In Poster presented at the international conference on infant studies, Berlin, Germany, July 2014. Richards, J. E., Boswell, C., Stevens, M., & Vendemia, J. M. (2014). Evaluating methods for constructing average high-density electrode positions. Brain Topography, 1–17. Rutherford, M. A. (2002). MRI of the neonatal brain. London, UK: W. B. Saunders Co. Salat, D. H., Greve, D. N., Pacheco, J. L., Quinn, B. T., Helmer, K. G., Buckner, R. L., et al. (2009). Regional white matter volume differences in nondemented aging and Alzheimer’s disease. Neuroimage, 44(4), 1247–1258. Sanchez, C., Richards, J., & Almli, C. R. (2011). Neurodevelopmental MRI brain templates for children from 2 weeks to 4 years of age. Developmental Psychology, 54, 77–91. Sanchez, C., Richards, J., & Almli, C. R. (2012). Age-specific MRI templates for pediatric neuroimaging. Developmental Neuropsychology, 37(5), 379–399. Sato, K., Taki, Y., Fukuda, H., & Kawashima, R. (2003). Neuroanatomical database of normal Japanese brains. Neural Networks, 16(9), 1301–1310. Shattuck, D. W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K. L., et al. (2008). Construction of a 3D probabilistic atlas of human cortical structures. NeuroImage, 39(3), 1064–1080. Shi, F., Fan, Y., Tang, S., Gilmore, J. H., Lin, W. L., & Shen, D. G. (2010). Neonatal brain image segmentation in longitudinal MRI studies. NeuroImage, 49(1), 391–400. Shi, F., Yap, P. T., Wu, G., Jia, H., Gilmore, J. H., Lin, W., et al. (2011). Infant brain atlases from neonates and 1- and 2-year-olds. PLoS One, 6(4), e18746.


51

Singh, A. K., Okamoto, M., Dan, H., Jurcak, V., & Dan, I. (2005). Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI. NeuroImage, 27, 842–851. Slotnick, S. D. (2004). Source localization of ERP generators. In T. C. Hardy (Ed.), Eventrelated potentials: A methods handbook (pp. 149–166). Cambridge: The MIT Press. Smith, C. D., Chebrolu, H., Wekstein, D. R., Schmitt, F. A., & Markesbery, W. R. (2007). Age and gender effects on human brain anatomy: A voxel-based morphometric study in healthy elderly. Neurobiology of Aging, 28(7), 1075–1087. Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., JohansenBerg, H., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, 208–219. Sowell, E. R., Thompson, P. M., & Toga, A. W. (2004). Mapping changes in the human cortex throughout the span of life. Neuroscientist, 10(4), 372–392. Sullivan, E. V., Rosenbloom, M., Serventi, K. L., & Pfefferbaum, A. (2004). Effects of age and sex on volumes of the thalamus, pons, and cortex. Neurobiology of Aging, 25(2), 185–192. Taki, Y., Goto, R., Evans, A., Zijdenbos, A., Neelin, P., Lerch, J., et al. (2004). Voxel-based morphometry of human brain with age and cerebrovascular risk factors. Neurobiology of Aging, 25(4), 455–463. Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain: 3-Dimensional proportional system—An approach to cerebral imaging. New York: Theme Medical Publishers. Tang, Y., Hojatkashani, C., Dinov, I. D., Sun, B., Fan, L., Lin, X., et al. (2010). The construction of a Chinese MRI brain atlas: A morphometric comparison study between Chinese and Caucasian cohorts. NeuroImage, 51, 33–41. Thompson, P. M., Mega, M. S., Woods, R. P., Zoumalan, C. I., Lindshield, C. J., Blanton, R. E., et al. (2001). Cortical change in Alzheimer’s disease detected with a disease-specific population-based brain atlas. Cerebral Cortex, 11(1), 1–16. Thorpe, S. G., Cannon, E. N., & Fox, N. A. (2014). Spectral and source structural development of mu and alpha rhythms from infancy through adulthood. Unpublished manuscript. Toga, A. W., Thompson, P. M., & Sowell, E. R. (2006). Mapping brain maturation. FOCUS: The Journal of Lifelong Learning in Psychiatry, 4(3), 378–390. Tsuzuki, D., Cai, D. S., Dan, H., Kyutoku, Y., Fujita, A., Watanabe, E., et al. (2012). Stable and convenient spatial registration of stand-alone NIRS data through anchor-based probabilistic registration. Neuroscience Research, 72(2), 163–171. Tsuzuki, D., Jurcak, V., Singh, A. K., Okamoto, M., Watanabe, E., & Dan, I. (2007). Virtual spatial registration of stand-alone fNIRS data to MNI space. NeuroImage, 34(4), 1506–1518. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273–289. Vanderwert, R. E., & Nelson, C. A. (2014). The use of near-infrared spectroscopy in the study of typical and atypical development. NeuroImage, 85, 264–271. Waber, D. P., De Moor, C., Forbes, P. W., Almli, C. R., Botteron, K. N., Leonard, G., et al. (2007). The NIH MRI study of normal brain development: Performance of a population based sample of healthy children aged 6 to 18 years on a neuropsychological battery. Journal of International Neuropsychological Society, 13(5), 729–746. Weisenfeld, N. I., & Warfield, S. K. (2009). Automatic segmentation of newborn brain MRI. NeuroImage, 47(2), 564–572. Wilke, M., Holland, S. K., Altaye, M., & Caser, C. (2008). Template-O-Matic: A toolbox for creating customized pediatric templates. NeuroImage, 41(3), 903–913.

52


Wilke, M., Schmithorst, V. J., & Holland, S. K. (2002). Assessment of spatial normalization of whole-brain magnetic resonance images in children. Human Brain Mapping, 17(1), 48–60. Woolrich, M. W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., et al. (2009). Bayesian analysis of neuroimaging data in FSL. NeuroImage, 45(1), S173–S186. http://dx.doi.org/10.1016/j.neuroimage.2008.10.055. Xie, W., Richards, J. E., Lei, D., Kang, L., & Gong, Q. (2014a). Comparison of the brain development trajectory between Chinese and U.S. children and adolescents. Submitted. Xie, W., Richards, J. E., Lei, D., Kang, L., & Gong, Q. (2014b). The construction of MRI brain/head templates for Chinese children from 7–16 years of age. Submitted. Xue, H., Srinivasan, L., Jiang, S., Rutherford, M., Edwards, A. D., Rueckert, D., et al. (2007). Automatic segmentation and reconstruction of the cortex from neonatal MRI. NeuroImage, 38(3), 461–477. Yakovlev, P. I., & Lecours, A. R. (1967). The myelogenetic cycles of regional maturation of the brain. In A. Minkowski (Ed.), Regional development of the brain in early life (pp. 3–70). Oxford: Blackwell Science. Yoon, U., Fonov, V. S., Perusse, D., & Evans, A. C. (2009). The effect of template choice on morphometric analysis of pediatric brain data. NeuroImage, 45(3), 769–777. Zhang, Y. Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markove random field model and the expectation maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45–57. Zieber, N., & Richards, J. E. (2013). Developmental changes in the infant N290 in response to faces and toys. Poster presented at the Society for Research in Child Development, Seattle, WA, April 2013.

CHAPTER TWO

The Importance of Puberty for Adolescent Development: Conceptualization and Measurement Sheri A. Berenbaum*,†,1, Adriene M. Beltz*,{, Robin Corley}

*Department of Psychology, The Pennsylvania State University, University Park, Pennsylvania, USA † Department of Pediatrics, The Pennsylvania State University, University Park, Pennsylvania, USA { Department of Human Development and Family Studies, The Pennsylvania State University, University Park, Pennsylvania, USA } Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado, USA 1 Corresponding author: e-mail address: [email protected]

Contents 1. Puberty and Adolescent Development 2. Defining and Measuring Puberty 2.1 Pubertal Processes 2.2 Indices of Pubertal Development 2.3 Clarifying What Is Measured 2.4 Mechanisms from Puberty to Behavior 3. Evidence for Pubertal Influences on Adolescent Development 3.1 The Role of Pubertal Timing in Behavior 3.2 The Role of Pubertal Status in Behavior 3.3 Brain Development in Adolescence 4. Assumptions, Strengths, and Limitations of Work on Puberty–Behavior Links 4.1 The Nature of Adolescent Change 4.2 Understanding Links Between Pubertal Status and Adolescent Psychological Change 4.3 Understanding the Neural and Psychological Role of Pubertal Hormones 4.4 Methodological Issues 5. Conclusions and Future Directions 5.1 Summary of Findings on Pubertal Influences on Psychological Development 5.2 Mechanisms and Methods 5.3 Linking to Other Domains of Development References


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Sheri A. Berenbaum et al.

Abstract How and why are teenagers different from children and adults? A key question concerns the ways in which pubertal development shapes psychological changes in adolescence directly through changes to the brain and indirectly through the social environment. Empirical work linking pubertal development to adolescent psychological function draws from several different perspectives, often with varying approaches and a focus on different outcomes and mechanisms. The main themes concern effects of atypical pubertal timing on behavior problems during adolescence, effects of pubertal status (and associated hormones) on normative changes in behaviors that can facilitate or hinder development (especially risk-taking, social reorientation, and stress responsivity), and the role of puberty in triggering psychopathology in vulnerable individuals. There is also interest in understanding the ways in which changes in the brain reflect pubertal processes and underlie psychological development in adolescence. In this chapter, we consider the ways that puberty might affect adolescent psychological development, and why this is of importance to developmentalists. We describe the processes of pubertal development; summarize what is known about pubertal influences on adolescent development; consider the assumptions that underlie most work and the methodological issues that affect the interpretation of results; and propose research directions to help understand paths from puberty to behavior. Throughout, we emphasize the importance of pubertal change in all aspects of psychological development, and the ways in which puberty represents an opportunity to study the interplay of biological and social influences.

1. PUBERTY AND ADOLESCENT DEVELOPMENT How and why are teenagers different from children and adults? Developmentalists have long been interested in psychological changes that take place in adolescence. These include normative changes associated with the attainment of reproductive maturity (e.g., family, peer, and romantic relationships, adult cognition), variations in those normative processes (e.g., risk-taking), and the development of problems that originate in adolescence or increase in incidence or severity at this time (e.g., depression, substance use, and psychosis, Lydon, Wilson, Child, & Geier, 2014; Spear, 2009; Zahn-Waxler, Shirtcliff, & Marceau, 2008). The changes that occur in adolescence are undoubtedly influenced by a variety of social and biological processes. For much of the recent past, adolescent psychological development was largely seen to reflect social processes, and research on biological processes ran on a parallel and not very visible track. But there has been a resurgence of interest in the ways that pubertal development helps to shape adolescent behavior directly and

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through the social environment. Hormonal changes at puberty result in dramatic changes in anatomy and physiology. Hormonal changes also contribute to psychological change through direct effects on brain structure and function and through indirect effects via responses to a teen’s changing body and social roles, including the teen’s own perceptions and treatment from peers, parents, and other adults. Some of the key questions in developmental science now concern the ways in which pubertal processes contribute to psychological changes in adolescence, particularly the increasing rates of psychopathology and problem behaviors; this is reflected in several recent journal special issues (Engle, 2013; Luciana, 2010; Segalowitz & Luciana, 2014; Sisk & Berenbaum, 2013). Studies of links between puberty and psychological development are concerned with a number of separate, but related, questions, with frequent blurring of questions and lack of clarity about methods best suited to answer them. Therefore, the goals of our chapter are to (1) summarize the processes of pubertal development and consider how they might affect psychological development; (2) summarize the work on pubertal influences on adolescent development, highlighting the themes, findings, and open questions; (3) probe the assumptions that underlie most work, and articulate what is tested in different types of studies, also considering methodological issues; and (4) propose research directions to help understand paths from puberty to behavior.

2. DEFINING AND MEASURING PUBERTY In order to understand how puberty might affect psychological development, it is important to be clear about the pubertal processes themselves and the aspects of those processes that are thought to matter. We discuss the changes of puberty and then how they have been considered in relation to psychological development.

2.1. Pubertal Processes Puberty is a series of processes involving the development of the hypothalamic–pituitary–gonadal axis (gonadarche), the adrenal system (adrenarche), and growth—culminating in reproductive maturity and adult anatomy and physiology. Psychological research tends to focus on the sex hormones that increase at puberty (especially estradiol in girls, testosterone in boys, and adrenal hormones in both sexes) and on the physical features that are influenced by these hormones (secondary sex characteristics). For

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example, estradiol influences breast development and menarche in girls, testosterone influences testicular development and voice changes in boys, androgens influence body hair in both sexes, and sex hormones and growth hormone influence height in both sexes. Several points about pubertal development are important to consider in evaluating its role in psychological development. Different pubertal processes and their related features develop on somewhat different timetables (Susman et al., 2010; Tanner, 1978), with potentially differential value in terms of social signaling and personal salience. Adrenarche occurs earlier than gonadarche, with adrenarche occurring close to the same age in both sexes, but gonadarche occurring earlier in girls than in boys. The features of puberty develop in a fairly similar sequence for all youth, but there is considerable variability in the age at which they develop (their onset or timing) and their speed of development (tempo); there is also likely variability in the synchrony of development of different features but this has not been well studied (Mendle, 2014; Susman et al., 2010). For girls, breast development is typically the first sign of puberty and is visible to others, whereas menarche occurs late in puberty and is private. For boys, testicular enlargement is typically the first sign of puberty and is generally not apparent to others, whereas the height spurt (visible to others) does not occur until midpuberty. Progression of pubertal development is generally described by Tanner stages (1–5) for the cardinal features: genitalia (breast in girls, penis and testis in boys), pubic hair, and height spurt (Tanner, 1962, 1978). Prepuberty is Tanner stage 1 and complete development is Tanner stage 5. Intermediate stages of development are described by Tanner stages 2–4. Midpuberty is Tanner stage 3 and is associated with the surge in gonadal hormones (estradiol in girls, testosterone in boys). Tanner stages for each feature clearly form an ordinal scale; it is not as clear that they form the interval scale typically used. For example, it is not known whether the difference in breast development between Tanner 2 and 3 is the same as that between Tanner 3 and 4. Several aspects of the pubertal process are illustrated in Figure 1, which is taken from a classic work on physical development (Tanner, 1978). The figure shows the sequence of pubertal events in boys and girls described by Marshall and Tanner (1969, 1970) from data they collected in the 1960s. The figure is used here to illustrate points made earlier: girls mature earlier than boys, different features develop on different timetables, and there is considerable variability across children in the ages at which they reach different stages of puberty. These data come from a sample that is unique in several ways: it was the largest number of children studied longitudinally


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Figure 1 Sequences of events at puberty in girls (top) and boys (bottom). Reprinted from Valadian, I., & Porter, D. (1977). Physical growth and development from conception to maturity. Boston: Little Brown. Copyright Wolters Kluwer.

up to that point, but the children lived in family groups in a children’s home, were of low socioeconomic status, and may not have received good care before they entered the home. Thus, caution is needed when making inferences to contemporary samples about specific events such as age at menarche, but the general pattern of results reported in this early study is consistent with recent data (e.g., Susman et al., 2010).

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2.2. Indices of Pubertal Development There are many ways to measure the processes of puberty. Hormones can be measured directly but it is more common to measure physical development reported by the youth or by a parent, or rated by a health professional during a physical exam. In this section, we describe the different indicators of pubertal development and provide examples of their use in psychological research; a summary of the indicators is provided in Table 1. Section 3 provides a discussion of the evidence regarding the psychological significance of different aspects of puberty. 2.2.1 Hormone Levels Developmentalists have generally measured the physical processes of puberty rather than sex hormones directly. This initially reflected technical difficulties in hormone measurement, but many hormones can now be assayed at relatively low cost using saliva samples. Nevertheless, physical features still remain an important indicator because they reflect an integration of physiological processes. Furthermore, salivary hormones are not simple to measure: results vary with procedures used for collecting saliva samples and with the method of assay (Handelsman & Wartofsky, 2013; van Anders, 2010). Nor are hormone levels straightforward to interpret. They reflect just a single snapshot unless repeated sampling is done (a difficult task). Hormones vary cyclically, for example, in relation to the time of day and season in men and to menstrual cycle in girls and women. Hormones reflect not just pubertal development but multiple sources of variation, including genes, environmental factors such as diet and exercise, and other hormones. Hormones are even changed by behavior itself; for example, testosterone is increased when a favored sports team wins a game (Bernhardt, Dabbs, Fielden, & Lutter, 1998) and is reduced by fatherhood (Gettler, McDade, Feranil, & Kuzawa, 2011). And another level of complexity is added by the fact that the physical effects of hormones depend not just on level but on the individual’s sensitivity to the hormones, typically reflected in variation in characteristics of receptors. 2.2.2 Pubertal Status Pubertal development is thus most often measured by physical development, with pubertal status reflecting a youth’s point in development. Status is usually described by the five Tanner stages noted earlier. Some behavioral changes are likely to occur at the onset of puberty, whereas others are likely to occur in the


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middle of puberty, and still others when development is complete. Although there is some discussion of the differential psychological significance of different stages (e.g., Dorn, Dahl, & Biro, 2006), there is little empirical investigation of the topic, likely because of methodological challenges. The gold standard for measuring pubertal status has long been ratings by health professionals from physical exams when the youth is undressed. But the expensive and intrusive nature of such measures has led to the use of ratings from youth themselves or their parents. The most widely used measure is the Pubertal Development Scale (PDS; Petersen, Crockett, Richards, & Boxer, 1988), which focuses on the development of several secondary sexual characteristics, including body hair, skin changes, and growth spurt in both sexes, facial hair and deepening voice in boys, and breast development and menarche in girls. Menarche is reported as absent or completed, and if completed, age at onset. Other items are rated on a 4-point scale: 1: no development, 2: barely, 3: definitely, 4: completed. The psychometric properties of the PDS are well studied (Petersen et al., 1988), and correlations with pubertal stage rated by health professionals are generally around 0.70 (Schmitz et al., 2004; Shirtcliff, Dahl, & Pollak, 2009). PDS scores have been shown to correlate with salivary hormone levels to the same extent as physical exam did (Shirtcliff et al., 2009). The PDS has been considered to be “most appropriate for broad estimates of development, or for use in longitudinal studies” (Coleman & Coleman, 2002, p. 547), although this is not without controversy (e.g., Dorn et al., 2006; Shirtcliff et al., 2009). Particular concerns relate to greater inaccuracies at some stages than others (Huang et al., 2012) and apparent regressions when children report on their development across time (although regressions may also be seen in ratings by health professionals). Other measures have been developed to parallel Tanner stage ratings by health professionals. This includes measures in which youth rate their development by comparing themselves to line drawings or photographs of physical features representing the different Tanner stages (Biro & Dorn, 2005; Morris & Udry, 1980), but this may add confusion because youth may judge on factors besides developmental stage (e.g., size rather than stage of breasts, amount rather than distribution of pubic hair). All self-report measures have limitations; a particular concern is that self-perceptions of physical development may differ from objective measures in ways that are related to outcomes of interest. Pubertal status has become an increasing focus of psychological studies, especially those concerned with the ways that hormones induce normative

Table 1 Measures of Pubertal Development Measure

Score Used

Construct

Advantages

Age

Chronological age Adolescence Highly correlated with puberty (approximately ages 9–18 years)

Age and puberty confounded

Pubertal Development Scale, youth selfreport

Degree of development Pubertal (1–4) across multiple indicators status of secondary sexual development

Concerns about validity; mixes indicators of different pubertal processes

Not intrusive; suitable for longitudinal research, large-scale studies

Limitations

Physical exam by Tanner stage for individual health professional features: pubic hair, breasts (girls), genitals (boys)

Pubertal status

“Gold standard” index of pubertal Intrusive; high refusals development

Color photographs, youth self-report

Tanner stage for individual features

Pubertal status

Less intrusive than physical exam Ratings may be based on features unrelated to development (e.g., size)

Line drawings, youth self-report

Tanner stage for individual features

Pubertal status

Less graphic than pictures, reduces Simplified images may not provide focus on extraneous features sufficient information to guide ratings

Menarche

Age of first menstrual period

Pubertal timing

Maturation of gonadal axis, follows gonadal hormone surge

Pubertal tempo

Endpoint can be easily measured Based on only two timepoints; onset is difficult to capture; no equivalent for boys

Difference Time from onset of breast between two development to menarche pubertal milestones

Not an external social signal; occurs late in development so not an indicator of pubertal onset; no easy equivalent for boys

Linear trajectory midpoint

Age at midpuberty determined Pubertal from longitudinal assessments of timing pubertal status

Simple to understand, calculate, Estimates depend on timing of and interpret; partially adjusts for assessments; does not allow missing data and measurement independent assessment of onset error

Linear trajectory slope

Rate of change in status across assessments

Simple to understand and calculate Endpoint depends on assessment intervals; assumes constant rate of development

Logistic trajectory midpoint

Age at midpuberty determined Pubertal from longitudinal assessments of timing pubertal status

Same advantages as linear trajectory; logistic curves fit group data better than linear curves; allows for varying assessment endpoints

Assumes that development proceeds fastest at midpoint and slower at beginning and end; does not allow independent assessment of onset

Logistic trajectory slope at midpoint

Rate of change in status at midpoint of puberty

Pubertal tempo

Estimates peak rate of developmental change

Estimate may be biased by varying intervals between assessments; unclear reliability and validity of instantaneous change at one point

Salivary or serum hormones

Testosterone (boys) Estradiol (girls)

Pubertal status

Direct assessment of pubertal processes

Hormone levels also depend on other factors (e.g., menstrual cycle, genes); reciprocally influenced by behavior

Peer comparison, youth self-report

Perception of self development Perceived or May have as much or more compared to peers subjective consequence as objective timing timing

Pubertal tempo

Peer group bias due to homophily; restricted to external pubertal signs

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changes in behaviors such as risk-taking and that trigger psychopathology in vulnerable youth, along with the neural substrates of those changes. With respect to normative behaviors, for example, sensation seeking and associated behaviors (such as cigarette smoking) increase in early to middle adolescence as pubertal hormones increase, especially for boys, and then stabilize in early to middle adulthood, with no subsequent increase with age (Lydon et al., 2014; Steinberg et al., 2008). With respect to puberty as a trigger for problems, for example, the sex differences in depression and eating disorders that emerge in adolescence have been linked to the attainment of midpuberty in girls and the associated surge in estradiol (Angold, Costello, & Worthman, 1998; Klump, Keel, Sisk, & Burt, 2010). 2.2.3 Pubertal Timing Although all typical youth experience all Tanner stages, they vary considerably in the age at which they do so (Tanner, 1978), so pubertal timing reflects their pubertal status relative to others. Timing is typically measured by having youth self-report or health professionals rate their status, and then comparing that status to peers (either in the study sample or to population norms). Recent advances in describing pubertal timing have come from growth modeling of longitudinal data. Such models take advantage of multiple data points and adjust for missing data and apparent regressions in development associated with imperfect measurement. They also consider the shape of development. Results show that a logistic (S-shaped) model of development fits better than a simple linear model, in accordance with the physiological changes that take place at puberty (Beltz, Corley, Wadsworth, Bricker, & Berenbaum, 2014; Castellanos-Ryan, Parent, Vitaro, Tremblay, & Seguin, 2013; Eaves et al., 2004; Huang, Biro, & Dorn, 2009; Marceau, Ram, Houts, Grimm, & Susman, 2011; Mendle, Harden, BrooksGunn, & Graber, 2010). (This also suggests that Tanner stages do not reflect an interval scale.) But data indicate that all indices of pubertal timing provide similar information: timing estimates by linear and logistic models are highly correlated with each other in both sexes and with menarche in girls, all measures correlate similarly with behavior, and there is large genetic overlap among the measures (Beltz et al., 2014; Corley, Beltz, Wadsworth, & Berenbaum, 2014). Most research on psychological consequences of puberty focuses on timing. There is a particular emphasis on the link between early maturation in girls and concurrent problem behavior, generally seen to result from the


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negative influence of older (often male) peers (Caspi & Moffitt, 1991; Ge, Conger, & Elder, 1996). But there are other aspects of timing that likely have psychological significance worthy of investigation. There is some suggestion that development that is off-time in any way can have adverse consequences, with particular effects of late development in boys (Graber, 2013). Furthermore, because puberty is not a single process and different features develop on different timetables, it is possible that timing effects are subtle. For example, there may be different consequences of having early initial breast development versus early growth spurt. Relatedly, it may matter if the features do not develop in synchrony (Mendle, 2014; Susman et al., 2010). 2.2.4 Pubertal Tempo Beyond differences in the timing of development, individuals vary in the tempo of development, or the speed with which they complete the processes culminating in full reproductive maturity (Tanner, 1978). Tempo has traditionally been measured as the interval between two points in puberty, most often the time from early breast development (Tanner stage 2) to menarche (which occurs late in development). Several studies show that tempo is adjusted when timing is not typical, so that tempo is slow for children who enter puberty early, and fast for children who enter late; this is bestdocumented in girls (Biro et al., 2006; Martı´-Henneberg & Vizmanos, 1997; Pantsiotou et al., 2008). The psychological significance of variations in pubertal tempo has not been well studied, in part because an accurate index of tempo requires information at multiple points of development, including early and late stages of puberty. Recent studies have begun to address this gap. Growth models of longitudinal data provide estimates of pubertal tempo as well as timing, but different models conceptualize and measure tempo in different ways from each other and from traditional methods: traditional measures reflect the interval between pubertal events, linear models estimate rate of change per year between two consecutive puberty stages, and logistic models estimate peak change rate at the midpoint of puberty. Therefore, different estimates can lead to different conclusions about the importance of tempo for psychological development (see Beltz et al., 2014, for examples). 2.2.5 Subjective Versus Objective Pubertal Development Recent work has returned to an early theme in measuring pubertal development: the objective truth versus the youth’s perception of it (Dorn et al., 2006; Dubas, Graber, & Petersen, 1991; Mendle, 2014). For example,

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psychological risk may be more strongly linked to a girl’s perceived pubertal timing than to age at menarche. In an early longitudinal study addressing this issue, objective and subjective reports of pubertal timing were found to be moderately correlated, and subjective timing predicted behavior better than objective timing (Dubas et al., 1991). This is a topic worth further investigation with contemporary measures.

2.3. Clarifying What Is Measured Most measures of status, timing, and tempo use either a single measure of menarche in girls or a summary measure of pubertal development (e.g., an average of scores on all PDS items). These measures do appear to reflect processes that have psychological significance; for example, menarche has meaning for the girl experiencing it, and a summary score accurately reflects a youth’s overall development. But they may fail to capture other important processes. For example, menarche is not a signal to others, and it occurs late in puberty after the hormone surge and most aspects of development have advanced; a summary combines features that develop on different timetables (and perhaps different scales) and are differently linked to adrenal and gonadal hormones. Furthermore, significance may lie not just in the development of individual features or in an average score, but in the coordinated development of the different features. There may also be differential significance to different points in development. Onset of development signals to both the youth and the social world that puberty has begun, so this is likely a time of identity development and new social roles and expectations. The midpoint of puberty is associated with a surge in sex hormones, so this is likely a time for major changes in the brain. Menarche in girls marks reproductive maturity, although it may take some time before menstrual cycles become regular and fertility is achieved. Complete development signifies full adult status, at least biologically. Few studies differentiate among these points, and it is not uncommon for an investigator to discuss pubertal onset but use a measure of a point later in development, such as menarche or PDS score of 2.5 (midpoint). Recent methodological advances in modeling pubertal development do not solve this problem; for both logistic and linear growth models, pubertal timing is estimated at the midpoint of development (Tanner stage 3 or PDS 2.5). There is no single best way to describe pubertal development, and research questions should drive the measures or models used (Beltz et al., 2014; Dorn & Biro, 2010; Dorn et al., 2006). This point is not new


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(e.g., Dorn et al., 2006), but it has often gone unheeded, likely for practical reasons. It is easy and cost effective to use a total score from a self-report measure, and measures that provide more information are intrusive and likely to be refused by many youth, reducing sample size and study generalizability. For example, in one national study, only about 70% of children had valid pubertal measurements on at least one of seven annual assessments, and girls had more usable data than boys at all ages (Susman et al., 2010). It is also difficult to collect the longitudinal data most useful for differentiating pubertal stages, especially onset versus midpuberty. We return to this issue in Section 4.

2.4. Mechanisms from Puberty to Behavior Pubertal processes may influence behavior through hormones or through secondary effects of those hormones. Sex hormones may produce changes to physiological processes that, in turn, induce psychological change. Hormones may act by (a) producing permanent or temporary changes to brain structures and functions underlying behavior, (b) affecting the transcription of specific genes involved in behavior, or (c) affecting other aspects of anatomy and physiology that also affect behavior (e.g., weight). Secondary effects may occur as a result of social responses to the physical changes induced by increasing sex hormones. Puberty may affect behavior because youth see themselves differently, or because other social agents respond to them differently. For example, youth may request and be granted adult privileges along with the expectations to behave in mature ways. Puberty is not a single process, but, as noted in Section 2.1, consists of changes in gonadal, adrenal, and growth hormone axes that, singly and in combination with each other, and with other factors, produce physical changes. Thus, different secondary sex characteristics develop on different timetables, in terms of onset, tempo, and time to completion (Tanner, 1978). It seems likely that different aspects of adolescent psychological development are influenced by different aspects of puberty; for example, some hormones influence some behaviors but not others, social influences act on some aspects of development more than others, and some behaviors are influenced by early pubertal changes, whereas others change only when pubertal development is advanced. This idea is implicit in much of the work linking puberty to adolescent development, and it has occasionally been explicit in writings on puberty and adolescent development (e.g., Dorn et al., 2006; Mendle, 2014). But issues of measurement and sampling have

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resulted in few direct empirical investigations of this idea. This is discussed further and illustrated in Sections 3 and 4.

3. EVIDENCE FOR PUBERTAL INFLUENCES ON ADOLESCENT DEVELOPMENT In this section, we briefly summarize work on pubertal influences on adolescent development, highlighting the themes, findings, and questions that await further research. This is not an exhaustive review, but rather an illustration of the type of work being done, with representative findings and challenges. Research on links between puberty and adolescent psychological development has a long history (for reviews, see Dorn et al., 2006; Susman & Dorn, 2009), but until recently it has not been a prominent focus of mainstream developmental research, even adolescent research. For example, chapters in the 2009 edition of the Handbook of Adolescent Psychology have minimal reference to puberty in either of the two volumes (Lerner & Steinberg, 2009). In the volume on individual bases of adolescent development, aside from a chapter focused on the topic (Susman & Dorn, 2009), puberty is mentioned in only 3 of 20 chapters (on gender development, internalizing disorders, and substance use). In the volume on contextual influences on adolescent development, puberty is mentioned (in the index) on only 3 pages out of more than 650 pages. Given the prominent and visible role of pubertal changes and that adolescence is primarily devoted to attaining reproductive maturity, it is surprising that those changes have not received more scientific attention for their importance to adolescent psychological development. Fortunately, this situation is changing, with the goal of most current work to understand variations in normative development, especially those that might confer risk (e.g., Engle, 2013; Forbes et al., 2010; Klapwijk et al., 2013; Smith, Chein, & Steinberg, 2013), including for the aspects of psychopathology that emerge in adolescence (e.g., Graber, 2013; Klump, 2013; Trotman et al., 2013). Several perspectives drive most of the research (past and present) relating pubertal processes to psychological development in adolescence: (a) effects of pubertal timing on concurrent psychological problems, and, in cases, on long-term psychological function; (b) effects of pubertal status on concurrent behavior and the neural substrates that subserve behavior; and (c) effects of increasing sex hormones on the organization of the brain


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and subsequent behavior. Most studies originate in one of these perspectives, but the data they generate can often be used to answer questions from another perspective.

3.1. The Role of Pubertal Timing in Behavior A prominent theme in work linking puberty to behavior concerns psychological consequences of variations in the timing of pubertal development, particularly elevated risks of depression and externalizing behavior problems in girls who mature early (e.g., delinquency, substance use, early sexual activity), and depression and adjustment problems in boys who mature early or late (for reviews, see Ge & Natsuaki, 2009; Graber, 2013; Negriff & Susman, 2011). Most studies indicate that adverse consequences of early pubertal timing in girls is limited to adolescence, except perhaps for increased depression in the long term (Copeland, Shanahan, Miller, Costello, & Angold, 2010; Graber, Seeley, Brooks-Gunn, & Lewinsohn, 2004); consequences of late timing in boys may be long lasting (Graber, 2013). Consider a recent study that illustrates work in this area; it focused on early puberty and peer influences on externalizing behaviors across adolescence in a large sample of girls (Mrug et al., 2014). Early puberty was associated with increases in delinquency and aggression at the initial assessment (age 11), and continued to be associated with delinquency at other assessments (ages 13 and 16 years). Early-maturing girls appeared to be especially sensitive to negative influences from peers. These results are consistent with others that suggest that early puberty puts girls at risk for problem behaviors through associations with deviant peers. The study is also similar to others that use self-report measures of peers and behavior problems and age at menarche to index pubertal development, and that restrict outcome assessments to adolescence (so it is unclear if problems persist into adulthood). The study is noteworthy, however, for the large sample and repeated assessments, and for publication in a prominent medical journal, suggesting that pediatricians are becoming aware of the mental health effects of pubertal development. As illustrated by the study of Mrug and colleagues, links between timing and psychological problems have generally been attributed to social influences at the time of puberty (reviewed in Negriff & Susman, 2011; Susman & Dorn, 2009). For example, early maturation has been seen to have particular consequence when parental monitoring was low (LynneLandsman, Graber, & Andrews, 2010; Westling, Andrews, Hampson, &

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Peterson, 2008), or girls were in coeducational (vs. all girls’) schools (Caspi, Lynam, Moffitt, & Silva, 1993); problems may be mediated by association with deviant peers (Marklein, Negriff, & Dorn, 2009; Westling et al., 2008). But there is some suggestion that early puberty reflects an accentuation or manifestation of problems occurring in childhood (Caspi & Moffitt, 1991; Mensah et al., 2013); this should be considered in light of findings that puberty itself appears to be accelerated under conditions of environmental adversity, particularly father absence or other aspects of the family environment (Belsky, Steinberg, & Draper, 1991; Ellis, 2004; Webster, Graber, Gesselman, Crosier, & Schember, 2014). There is also recent interest in the possibility that pubertal timing effects reflect hormonal influences on brain organization and subsequent behavior, as discussed in Section 4.3 (Berenbaum & Beltz, 2011; Schulz, Molenda-Figueira, & Sisk, 2009).

3.2. The Role of Pubertal Status in Behavior Another theme concerns psychological changes in adolescence brought on by changes in pubertal status, as a reflection of direct effects of increased sex hormones on the brain and indirect effects of social responses to physical changes produced by those hormones. Much of the contemporary work on this topic is focused on direct effects of the midpubertal surge of sex hormones (measured or inferred) on the neural substrates of risk-taking/risky decisions, stress reactivity, and social reorientation. These functions all relate to the changing roles of adolescence and contribute to both normal adaptation and to the increased incidence of problem behaviors seen at this period of development. The psychological changes have been attributed to brain development in adolescence, particularly changes in prefrontal cortex and limbic brain regions and in the relation between them, with a prominent role hypothesized for dopamine systems (Luciana, 2013; Spear, 2000). Different neural subsystems are suggested to subserve different aspects of psychological development, and both individual and contextual factors contribute to and modify the expression of the neural changes. 3.2.1 Risky Decisions Risk taking shows normative increases in adolescence, more so in boys than in girls, and is suggested to lead to externalizing problems, such as substance use, in some youth. Much has been written about the ways that risk-taking results from sex hormones and social experiences acting on the developing brain, particularly the differential development of subcortical and cortical areas of the brain. According to this “dual-systems” perspective, risk-taking


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results from the asynchronous development of appetitive behaviors subserved by subcortical systems and hypothesized to be under the control of gonadal hormones, and cognitive or impulse control subserved by prefrontal cortex and hypothesized to be hormone-independent (e.g., Geier, 2013; Klapwijk et al., 2013; Somerville, Jones, & Casey, 2010; Steinberg, 2010; Steinberg et al., 2008). In essence, risk-taking is hypothesized to persist until structural and functional neural connections between the (para)limbic subcortical structures and prefrontal cortex are fully developed (in early to middle adulthood) (Steinberg, 2008). The reward system may provide a mechanism whereby risk-taking leads to problem behaviors; for example, substances such as nicotine serve as reinforcers, with the potential to hijack the reward system and lead to lifelong substance dependence (Lydon et al., 2014). Adolescence is a likely period of vulnerability because the reward system is changing. The dual-systems approach has been frequently discussed and used to explain adolescent development but has not been well-tested; even rarer are studies that explicitly examine the role of puberty in developmental changes. The available evidence is limited to risk-taking and most is indirect (e.g., from cross-sectional studies using magnetic resonance imaging, especially functional MRI, fMRI, Forbes et al., 2010; Op de Macks et al., 2011). Consider one example: a well-designed and executed study of youth aged 10–17 years focused on the link between reward-related brain activation during a gambling task and pubertal status, indexed by salivary testosterone (Op de Macks et al., 2011). Results showed a positive association (stronger in boys than in girls) between testosterone and activity in the ventral striatum (a subcortical structure considered part of the reward system) when participants won—compared to when they lost—money. The study is noteworthy because age was statistically controlled, enabling explicit examination of pubertal status, and testosterone was assessed with multiple samples (with values highly correlated across samples) to control for error and natural variations. Nonetheless, interpretation is still limited because results were not reported on other measures of pubertal status used (estradiol, and self-reports with the PDS and pictures of Tanner stages), and there were not the expected sex differences in the gambling task or in pubertal development. A recent longitudinal study using structural MRI failed to find evidence for a link between changes in subcortical and prefrontal brain volumes and changes in risk-taking behavior, raising some questions about the hypothesized brain changes as cause of risky decisions (Mills, Goddings, Clasen, Giedd, & Blakemore, 2014). Caution is necessary, however, before

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eliminating the dual-systems explanation because the sample was small, risktaking was measured retrospectively, and there was no consideration of subcortical–prefrontal connectivity. Refinements, modifications, and challenges to the dual-systems approach have emphasized the complexity of the neural and psychological processes involved in adolescent risky decision making (e.g., Bjork, LynneLandsman, Sirocco, & Boyce, 2012). Social context plays a role; for example, the presence of peers may act to sensitize the reward system and instigate a youth’s risk-taking behavior, including reckless driving (reviewed in Albert, Chein, & Steinberg, 2013). Methodological complexities include the reward task used (Richards, Plate, & Ernst, 2013) and adolescents’ sleep quality and quantity (Holm et al., 2009). There may be a difference between ability and use; youth are able to exert cognitive control but do not always do so, perhaps, for example, because of multiple demands on the system (Luciana & Collins, 2012). Individual and developmental differences in dopaminergic function may also be important (Urosˇevic´, Collins, Muetzel, Lim, & Luciana, 2012). Furthermore, it has been argued that hypotheses regarding the relation between prefrontal and subcortical systems have been invoked to explain other phenomena (so perhaps they are overused), including differences between typically developing and atypical populations (such as depression), and that they do not do justice to the complexities of development (Pfeifer & Allen, 2012). 3.2.2 Social Reorientation A primary task of adolescence involves the formation of new social relationships, including romantic and sexual relationships that come with reproductive maturity. An expanding body of research is focused on the ways that changes in the brain support the skills needed for this “social reorientation,” including changes in face recognition and attribution of mental states (Blakemore, 2008) and processing of social information (Nelson, Leibenluft, McClure, & Pine, 2005). Difficulties in social reorientation are suggested to contribute to mood and anxiety disorders that increase in incidence in adolescence (Nelson et al., 2005). There is little empirical work on the role of puberty in the social reorientation of adolescence although the issue has been considered in several reviews (Blakemore, 2008; Crone & Dahl, 2012; Forbes et al., 2010; Nelson et al., 2005), and there is empirical work on the related topic of neural substrates of social information processing in adolescence using fMRI (e.g., Jones et al., 2011; Masten et al., 2009). Furthermore, evidence in nonhuman primates supports the importance of pubertal hormones in social


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reorientation (Forbes et al., 2010), and the dual-systems approach does not adequately address the changes in social and affective processing that occur early in puberty (Crone & Dahl, 2012). There is one informative study (Goddings, Heyes, Bird, Viner, & Blakemore, 2012) on the link between pubertal status and the neural substrates of social processing, studied in midadolescent girls varying in pubertal status (indexed by hormone levels, physician-assessed Tanner stage, and age at menarche). There was a significant positive correlation between hormones (both testosterone and estradiol) and activity in the left anterior temporal cortex (a brain region implicated in mentalizing) during the processing of social (e.g., embarrassment) versus basic (e.g., disgust) emotions, with appropriate statistical control for age. Although these results support an effect of pubertal status on brain activation during social information processing, it is unclear why results were specific to hormone levels and did not extend to Tanner stage and age at menarche; differences may reflect the way that the data were analyzed. 3.2.3 Stress Responsivity Changes in the hypothalamic–pituitary–adrenal (HPA) axis in adolescence are thought to trigger changes in stress and emotional reactivity, contributing to psychopathology, including psychosis, in vulnerable youth (e.g., Doom & Gunnar, 2013; Romeo, 2010; Spear, 2009; Trotman et al., 2013; Walker, Sabuwalla, & Huot, 2004). Furthermore, stressful experiences themselves may change HPA responsiveness in a unique and permanent way in adolescence (Romeo, 2010). Empirical work is beginning to focus on changes in the HPA system in adolescence and links between different components of the system and different aspects of puberty. Consider two exemplar studies. In both studies, youth’s pubertal status was determined from self-reported PDS scores, although with different cutpoints to classify children as pre/early pubertal or mid-/late pubertal (PDS of 2.5 or 2.8, on a 1–4 scale). The first study examined puberty effects on affective behavior and on the psychophysiology of defensive and appetitive motivation (Quevedo, Benning, Gunnar, & Dahl, 2009). Pubertal status related to both eye-blink startle response and the auditory motor (postauricular, PA) reflex in different ways. Startle response to all affective pictures (regardless of valence) was increased in youth who were mid-/late pubertal compared to those who were pre-/early pubertal. PA responses depended on both pubertal status and stimulus valence. Furthermore, pubertal status moderated the association between psychophysiological and behavioral measures, leading the

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authors to suggest that puberty acts to reorganize defensive and appetitive motivational systems. The second study (Silk et al., 2009) examined pupillary and behavioral responses to an emotional word task. Pubertal status was related to pupillary reactivity; for example, youth who were mid-/late pubertal showed greater peak pupillary reactivity than pre-/early children, regardless of valence of the words. These studies showed that puberty influences basic reflexes related to processing of emotional materials, but that effects are not simply related to negative effect; effects are complex and not necessarily consistent across measures. In both studies, effects of pubertal status were found after age was considered. This is certainly a topic worth further study.

3.3. Brain Development in Adolescence Adolescence is characterized by changes to brain anatomy, with indications that some changes are specifically related to puberty; most evidence comes from neuroimaging of typical adolescents, using structural MRI. There are changes in the relative proportion of gray matter (containing cell bodies) and white matter (fiber tracts), with decreases in the amount of gray matter and increases in the amount of white matter (Lenroot & Giedd, 2006); these changes occur approximately 2 years earlier in girls than in boys, consistent with the sex difference in puberty (Lenroot et al., 2007). The developmental changes in cortical surface are illustrated in Figure 2. In general, the changes are associated with increases in sex-specific pubertal hormones, with effects most robust when age is experimentally or statistically controlled (Herting et al., 2014; reviewed in Peper, Hulshoff Pol, Crone, & van Honk, 2011). More powerful evidence comes from longitudinal studies, which have shown that, for both boys and girls, pubertal development (assessed with Tanner stage or indexed by testosterone and estradiol) is related to volumetric changes in subcortical brain regions (e.g., amygdala, hippocampus, caudate); the direction of effect and influence of age were seen to depend on sex and brain region (Goddings et al., 2014; Herting et al., 2014). Although these findings confirm that pubertal processes are involved in brain development, many questions remain. First, little is known about the mechanisms responsible for the changes, particularly whether they reflect brain reorganization brought about by pubertal hormones (see below) and the extent to which they depend on the social environment. Second, it is unclear what specific neural processes are changing. It is assumed that changes represent cell death and synaptic pruning, but there is little direct

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Figure 2 Schematic representation of the development of gray matter over the surface of the cortex. Views are right lateral and top. The bar is a color (different gray shades) representation of gray matter volume. Data represent 52 scans from 13 participants each scanned four times at approximately 2-year intervals. Reprinted from Lenroot, R. K., & Giedd, J. G. (2006). Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neuroscience and Biobehavioral Reviews, 30, 718–729, with permission from Elsevier.

evidence to show this. Third, the implications of anatomical changes for brain function and behavior in adolescence have hardly been studied, that is, little is known about how these changes in brain structure affect the psychological changes of adolescence (Casey, Tottenham, Liston, & Durston, 2005; Giedd, 2004). For example, changes in the brain might be directly responsible for the psychological changes or both sets of changes might simply co-occur as the result of a third factor (such as chronological age or pubertal hormones).

4. ASSUMPTIONS, STRENGTHS, AND LIMITATIONS OF WORK ON PUBERTY–BEHAVIOR LINKS In this section, we consider the assumptions that underlie most of the research literature we just summarized, articulate the different ways in which puberty is thought to affect behavior, and consider how previous research

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has been successful (or not) in testing links between puberty and behavior. In doing so, we also consider methodological improvements that could enhance our ability to study the paths from puberty to behavior.

4.1. The Nature of Adolescent Change Before we consider how puberty influences psychological change in adolescence, we need to consider the nature of that change. Change could be continuous, with adolescence representing a developmental period between childhood and adulthood. Continuous change is generally considered to be linear, but could reasonably be nonlinear, with adolescent function equivalent to adult function (so that adolescence involves the attainment of adult function). Adolescent change might also be qualitative, with psychological function differing in form from both childhood and adulthood. Surprisingly, few studies include a specific articulation of the nature of change hypothesized, or a design that enables differentiation of the different types of change. For example, if adolescence is a sensitive period for the effects of hormones and experiences, then adolescents may well differ from both children and adults, so a complete study should involve participants in all three age groups, and analyses should not assume linear change with age. Furthermore, studies must go beyond comparisons by age or pubertal status and apply a true developmental perspective, by considering the nature of change over time and the processes contributing to change (see also Doom & Gunnar, 2013).

4.2. Understanding Links Between Pubertal Status and Adolescent Psychological Change Most contemporary work linking puberty to behavior is focused on describing pubertally influenced brain changes that produce normative changes in behaviors thought to contribute to psychological problems in vulnerable individuals. Although the complete picture is far from clear, studies (cited earlier) have confirmed links along the path: pubertal status to brain structure and activation, brain structure and activation to behavior in adolescents, age effects on risk-taking, processing of social information (such as faces) and stress sensitivity, and links between stress sensitivity and processing of emotional material. But what does it mean for pubertal status to mark important neural and psychological change? Investigators rarely specify what it is about puberty that is important, although, in most studies, the designs used provide some information. Some studies involve the comparison of youth who have versus


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have not reached a particular pubertal stage, typically midpuberty measured by self-report; the underlying assumption is probably that the hormonal surge at midpuberty has prompted brain or behavioral changes. Other studies use direct measures of hormones in an attempt to attribute brain changes to increased hormones. But hormones do not always link directly to behavior, as discussed in the next section. Furthermore, the psychological effects of puberty might not be straightforward. If adolescence is a period of brain reorganization under the influence of pubertal hormones and social experiences, then there will not be simple links among brain, hormones, and behavior. This is illustrated in a study of pubertal effects on motivation, where puberty was seen to moderate the association between psychophysiological and behavioral measures, leading the authors to suggest “that it plays a role in reorganizing defensive and appetitive motivational systems” (Quevedo et al., 2009, p. 27). The situation is even more complex if different aspects of puberty play a role in reorganizing different systems or are differentially dependent on environmental input. This requires consideration of several issues, including: the meaning of pubertal status (e.g., whether the processes of interest reflect social mediation of physical changes at pubertal onset or hormonal changes at midpuberty); which feature best reflects the processes of interest (e.g., genitalia vs. height); whether pubertal status alone matters, or whether it is status in relation to chronological age (timing); and the importance of social changes occurring at that point in the youth’s development. From a true developmental perspective, we should probably not expect to find simple links between hormones and behavior.

4.3. Understanding the Neural and Psychological Role of Pubertal Hormones Even if brain and behavioral changes directly reflect hormonal changes, there are many ways that this can occur. In general, sex hormones affect behavior through two mechanisms. Organizational effects refer to permanent, long-lasting changes to the brain (and subsequent behavior) caused by hormones that are present during sensitive periods of brain development. Organizational effects have generally been considered to be restricted to early (especially prenatal) development, but recent work has suggested that hormones at puberty may also induce a reorganization of the brain and thus produce permanent changes (Berenbaum & Beltz, 2011; Schulz et al., 2009). Activational effects refer to temporary changes to the brain (and behavior) produced by circulating hormones at the time; hormones activate neural

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structures that were organized early in development. Activational effects depend on the immediate presence of the hormones, so adolescence and adulthood are periods for activational effects, when sex hormone levels are high. Activational effects mean that the hormones must be present in order to affect the brain and behavior, whereas organizational effects mean that the hormones need to be present only during a sensitive period of development. The main distinctions between organizational and activational effects concern timing and permanence, although these distinctions are not absolute (Arnold & Breedlove, 1985). Organizational effects of early hormones are generally studied with natural experiments (in which there is a separation of early hormones and rearing) but are increasingly studied in typical samples by relating hormone levels during prenatal development (e.g., in amniotic fluid) to later behavior. Activational effects are generally studied in typical samples at varying points in the reproductive cycle (e.g., across the menstrual cycle, postmenopausal versus premenopausal status) or by relating typical variations in hormones to variations in behavior. (See Blakemore, Berenbaum, & Liben, 2009 for details about types of hormone effects on behavior.) Thus, pubertal hormones might affect behavior in two ways. They might have immediate and temporary effects on the brain and thus behavior, in which case behavior should remain at a high level even after puberty because gonadal hormones remain high at least through midadulthood. Alternatively, hormones might reorganize a brain system (as they do during prenatal development), in which case hormones would be required to initiate, induce, or increase the behavior, but not to maintain the behavior. In the following sections, we consider existing studies in light of the mechanisms whereby pubertal hormones might affect behavior; in the next section, we suggest the type of work that might bring us closer to understanding how pubertal hormones affect behavior. 4.3.1 The Meaning of Associations Between Hormones and Behavior Empirical studies of links between hormones and behavior in adolescence are generally agnostic with respect to mechanism. At a simple level, a study linking testosterone to behavior in adolescence is an empirical test of activational influences (e.g., when the hormone is present in high levels, behavioral scores are high). In such cases, it does not matter whether the study sample consists of adolescents or adults. But many studies of adolescents use hormones as an index of pubertal status, as discussed earlier. The only way to ensure that hormones are a primary reflection of pubertal status is to measure them repeatedly from prepuberty through complete


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development in order to control for the other factors reflected in hormone levels (as noted in Section 2.2). For example, a 16-year-old boy who has high salivary testosterone relative to his peers might be in the late stages of puberty or he might have high testosterone for genetic reasons or recently engaged in behavior that increases testosterone (such as sexual activity or sports); the effect of his high testosterone will also be modified by his androgen receptor sensitivity. Furthermore, hormone–behavior links often depend on context. This is illustrated in two findings from a longitudinal study of gender development from ages 7 to 19 years. First, links between testosterone and behavior problems in boys depended on the quality of parent–child relationships, with testosterone associated positively with risk-taking and negatively with depression when the quality of these relationships was low (Booth, Johnson, Granger, Crouter, & McHale, 2003). Second, parents’ influences on gendered personal qualities depended not just on the absolute levels of boys’ testosterone, but on the change in those levels, with parents influencing expressivity and instrumentality only in boys whose testosterone rose slowly over that time period, and not in those with rapid hormone increases (McHale, Kim, Dotterer, Crouter, & Booth, 2009). Thus, it seems likely that hormones have complex links to behavior, and their utility as markers of pubertal status may vary with factors yet unknown. For example, in one study (Op de Macks et al., 2011), testosterone was correlated with self-reported status (PDS score), highly in boys and moderately in girls; but in another study (Goddings et al., 2012), neither testosterone nor estradiol were associated with pubertal status (although it is unclear how much this reflects measurement issues). This topic represents another exciting research opportunity. 4.3.2 Hormones as Modifiers of Gene Expression Puberty may have psychological significance because sex hormones “turn on” specific genes, and this would account in part for behavioral changes in adolescence. Evidence suggests that ovarian hormone changes in adolescence are partially responsible for the sex differences in disordered eating and depression that emerge in adolescence. A series of studies on disordered eating by Klump and colleagues shows the way to approach this question and provides evidence that the increase in disordered eating seen in girls in adolescence reflects genes activated by estradiol at midpuberty (Klump, 2013). For example, in girls, disordered eating appears to be strongly influenced by genes (is moderately heritable) at midpuberty and beyond, but not at all influenced by genes (shows low

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heritability) before puberty (Culbert, Burt, McGue, Iacono, & Klump, 2009); in boys, disordered eating was moderately heritable regardless of pubertal status (Klump et al., 2012). Direct measures of hormones were included in one study (Klump et al., 2010); otherwise, hormones were inferred from physical indicators. The increased depression seen in girls at midpuberty has often been attributed to social expectations due to development of secondary sex characteristics (Nolen-Hoeksema & Hilt, 2009). But a study examining both physical development (Tanner stages) and hormone levels showed that effects of physical changes are due to sex hormones themselves (Angold, Costello, Erkanli, & Worthman, 1999). This study also shows a benefit of including direct measures of hormones in disentangling explanations for behavioral change in adolescence. It is unclear from available data whether these hormone–behavior links reflect activational or organizational effects, that is, whether high hormone levels are necessary for continued gene expression, or hormones trigger the expression of specific genes and those genes remain turned on even if the hormones are no longer present at high levels—or perhaps activational effects on some aspects of the behavior and organizational effects on other aspects, or some other combination of the two kinds of effects. With respect to disordered eating, it appears that activational hormones play some role (e.g., emotional eating was influenced by ovarian hormones across the menstrual cycle, Klump et al., 2013). 4.3.3 Pubertal Hormones as Reorganizers of the Brain An important question is whether sex hormones at puberty produce permanent changes to the brain, reorganizing it in ways that facilitate tasks of adulthood. In this case, behavior change would be maintained even if the hormones were no longer present. Work of Sisk and her colleagues in rodents has made clear that the brain remains sensitive to the effects of sex hormones into adolescence, undergoing structural changes as a result of hormone exposure (Schulz et al., 2009; Sisk & Zehr, 2005). They have suggested two models for organizational effects, as shown in Figure 3: the first includes two separate periods during which the brain is sensitive to sex hormones, the well-documented period in early development and a hypothesized one in adolescence; the second model involves a continuum of sensitivity, with sensitivity peaking during early development, declining throughout the juvenile and adolescent periods, and ending sometime in late adolescence or early adulthood. Evidence from several labs suggests that rodent behavior is indeed modified in permanent ways by sex hormones

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Figure 3 Proposals from Sisk and colleagues about multiple sensitive periods for organizational effects of sex hormones, based on work in Syrian hamsters. (Top) Two-stage model for steroid-dependent organization of behavior. Testosterone secretions during the perinatal and adolescent periods organize adult mating behavior. The dashed line approximates testosterone secretions across development, and the shading denotes the approximate timing of perinatal and adolescent development in the Syrian hamster. The question mark highlights that less is known about testosterone-dependent behavioral organization in the time between the perinatal and adolescent periods. (Bottom) Model of declining sensitivity for organization effects of testosterone. Illustration based on evidence of effects of early, on time, and late adolescent testosterone treatments on adult mating behavior. The data suggest that adolescence is part of a protracted sensitive period for the organizing actions of testosterone (area under the solid gray curve), and that sensitivity to the organizing actions of testosterone decreases across postnatal development. The dashed line approximates testosterone secretions across development, whereas the solid line depicts decreasing sensitivity to the organizing actions of testosterone across development. Shading approximates the timing of perinatal, prepubertal, adolescent, and adult periods in the Syrian hamster. Reprinted from Schulz, K. M., Molenda-Figueira, H. A., & Sisk, C. L. (2009). Back to the future: The organizational-activational hypothesis adapted to puberty and adolescence. Hormones and Behavior, 55(5), 597–604, with permission from Elsevier.

present during adolescence, with some effects of variations in pubertal timing (for details, see Juraska, Sisk, & DonCarlos, 2013; Schulz et al., 2009; Sisk & Zehr, 2005).

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Several aspects of these effects are particularly relevant for human development ( Juraska et al., 2013; Schulz et al., 2009; Sisk & Zehr, 2005). The brain organization that occurs in puberty is an elaboration and refinement of neural circuits initially established by sex hormones early in development. There is some evidence for an active feminizing effect of ovarian hormones on female-typical behavior during puberty, in contrast to the primary role of androgens during the prenatal period. A continuum of sensitivity highlights a role for variations in pubertal timing so that the presence of high levels of sex hormones at different points in development have consequences for brain organization and subsequent behavior, perhaps requiring a reinterpretation of some of the findings on behavioral problems associated with off-time puberty discussed earlier. Social experiences may partially compensate for hormone deficiencies at puberty. Unfortunately, there have been no direct tests of the hypothesis that hormones act to reorganize the human brain and behavior at puberty because it is difficult to separate hormonal from social changes at puberty (for a discussion of possible study designs, see Berenbaum & Beltz, 2011). There is some indirect evidence for a continuum of sensitivity for cognition: mental rotation ability in young men was found to be inversely related to their pubertal timing (reported retrospectively), consistent with early exposure to testosterone enhancing sex-typed abilities (Beltz & Berenbaum, 2013). Other indirect and suggestive evidence for puberty as another organizational period comes from children with gender dysphoria, most of whom desist in adolescence (Drummond, Bradley, Peterson-Badali, & Zucker, 2008; Wallien & Cohen-Kettenis, 2008) and the increase in female-preponderant disordered eating and depression at midpuberty described previously. But all of these studies are open to alternative explanations and do not provide compelling evidence that any hormonal effects are permanent. Such evidence is most likely to come from natural experiments, including youth with disordered pubertal development who are treated with sex hormones at varying ages either to delay puberty (e.g., girls with central precocious puberty) or to initiate puberty (e.g., boys with delayed puberty, girls with Turner syndrome) (see discussion in Berenbaum & Beltz, 2011).

4.4. Methodological Issues Throughout the chapter, we have noted methodological issues associated with specific questions. In this section, we summarize those concerns and suggest some solutions and opportunities for future work.


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4.4.1 Articulating the Nature and Meaning of the Measure Used We cannot overemphasize the importance of articulating clearly what our pubertal measures tell us from both measurement and conceptual perspectives. For example, one of the most widely used measures, age at menarche, reflects a period late in puberty and is not a social signal, so links between menarche and behavior cannot be interpreted in terms of social responses to a newly developing body. Although this point has been made before (e.g., Dorn et al., 2006), it bears repeating. It is essential that studies of pubertal influences on behavior clearly articulate the processes that are being measured and why they matter. Relatedly, apparently small decisions might matter. For example, we noted earlier that two studies on stress responsivity in relation to pubertal status used slightly different cutpoints to define midpuberty (Quevedo et al., 2009; Silk et al., 2009), but the effects of those decisions are unknown. Work on pubertal effects on disordered eating suggests that criteria for defining pubertal status affect results (Culbert et al., 2009). 4.4.2 Differentiating Pubertal Timing from Pubertal Status When youth are in the midst of puberty, it may not be possible to differentiate pubertal timing from pubertal status. For example, a 12-year-old girl who has not yet menstruated might mature on time or late, although she did not mature early by most definitions; a 16-year-old girl who has completed puberty might have been early or on time; an 11-year-old boy who is prepubertal might be early, on time, or late. This creates problems for crosssectional studies of adolescents who vary in age from the early to the late teenage years, as is often the case. Thus, many studies of pubertal status might also be inadvertent studies of pubertal timing for some children, and many studies of pubertal timing are studies of pubertal status for children who have not yet completed development. 4.4.3 Differentiating Age from Pubertal Status Because puberty is associated with chronological age, it is essential to design studies and analyze data to separate effects of age and pubertal status. This can be done in several ways: selecting children homogeneous in age but differing in pubertal status (as would be found, for example, in a sample of 12- to 13-year-old girls); selecting children homogeneous in pubertal status but differing in age; or analyzing effects of pubertal status after statistically controlling for age. It is surprising how many studies have failed to consider age effects, perhaps because age is highly correlated with pubertal status,

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reducing likelihood of seeing effects of the latter. But such studies do not provide meaningful information about pubertal influences on development. 4.4.4 Benefits and Drawbacks of Modeling Advanced modeling techniques applied to longitudinal pubertal data have provided some important insights about pubertal development (see Beltz et al., 2014; Castellanos-Ryan et al., 2013; Corley et al., 2014; Eaves et al., 2004; Huang et al., 2009; Marceau et al., 2011; Mendle et al., 2010). First, puberty is better described by a logistic, rather than a linear model, consistent with expectations based on the physiological changes occurring at puberty. Second, estimates of timing of midpuberty from linear and logistic models are highly correlated with each other and with age at menarche. Third, commonalities among estimates of midpubertal timing are due to shared genes. Fourth, all timing estimates are similarly correlated with behavior. Fifth, estimates of tempo vary widely across methods of estimating it, due to different assumptions about tempo; the varying conceptualizations of tempo likely account for discrepant findings across studies. Sixth, it is difficult to separate timing of gonadarche and adrenarche using measures of physical features; thus, investigation of the relative psychological importance of adrenal and gonadal processes requires measures beyond physical exam or self-report. Advanced methods are appealing because they can describe the complex nature of development and address some methodological problems inherent in longitudinal studies (e.g., missing data, self-reports that do not increase monotonically with age); they also provide good estimates of timing in boys who are often excluded from study because there is not a simple parallel measure to menarche in girls. But studies using these models suggest that simple estimates of timing are appropriate in some situations. Furthermore, current models have not enhanced our ability to measure tempo (and, in fact, different models produce hugely different estimates of tempo), to differentiate aspects and processes of puberty and the synchrony of their development, or to capture different points in development (estimates of timing of different stages can be completely correlated with each other).

5. CONCLUSIONS AND FUTURE DIRECTIONS It is an exciting time to study pubertal influences on psychological development. The topic is of interest to scientists, funding agencies, policy makers, and the general public. Methodological and conceptual advances


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have created new opportunities, along with challenges that should generate new research and new discoveries. In this section, we summarize what is known and what remains to be understood, and provide suggestions for future work.

5.1. Summary of Findings on Pubertal Influences on Psychological Development Both pubertal timing and pubertal status affect development in adolescence. Pubertal timing that is not typical carries risk for behavior problems, especially in girls who are early maturers and boys who are either early or late maturers. More is known about immediate effects (i.e., during the period that the youth is off-time from his/her peers) than about long-term effects and the mechanisms that might mediate them. Mechanisms for immediate effects appear to involve the social environment, particularly deviant peers. Nevertheless, there are opportunities to follow up suggestive evidence that off-time puberty might change behavior through changes to the organization of the brain. An interesting question concerns the extent to which pubertal timing reflects a unique contributor to development or an elaboration of early processes (social and biological). Pubertal status has been linked to several aspects of behavior, physiology, and brain structure and activity, and in some cases, this has been shown to be independent of chronological age. Work is beginning to link the changes (e.g., brain to behavior), but much more is needed on that topic and on the mechanisms underlying these effects, especially the extent to which puberty represents a period of brain reorganization, and how change at puberty depends on sex hormones and the environment (both social and physical).

5.2. Mechanisms and Methods A key question concerns the meaning of links between hormones and behavior in adolescence. As noted earlier, hormones might serve to activate neural structures that were established early in life, with variations in those hormones linked to variations in behavior in linear or nonlinear ways (for the latter, e.g., the hormones might need to be present above some threshold level in order for the behavior to occur). In such situations, the links between hormones and behavior in puberty should be similar to those in adulthood, with the only potential difference reflecting absolute levels of hormones (higher in adults than in midpubertal adolescents). Alternatively,

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hormones might act to reorganize the brain (and associated behavior), so hormones need to be present at the appropriate levels only during key sensitive periods, with long-term consequences if they are not. We encourage increasing attention to—and articulation of—the mechanisms responsible for empirical findings, which can be tested with particular types of studies. Advances in understanding mechanisms linking puberty to behavior require that we move beyond cross-sectional studies of typical adolescents who vary in age and pubertal status. Thus, some mechanisms (e.g., psychological effects of activational hormones) can be tested in relation to natural variations; variations occur in women, for example, with the menstrual cycle, and menopause; variations in men are not as widely appreciated but occur, for example, across the day and seasons. Other mechanisms (e.g., psychological effects of organizational hormones) may be difficult to test and may rely more on natural experiments, such as youth with precocious or delayed puberty; such individuals are particularly valuable in testing whether there is a continuum of sensitivity to hormones in adolescence, because there is variability across youth in the age at which the condition appears and the age at which hormone treatment is initiated (described in Berenbaum & Beltz, 2011). It is tempting to measure hormones but there is no reason to do so unless the results can be interpreted clearly. On the one hand, little is gained from a single sample of hormones in a cross-sectional study. On the other hand, hormones assessed across time in a sample of developing youth provide an excellent opportunity for tracking psychological change in relation to change in pubertal status and differentiating effects of status from timing. And careful measurement of hormones in the right context provide an opportunity to determine cause, such as differentiating direct effects of hormones from social responses to a youth’s changing body (e.g., Angold et al., 1999). It is also important to remember that hormones might be associated with behavior through social mechanisms. The physical changes of puberty serve as social signals for the youth and for others in the social environment, so that the hormonal changes of puberty are highly correlated with social changes at that time. The brain changes in response to experiences, so it is possible that correlations between hormonal and brain changes in adolescence reflect the third factor of social experiences. This type of mechanism is often ignored, perhaps because of inaccurate assumptions about the meaning of changes in brain structure. So we emphasize that the brain is not fixed but develops in response to both genetic processes and experiences (physical and social).


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5.3. Linking to Other Domains of Development Research linking puberty to adolescent development has increased in recent years, but it has remained confined to specific domains, particularly those related to disruptions in psychological health. Yet, adolescence is characterized by changes in other important domains, including peer and family relationships, cognition, morality, identity, and romantic relationships and sexuality. Surely, pubertal hormones and brain changes affect development in those domains too, beyond the disruption to parent–child relationships. Failure to consider the role of puberty in development in those domains likely has multiple causes, including lack of expertise, time pressure, and research focus. But a more troubling contributor is likely the tendency to see “biology” as separate from the social world that concerns most developmentalists who study adolescence. Some of the work on neural and hormonal contributors to adolescent development helps to perpetuate the false distinction, given the tendency to minimize effects of social context on brain development or hormones or to acknowledge the importance of social experiences in shaping the brain. Therefore, we emphasize that puberty occurs in youth who are embedded in the social world. Adolescence is likely a sensitive period because both hormones and social experiences are undergoing rapid change. We call for all developmentalists to pay serious attention to the ways that pubertal development affects psychological development through direct effects on the brain and through important indirect effects of the social environment. This work might be facilitated by collaborations between developmentalists with expertise in specific domains (e.g., cognition, morality, and identity) who are focused on socialization influences and researchers who study the behavioral importance of hormones and the brain, especially in adolescence. And we hope that this work will be seen in venues where adolescent research is presented and published.

REFERENCES Albert, D., Chein, J., & Steinberg, L. (2013). The teenage brain: Peer influences on adolescent decision making. Current Directions in Psychological Science, 22, 114–120. http://dx. doi.org/10.1177/0963721412471347. Angold, A., Costello, E. J., Erkanli, A., & Worthman, C. M. (1999). Pubertal changes in hormone levels and depression in girls. Psychological Medicine, 29, 1043–1053. Angold, A., Costello, E. J., & Worthman, C. M. (1998). Puberty and depression: The roles of age, pubertal status and pubertal timing. Psychological Medicine, 28(1), 51–61. http://dx. doi.org/10.1017/s003329179700593x. Arnold, A. P., & Breedlove, S. M. (1985). Organizational and activational effects of sex steroids on brain and behavior: A reanalysis. Hormones and Behavior, 19, 469–498.

86


Belsky, J., Steinberg, L., & Draper, P. (1991). Childhood experience, interpersonal development, and reproductive strategy: An evolutionary theory of socialization. Child Development, 62, 647–670. Beltz, A. M., & Berenbaum, S. A. (2013). Cognitive effects of variations in pubertal timing: Is puberty a period of brain organization for human sex-typed cognition? Hormones and Behavior, 63, 823–828. http://dx.doi.org/10.1016/j.yhbeh.2013.04.002. Beltz, A. M., Corley, R. P., Wadsworth, S. J., Bricker, J. B., & Berenbaum, S. A. (2014). Modeling pubertal timing and tempo and examining links to behavior problems. Developmental Psychology, 50, 2715–2726. Berenbaum, S. A., & Beltz, A. M. (2011). Sexual differentiation of human behavior: Effects of prenatal and pubertal organizational hormones. Frontiers in Neuroendocrinology, 32(2), 183–200. http://dx.doi.org/10.1016/j.yfrne.2011.03.001. Bernhardt, P. C., Dabbs, J. M., Fielden, J. A., & Lutter, C. D. (1998). Testosterone changes during vicarious experiences of winning and losing among fans at sporting events. Physiology & Behavior, 65, 59–62. Biro, F. M., & Dorn, L. D. (2005). Puberty and adolescent sexuality. Pediatric Annals, 34(10), 777–784. http://dx.doi.org/10.3928/0090-4481-20051001-09. Biro, F. M., Huang, B., Crawford, P. B., Lucky, A. W., Streigel-Moore, R., Barton, B. A., et al. (2006). Pubertal correlates in black and white girls. Journal of Pediatrics, 148(2), 234–240. http://dx.doi.org/10.1016/j.jpeds.2005.10.020. Bjork, J. M., Lynne-Landsman, S. D., Sirocco, K., & Boyce, C. A. (2012). Brain maturation and risky behavior: The promise and the challenges of neuroimaging-based accounts. Child Development Perspectives, 6, 385–391. Blakemore, S. J. (2008). The social brain in adolescence. Nature Reviews. Neuroscience, 9(4), 267–277. http://dx.doi.org/10.1038/Nrn2353. Blakemore, J. E. O., Berenbaum, S. A., & Liben, L. S. (2009). Gender development. New York: Psychology Press/Taylor & Francis. Booth, A., Johnson, D. R., Granger, D. A., Crouter, A. C., & McHale, S. M. (2003). Testosterone and child and adolescent adjustment: The moderating role of parent–child relationships. Developmental Psychology, 39(1), 85–98. Casey, B. J., Tottenham, N., Liston, C., & Durston, S. (2005). Imaging the developing brain: What have we learned about cognitive development? Trends in Cognitive Sciences, 9(3), 104–110. http://dx.doi.org/10.1016/j.tics.2005.01.011. Caspi, A., Lynam, D., Moffitt, T. E., & Silva, P. A. (1993). Unraveling girls’ delinquency: Biological, dispositional, and contextual contributions to adolescent misbehavior. Developmental Psychology, 29, 19–30. Caspi, A., & Moffitt, T. E. (1991). Individual differences are accentuated during periods of social change: The sample case of girls at puberty. Journal of Personality and Social Psychology, 61, 157–168. Castellanos-Ryan, N., Parent, S., Vitaro, F., Tremblay, R. E., & Seguin, J. R. (2013). Pubertal development, personality, and substance use: A 10-year longitudinal study from childhood to adolescence. Journal of Abnormal Psychology, 122(3), 782–796. http://dx.doi.org/ 10.1037/a0033133. Coleman, L., & Coleman, J. (2002). The measurement of puberty: A review. Journal of Adolescence, 25, 535–550. http://dx.doi.org/10.1006/jado.2002.0494. Copeland, W., Shanahan, L., Miller, S., Costello, E. J., & Angold, A. (2010). Outcomes of early pubertal timing in young women: A prospective population-based study. American Journal of Psychiatry, 167(10), 1218–1225. http://dx.doi.org/10.1176/appi. ajp.2010.09081190. Corley, R. P., Beltz, A. M., Wadsworth, S. J., & Berenbaum, S. A. (2014). Genetic influences on pubertal development and links to behavior problems. Behavior Genetics, under review.


87

Crone, E. A., & Dahl, R. E. (2012). Understanding adolescence as a period of social–affective engagement and goal flexibility. Nature Reviews. Neuroscience, 13, 636–650. http://dx.doi. org/10.1038/nrn3313. Culbert, K. M., Burt, S. A., McGue, M., Iacono, W. G., & Klump, K. L. (2009). Puberty and the genetic diathesis of disordered eating attitudes and behaviors. Journal of Abnormal Psychology, 118(4), 788–796. http://dx.doi.org/10.1037/a0017207. Doom, J. R., & Gunnar, M. R. (2013). Stress physiology and developmental psychopathology: Past, present, and future. Development and Psychopathology, 25, 1359–1373. http:// dx.doi.org/10.1017/S0954579413000667. Dorn, L. D., & Biro, F. (2010). Puberty and its measurement: A decade in review. Journal of Research on Adolescence, 21, 180–195. Dorn, L. D., Dahl, R. E., & Biro, F. (2006). Defining the boundaries of early adolescence: A user’s guide to assessing pubertal status and pubertal timing in research with adolescents. Applied Developmental Science, 10(1), 30–56. http://dx.doi.org/10.1207/ s1532480xads1001_3. Drummond, K. D., Bradley, S. J., Peterson-Badali, M., & Zucker, K. J. (2008). A follow-up study of girls with gender identity disorder. Developmental Psychology, 44(1), 34–45. Dubas, J. S., Graber, J. A., & Petersen, A. C. (1991). A longitudinal investigation of adolescents changing perceptions of pubertal timing. Developmental Psychology, 27(4), 580–586. http://dx.doi.org/10.1037/0012-1649.27.4.580. Eaves, L., Silberg, J., Foley, D., Bulik, C., Maes, H., Erkanli, A., et al. (2004). Genetic and environmental influences on the relative timing of pubertal change. Twin Research, 7(5), 471–481. http://dx.doi.org/10.1375/twin.7.5.471. Ellis, B. J. (2004). Timing of pubertal maturation in girls: An integrated life history approach. Psychological Bulletin, 130(6), 920–958. Engle, R. W. (2013). Introduction to special issue on the teenage brain. Current Directions in Psychological Science, 22, 79. Forbes, E. E., Ryan, N. D., Phillips, M. L., Manuck, S. B., Worthman, C. M., Moyles, D. L., et al. (2010). Healthy adolescents’ neural response to reward: Associations with puberty, positive affect, and depressive symptoms. Journal of the American Academy of Child and Adolescent Psychiatry, 49(2), 162–172. http://dx.doi.org/10.1016/j.jaac.2009.11.006. Ge, X., Conger, R. D., & Elder, G. H. (1996). Coming of age too early: Pubertal influences on girls’ vulnerability to psychological distress. Child Development, 67, 386–400. http:// dx.doi.org/10.2307/1131784. Ge, X., & Natsuaki, M. N. (2009). In search of explanations for early pubertal timing effects on developmental psychopathology. Current Directions in Psychological Science, 18(6), 327–331. http://dx.doi.org/10.1111/j.1467-8721.2009.01661.x. Geier, C. F. (2013). Adolescent cognitive control and reward processing: Implications for risk taking and substance use. Hormones and Behavior, 64(2), 333–342. http://dx.doi.org/ 10.1016/j.yhbeh.2013.02.008. Gettler, L. T., McDade, T. W., Feranil, A. B., & Kuzawa, C. W. (2011). Longitudinal evidence that fatherhood decreases testosterone in human males. Proceedings of the National Academy of Science of the United States of America, 108, 16194–16199. http://dx.doi.org/10. 1073/pnas.1105403108. Giedd, J. N. (2004). Structural magnetic resonance imaging of the adolescent brain. Annals of the New York Academy of Sciences, 1021, 77–85. http://dx.doi.org/10.1196/annals.1308.009. Goddings, A.-L., Heyes, S. B., Bird, G., Viner, R. M., & Blakemore, S. J. (2012). The relationship between puberty and social emotion processing. Developmental Science, 15, 801–811. http://dx.doi.org/10.1111/j.1467-7687.2012.01174.x. Goddings, A.-L., Mills, K. L., Clasen, L. S., Giedd, J. N., Viner, R. M., & Blakemore, S. J. (2014). The influence of puberty on subcortical brain development. NeuroImage, 88, 243–251.

88


Graber, J. A. (2013). Pubertal timing and the development of psychopathology in adolescence and beyond. Hormones and Behavior, 64(2), 262–269. http://dx.doi.org/ 10.1016/j.yhbeh.2013.04.003. Graber, J. A., Seeley, J. R., Brooks-Gunn, J., & Lewinsohn, P. M. (2004). Is pubertal timing associated with psychopathology in young adulthood? Journal of the American Academy of Child and Adolescent Psychiatry, 43(6), 718–726. Handelsman, D. J., & Wartofsky, L. (2013). Requirement for mass spectrometry sex steroid assays. Journal of Clinical Endocrinology and Metabolism, 98(10), 3971–3973. http://dx.doi. org/10.1210/jc.2013-3375. Herting, M. M., Gautam, P., Spielberg, J. M., Kan, E., Dahl, R. E., & Sowell, E. R. (2014). The role of testosterone and estradiol in brain volume changes across adolescence: A longitudinal structural MRI study. Human Brain Mapping, 35(11), 5633–5645. http://dx.doi.org/10.1002/hbm.22575. Holm, S. A., Forbes, E. E., Ryan, N. D., Phillips, M. L., Tarr, J. A., & Dahl, R. E. (2009). Reward-related brain function and sleep in pre/early pubertal and mid/late pubertal adolescents. Journal of Adolescent Health, 45, 326–334. http://dx.doi.org/10.1016/ j.jadohealth.2009.04.001. Huang, B., Biro, F. M., & Dorn, L. D. (2009). Determination of relative timing of pubertal maturation through ordinal logistic modeling: Evaluation of growth and timing parameters. Journal of Adolescent Health, 45(4), 383–388. http://dx.doi.org/10.1016/ j.jadohealth.2009.02.013. Huang, B., Hillman, J., Biro, F. M., Ding, L., Dorn, L. D., & Susman, E. J. (2012). Correspondence between gonadal steroid hormone concentrations and secondary sexual characteristics assessed by clinicians, adolescents, and parents. Journal of Research on Adolescence, 22(2), 381–391. http://dx.doi.org/10.1111/j.1532-7795.2011.00773.x. Jones, R. M., Somerville, L. H., Li, J., Ruberry, E. J., Libby, V., Glover, G., et al. (2011). Behavioral and neural properties of social reinforcement learning. Journal of Neuroscience, 31, 13039–13045. http://dx.doi.org/10.1523/JNEUROSCI.2972-11.2011. Juraska, J. M., Sisk, C. L., & DonCarlos, L. L. (2013). Sexual differentiation of the adolescent rodent brain: Hormonal influences and developmental mechanisms. Hormones and Behavior, 64, 203–210. http://dx.doi.org/10.1016/j.yhbeh.2013.05.010. Klapwijk, E. T., Goddings, A.-L., Burnett Heyes, S., Bird, G., Viner, R. M., & Blakemore, S. J. (2013). Increased functional connectivity with puberty in the mentalising network involved in social emotion processing. Hormones and Behavior, 64, 314–322. http://dx.doi.org/10.1016/j.yhbeh.2013.03.012. Klump, K. L. (2013). Puberty as a critical risk period for eating disorders: A review of human and animal studies. Hormones and Behavior, 64, 399–410. http://dx.doi.org/10.1016/ j.yhbeh.2013.02.019. Klump, K. L., Culbert, K. M., Slane, J. D., Burt, S. A., Sisk, C. L., & Nigg, J. T. (2012). The effects of puberty on genetic risk for disordered eating: Evidence for a sex difference. Psychological Medicine, 42, 627–637. http://dx.doi.org/10.1017/S0033291711001541. Klump, K. L., Keel, P. K., Racine, S. E., Burt, S. A., Neale, M., Sisk, C. L., et al. (2013). The interactive effects of estrogen and progesterone on changes in emotional eating across the menstrual cycle. Journal of Abnormal Psychology, 122, 131–137. http://dx.doi.org/ 10.1037/a0029524. Klump, K. L., Keel, P. K., Sisk, C., & Burt, S. A. (2010). Preliminary evidence that estradiol moderates genetic influences on disordered eating attitudes and behaviors during puberty. Psychological Medicine, 40(10), 1745–1753. http://dx.doi.org/10.1017/ s0033291709992236. Lenroot, R. K., & Giedd, J. G. (2006). Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neuroscience and Biobehavioral Reviews, 30, 718–729.


89

Lenroot, R. K., Gogtay, N., Greenstein, D. K., Wells, E. M., Wallace, G. L., Clasen, L. S., et al. (2007). Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage, 15, 1065–1073. Lerner, R. M., & Steinberg, L. (Eds.). (2009). Handbook of adolescent psychology (3rd ed.). Hoboken, NJ: Wiley. Luciana, M. (2010). Adolescent brain development: Current themes and future directions. Introduction to the special issue. Brain and Cognition, 72, 1–5. http://dx.doi.org/ 10.1016/j.bandc.2009.11.002. Luciana, M. (2013). Adolescent brain development in normality and psychopathology. Development and Psychopathology, 25, 1325–1345. http://dx.doi.org/10.1017/ S0954579413000643. Luciana, M., & Collins, P. F. (2012). Incentive motivation, cognitive control, and the adolescent brain: Is it time for a paradigm shift? Child Development Perspectives, 6, 392–399. Lydon, D. M., Wilson, S. J., Child, A., & Geier, C. F. (2014). Adolescent brain maturation and smoking: What we know and where we’re headed. Neuroscience and Biobehavioral Reviews, 45, 323–342. http://dx.doi.org/10.1016/j.neubiorev.2014.07.003. Lynne-Landsman, S. D., Graber, J. A., & Andrews, J. A. (2010). Do trajectories of household risk in childhood moderate pubertal timing effects on substance initiation in middle school? Developmental Psychology, 46(4), 853–868. http://dx.doi.org/10.1037/ a0019667. Marceau, K., Ram, N., Houts, R. M., Grimm, K. J., & Susman, E. J. (2011). Individual differences in boys’ and girls’ timing and tempo of puberty: Modeling development with nonlinear growth models. Developmental Psychology, 47(5), 1389–1409. http://dx.doi. org/10.1037/a0023838. Marklein, E., Negriff, S., & Dorn, L. D. (2009). Pubertal timing, friend smoking, and substance use in adolescent girls. Prevention Science, 10(2), 141–150. http://dx.doi.org/ 10.1007/s11121-008-0120-y. Marshall, W. A., & Tanner, J. M. (1969). Variations in the patterns of pubertal changes in girls. Archives of Disease in Childhood, 44, 291–303. Marshall, W. A., & Tanner, J. M. (1970). Variations in the patterns of pubertal changes in boys. Archives of Disease in Childhood, 45, 13–23. Martı´-Henneberg, C., & Vizmanos, B. (1997). The duration of puberty in girls is related to the timing of its onset. Journal of Pediatrics, 131(4), 618–621. http://dx.doi.org/10.1016/ s0022-3476(97)70073-8. Masten, C. L., Eisenberger, N. I., Borofsky, L. A., Pfeifer, J. H., McNealy, K., Mazziotta, J. C., et al. (2009). Neural correlates of social exclusion during adolescence: Understanding the distress of peer rejection. Social Cognitive and Affective Neuroscience, 4, 143–157. http://dx.doi.org/10.1093/scan/nsp007. McHale, S. M., Kim, J. Y., Dotterer, A. M., Crouter, A. C., & Booth, A. (2009). The development of gendered interests and personality qualities from middle childhood through adolescence: A biosocial analysis. Child Development, 80(2), 482–495. Mendle, J. (2014). Beyond pubertal timing: New directions for studying individual differences in development. Current Directions in Psychological Science, 23, 215–219. Mendle, J., Harden, K. P., Brooks-Gunn, J., & Graber, J. A. (2010). Development’s tortoise and hare: Pubertal timing, pubertal tempo, and depressive symptoms in boys and girls. Developmental Psychology, 46(5), 1341–1353. http://dx.doi.org/10.1037/a0020205. Mensah, F. K., Bayer, J. K., Wake, M., Carlin, J. B., Allen, N. B., & Patton, G. C. (2013). Early puberty and childhood social and behavioral adjustment. Journal of Adolescent Health, 53, 118–124. http://dx.doi.org/10.1016/j.jadohealth.2012.12.018. Mills, K. L., Goddings, A. L., Clasen, L. S., Giedd, J. N., & Blakemore, S. J. (2014). The developmental mismatch in structural brain maturation during adolescence. Developmental Neuroscience, 36(3–4), 147–160. http://dx.doi.org/10.1159/000362328.

90


Morris, N. M., & Udry, J. R. (1980). Validation of a self-administered instrument to assess stage of adolescent development. Journal of Youth and Adolescence, 9, 271–280. http://dx. doi.org/10.1007/BF02088471. Mrug, S., Elliott, M. N., Davies, S., Tortolero, S. R., Cuccaro, P., & Schuster, M. A. (2014). Early puberty, negative peer influence, and problem behaviors in adolescent girls. Pediatrics, 133, 7–14. http://dx.doi.org/10.1542/peds. 2013-0628. Negriff, S., & Susman, E. J. (2011). Pubertal timing, depression, and externalizing problems: A framework, review, and examination of gender differences. Journal of Research on Adolescence, 21(3), 717–746. http://dx.doi.org/10.1111/j.1532-7795.2010.00708.x. Nelson, E. E., Leibenluft, E., McClure, E. B., & Pine, D. S. (2005). The social re-orientation of adolescence: A neuroscience perspective on the process and its relation to psychopathology. Psychological Medicine, 35, 163–174. Nolen-Hoeksema, S., & Hilt, L. (2009). Gender differences in depression. In I. H. Gotlieb & C. L. Hammen (Eds.), Handbook of depression (2nd ed., pp. 386–404). New York: Guilford. Op de Macks, Z. A., Gunther Moor, B., Overgaauw, S., G€ urog˘lu, B., Dahl, R. E., & Crone, E. A. (2011). The social re-orientation of adolescence: A neuroscience perspective on the process and its relation to psychopathology. Developmental Cognitive Neuroscience, 1, 506–516. http://dx.doi.org/10.1016/j.dcn.2011.06.003. Pantsiotou, S., Papadimitriou, A., Douros, K., Priftis, K., Nicolaidou, P., & Fretzayas, A. (2008). Maturational tempo differences in relation to the timing of the onset of puberty in girls. Acta Paediatrica, 97(2), 217–220. http://dx.doi.org/10.1111/j.16512227.2007.00598.x. Peper, J. S., Hulshoff Pol, H. E., Crone, E. A., & van Honk, J. (2011). Sex steroids and brain structure in pubertal boys and girls: A mini-review of neuroimaging studies. Neuroscience, 191, 28–37. http://dx.doi.org/10.1016/j.neuroscience.2011.02.014. Petersen, A. C., Crockett, L., Richards, M., & Boxer, A. (1988). A self-report measure of pubertal status: Reliability, validity, and initial norms. Journal of Youth and Adolescence, 17, 117–133. http://dx.doi.org/10.1007/BF01537962. Pfeifer, J. H., & Allen, N. B. (2012). Arrested development? Reconsidering dual-systems models of brain function in adolescence and disorders. Trends in Cognitive Science, 16, 322–329. http://dx.doi.org/10.1016/j.tics.2012.04.011. Quevedo, K. M., Benning, S. D., Gunnar, M. R., & Dahl, R. E. (2009). The onset of puberty: Effects on the psychophysiology of defensive and appetitive motivation. Development and Psychopathology, 21, 27–45. Richards, J. M., Plate, R. C., & Ernst, M. (2013). A systematic review of fMRI reward paradigms used in studies of adolescents vs. adults: The impact of task design and implications for understanding neurodevelopment. Neuroscience and Biobehavioral Reviews, 37, 976–991. Romeo, R. D. (2010). Pubertal maturation and programming of hypothalamic-pituitaryadrenal reactivity. Frontiers in Neuroendocrinology, 31, 232–240. http://dx.doi.org/ 10.1016/j.yfrne.2010.02.004. Schmitz, K. E., Hovell, M. F., Nichols, J. F., Irvin, V. L., Keating, K., Simon, G. M., et al. (2004). A validation study of early adolescents’ pubertal self-assessments. Journal of Early Adolescence, 24(4), 357–384. http://dx.doi.org/10.1177/0272431604268531. Schulz, K. M., Molenda-Figueira, H. A., & Sisk, C. L. (2009). Back to the future: The organizational-activational hypothesis adapted to puberty and adolescence. Hormones and Behavior, 55(5), 597–604. http://dx.doi.org/10.1016/j.yhbeh.2009.03.010. Segalowitz, S. J., & Luciana, M. (2014). Introduction to a special issue on reward and regulatory processes in adolescence. Brain and Cognition, 89, 1–2.


91

Shirtcliff, E. A., Dahl, R. E., & Pollak, S. D. (2009). Pubertal development: Correspondence between hormonal and physical development. Child Development, 80(2), 327–337. http://dx.doi.org/10.1111/j.1467-8624.2009.01263.x. Silk, J. S., Siegle, G. J., Whalen, D. J., Ostapenko, L. J., LaDoueur, C. D., & Dahl, R. E. (2009). Pubertal changes in emotional information processing: Pupillary, behavioral, and subjective evidence during emotional word identification. Development and Psychopathology, 21, 7–26. http://dx.doi.org/10.1017/S0954579409000029. Sisk, C. L., & Berenbaum, S. A. (2013). Editorial for the special issue of hormones and behavior on puberty and adolescence. Hormones and Behavior, 64, 173–174. Sisk, C. L., & Zehr, J. L. (2005). Pubertal hormones organize the adolescent brain and behavior. Frontiers in Neuroendocrinology, 26, 163–174. Smith, A. R., Chein, J., & Steinberg, L. (2013). Impact of socio-emotional context, brain development, and pubertal maturation on adolescent risk-taking. Hormones and Behavior, 64(2), 323–332. http://dx.doi.org/10.1016/j.yhbeh.2013.03.006. Somerville, L. H., Jones, R. M., & Casey, B. J. (2010). A time of change: Behavioral and neural correlates of adolescent sensitivity to appetitive and aversive environmental cues. Brain and Cognition, 72, 124–133. http://dx.doi.org/10.1016/j.bandc.2009.07.003. Spear, L. P. (2000). The adolescent brain and age-related behavioral manifestations. Neuroscience and Biobehavioral Reviews, 24, 417–463. Spear, L. P. (2009). Heightened stress responsivity and emotional reactivity during pubertal maturation: Implications for psychopathology. Development and Psychopathology, 21, 87–97. http://dx.doi.org/10.1017/S0954579409000066. Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28(1), 78–106. http://dx.doi.org/10.1016/J.Dr.2007.08.002. Steinberg, L. (2010). A dual systems model of adolescent risk-taking. Developmental Psychobiology, 52(3), 216–224. http://dx.doi.org/10.1002/Dev.20445. Steinberg, L., Albert, D., Cauffman, E., Banich, M., Graham, S., & Woolard, J. (2008). Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: Evidence for a dual systems model. Developmental Psychology, 44(6), 1764–1778. http:// dx.doi.org/10.1037/A0012955. Susman, E. J., & Dorn, L. D. (2009). Puberty: Its role in development. In R. M. Lerner & L. Steinberg (Eds.), Handbook of adolescent psychology (3rd ed., pp. 116–151). Hoboken, NJ: Wiley. Susman, E. J., Houts, R. M., Steinberg, L., Belsky, J., Cauffman, E., DeHart, G., et al. (2010). Longitudinal development of secondary sexual characteristics in girls and boys between ages 9 1/2 and 15 1/2 years. Archives of Pediatrics and Adolescent Medicine, 164(2), 166–173. http://dx.doi.org/10.1001/archpediatrics.2009.261. Tanner, J. M. (1962). Growth at adolescence (2nd ed.). Oxford: Blackwell Scientific. Tanner, J. M. (1978). Foetus into man: Physical growth from conception to maturity. Cambridge, MA: Harvard University Press. Trotman, H. D., Holtzman, C. W., Ryan, A. T., Shapiro, D. I., MacDonald, A. N., Goulding, S. M., et al. (2013). The development of psychotic disorders in adolescence: A potential role for hormones. Hormones and Behavior, 64, 411–419. http://dx.doi.org/ 10.1016/j.yhbeh.2013.02.018. Urosˇevic´, S., Collins, P., Muetzel, R., Lim, K., & Luciana, M. (2012). Longitudinal changes in behavioral approach system sensitivity and brain structures involved in reward processing during adolescence. Developmental Psychology, 48, 1488–1500. http://dx. doi.org/10.1037/a0027502. Valadian, I., & Porter, D. (1977). Physical growth and development from conception to maturity. Boston: Little Brown.

92


van Anders, S. M. (2010). Chewing gum has large effects on salivary testosterone, estradiol, and secretory immunoglobulin A assays in women and men. Psychoneuroendocrinology, 35(2), 305–309. http://dx.doi.org/10.1016/j.psyneuen.2009.06.009. Walker, E. F., Sabuwalla, Z., & Huot, R. (2004). Pubertal neuromaturation, stress sensitivity, and psychopathology. Development and Psychopathology, 16, 807–824. Wallien, M. S., & Cohen-Kettenis, P. T. (2008). Psychosexual outcome of gender-dysphoric children. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 1413–1423. Webster, G. D., Graber, J. A., Gesselman, A. N., Crosier, B. S., & Schember, T. O. (2014). A life history theory of father absence and menarche: A meta-analysis. Evolutionary Psychology, 12, 273–294. Westling, E., Andrews, J. A., Hampson, S. E., & Peterson, M. (2008). Pubertal timing and substance use: The effects of gender, parental monitoring and deviant peers. Journal of Adolescent Health, 42(6), 555–563. http://dx.doi.org/10.1016/j.jadohealth.2007.11.002. Zahn-Waxler, C., Shirtcliff, E. A., & Marceau, K. (2008). Disorders of childhood and adolescence: Gender and psychopathology. Annual Review of Clinical Psychology, 4, 275–303.

CHAPTER THREE

Foundations of Children's Numerical and Mathematical Skills: The Roles of Symbolic and Nonsymbolic Representations of Numerical Magnitude Ian M. Lyons, Daniel Ansari1 Numerical Cognition Laboratory, Department of Psychology & Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada 1 Corresponding author: e-mail address: [email protected]

Contents 1. 2. 3. 4.

Introduction An Approximate System for the Representation of Numerical Magnitude The Symbolic Representation of Numerical Magnitude The Relationship Between Symbolic and Nonsymbolic Representations of Numerical Magnitude 5. Summary and Conclusions References

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Abstract Numerical and mathematical skills are critical predictors of academic success. The last three decades have seen a substantial growth in our understanding of how the human mind and brain represent and process numbers. In particular, research has shown that we share with animals the ability to represent numerical magnitude (the total number of items in a set) and that preverbal infants can process numerical magnitude. Further research has shown that similar processing signatures characterize numerical magnitude processing across species and developmental time. These findings suggest that an approximate system for nonsymbolic (e.g., dot arrays) numerical magnitude representation serves as the basis for the acquisition of cultural, symbolic (e.g., Arabic numerals) representations of numerical magnitude. This chapter explores this hypothesis by reviewing studies that have examined the relation between individual differences in nonsymbolic numerical magnitude processing and symbolic math abilities (e.g., arithmetic). Furthermore, we examine the extent to which the available literature provides strong evidence for a link between symbolic and nonsymbolic representations of numerical magnitude at the behavioral and neural levels of analysis. We conclude that claims that symbolic number abilities are grounded in the approximate system Advances in Child Development and Behavior, Volume 48 ISSN 0065-2407 http://dx.doi.org/10.1016/bs.acdb.2014.11.003

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Ian M. Lyons and Daniel Ansari

for the nonsymbolic representation of numerical magnitude are not strongly supported by the available evidence. Alternative models and future research directions are discussed.

1. INTRODUCTION Numerical information informs our everyday behavior. Consider a glance at the alarm clock in the morning, counting change in the line-up at the coffee shop, or reading about the latest election polls—each of these common situations places demands on the ability to process numerical information. Research has shown that the ability to process numbers and use them in mathematical operations (such as calculation) is a critical predictor of an individual’s economic and social success (e.g., Bynner & Parsons, 1997). Longitudinal studies investigating the predictors of academic achievement reveal that school-entry numerical and mathematical skills are a strong predictor of later academic achievement. School-entry math skills are a stronger predictor of later achievement than both school-entry reading and attentional skills (e.g., Duncan et al., 2007). Findings such as these demonstrate the critical role that numerical and mathematical knowledge and skills play in children’s academic development and outcomes. What do we know about how numbers are represented in the brain and mind and how such representations change over the course of learning and development? The past three decades have seen a surge in the empirical study of number representation and processing in multiple species and at different levels of analyses (for reviews, see Ansari, 2008; Dehaene, 1997; Nieder & Dehaene, 2009). The aim of this chapter is to provide an overview of this research and to synthesize what is currently known, as well as to discuss open questions and future research directions.

2. AN APPROXIMATE SYSTEM FOR THE REPRESENTATION OF NUMERICAL MAGNITUDE Much of the research on how we represent and process numerical information has been focused on uncovering the foundational systems that underpin the development of complex numerical and mathematical abilities. In particular, there has been a focus on understanding the representations of numerical magnitude, or the total number of items in a set. The representation of processing numerical magnitude has been investigated from infancy onward at both the behavioral and brain levels of analysis. In addition to

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studies with humans, numerical magnitude processing has been investigated in nonhuman primates in an effort to investigate cross-species similarities and differences in processing (Dehaene, 1997; Nieder & Dehaene, 2009). These studies have revealed that there are similarities in the way in which nonhuman primates, human infants, children, and adults represent and process numerical magnitude. This convergence across species and developmental time has lead to the suggestion that humans are born with an approximate system for the processing of numerical magnitude. Data from looking time paradigms have demonstrated that young infants can discriminate between numerical quantities. In a seminal study, Xu and Spelke (2000) found that 6-months-old infants discriminate between an array of 8 dots and an array of 16 dots, but not between 8 versus 12 dots. These results not only demonstrated for the first time that young infants can discriminate between two arrays of relatively large numerical magnitudes but also that their ability to do so is influenced by the numerical ratio (smaller/larger) between the numerical quantities. This pattern of results has been widely replicated, and the precision with which infants can discriminate between numerical magnitudes improves over the first year of life (see Libertus & Brannon, 2009 for a review of the infant numerical magnitude processing literature). The influence of numerical distance and ratio on numerical magnitude discrimination has been demonstrated in numerous studies with both human children and adults (e.g., Moyer & Landauer, 1967; Sekuler & Mierkiewicz, 1977). Specifically, when children and adults judge which of two numerical magnitudes (either numerical symbols, such as Arabic numerals or nonsymbolic stimuli, such as dot arrays, see Figure 1) are numerically larger, the speed and accuracy of their judgments is correlated with the numerical distance, and ratio of the numerical magnitude they compare. For children and adults, the larger the numerical distance between the magnitudes (or the closer the ratio between them is to 1), the slower and more error-prone their judgments of relative numerical magnitude are (see Figure 2).

Figure 1 Symbolic and nonsymbolic versions of the numerical magnitude comparison task.

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Figure 2 Examples of the numerical distance effect (NDE) (A) and the numerical ratio effect (NRE) (B).

As noted by Moyer and Landauer (1967) in their seminal paper, the numerical ratio explains more variability in numerical magnitude comparison data than does the numerical distance. Take, for example, the comparisons of a stimulus display of 8 versus 9 and a display of 1 versus 2. Both of these number pairs have a numerical distance of 1 but their ratio is different.


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Thus, a numerical distance effect would predict similar response times for both pairs, while the ratio effect predicts longer reaction times for 8 versus 9 compared to 1 versus 2 (i.e., 1 is 50% of 2, while 8 is 88% of 9). Despite the better prediction of reaction time data during comparison when ratio rather than distance is the independent variable, distance and ratio are highly correlated; thus, very similar predictions result in slight differences in the variance explained, rather than representing radically different models of numerical magnitude comparison data. In addition to the demonstrated existence of qualitatively similar distance and ratio effects in studies with humans ranging from young infants to adults, the influence of distance/ratio on numerical magnitude discrimination has also been shown in studies of nonhuman primates (e.g., Brannon & Terrace, 1998; Cantlon & Brannon, 2006). This body of evidence has lead to the proposal that humans share with other species an approximate system for the representation and processing of numerical magnitude, and given findings from infants, that humans are born with such a system (for a review, see Cantlon, 2012). It is because of the effect of numerical distance/ratio that the system is purported to represent numerical magnitude approximately. That is, if numerical magnitudes were represented exactly, then the ability to discriminate between them should not vary as a function of their similarity (i.e., as a function of distance/ratio), which is of course not the case. The fact that similarity between numbers predicts how well they can be differentiated from one another suggests that numbers that are close are represented more similarly than those that are comparatively further apart. Moreover, the existence of the numerical ratio effect suggests that the similarity between numerical magnitudes will increase as a function of their size. For example, the similarity between 9 and 8 is greater than that between 1 and 2. In this way, nonsymbolic numerical magnitudes are thought to be represented in an analog rather than digital format (Lyons, Ansari, & Beilock, in press). The most prominent account of the numerical distance/ratio effects posits that numbers are represented along a mental continuum (a “mental number line”) and that the representations of numerical magnitude overlap with one another, but that their overlap decreases as a function of the numerical distance/ratio between them (see Figure 2). While some have argued that numbers are represented on a linear scale (Figure 3, panel A), other researchers contend that the Gaussian tuning curves have a fixed width, but are represented along a logarithmic number line (Figure 3, panel B). It is beyond the scope of this chapter to discuss these subtle differences in the way in which the analog representation

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Figure 3 Models of the approximate representations of numerical magnitude.

Figure 4 Reconstruction of the human brain, showing the intraparietal sulcus (IPS) displayed in green (light gray in the print version).

of numerical magnitude has been conceptualized (for a review, see Feigenson, Dehaene, & Spelke, 2004). It is important to note in the context of the current discussion that representations are thought to be approximate/ analog in both models. Thus according to this account, the observation of ratio/distance effects across species and human developmental time indicates the existence of an approximate system of numerical magnitude representation that is likely evolutionarily ancient (given the evidence from nonhuman primates) and innate (given the evidence from very young human infants). In addition to behavioral evidence (such as looking times or reaction times), recent advances in noninvasive neuroimaging have allowed researchers to search for the neural correlates of numerical magnitude representations. Researchers have found that a brain region referred to as the parietal cortex, and in particular the intraparietal sulcus (see Figure 4), plays a key role in numerical magnitude processing (for reviews, see Ansari, 2008;


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Nieder & Dehaene, 2009). Moreover, studies with infants and young children have revealed that this brain region is activated during numerical magnitude processing from an early age onward (Cantlon, Brannon, Carter, & Pelphrey, 2006; Hyde, Boas, Blair, & Carey, 2010; Izard, Dehaene-Lambertz, & Dehaene, 2008). Thus, brain imaging provides another source of evidence in support of the notion that an approximate system for the representation of numerical magnitude exists from an early age onward and that there are qualitative similarities in the representation of numerical magnitude over the course of development.

3. THE SYMBOLIC REPRESENTATION OF NUMERICAL MAGNITUDE The literature just reviewed suggests the existence of an approximate representation of numerical magnitude that humans share with other species and that can be measured very early in human development. All of the findings reviewed thus far relied upon nonsymbolic representations of numerical magnitude (e.g., dot arrays) to glean insights into the representation and processing of numerical magnitude. In contrast to nonhuman primates, however, humans who grow up in literate cultures acquire symbolic representations of numerical magnitude, such as number words and Arabic numerals. Arabic numerals are something of a quasi-universal language of mathematics, since they are used to represent numerical magnitude across the globe. This raises a key question: Is the human acquisition of symbolic representations of numerical magnitude grounded in the approximate, potentially innate nonsymbolic representations of numerical magnitude? Various researchers have hypothesized that children’s approximate, nonsymbolic numerical magnitude processing abilities form the foundation on which more sophisticated, symbolic, culturally acquired mathematical skills rest (e.g., Dehaene, 1997). If this hypothesis is correct, one would expect that a child with a representation of nonsymbolic numerical magnitude (e.g., one that is especially accurate) would be more likely to excel on mathematical tasks, such as standardized tests of symbolic arithmetic and other math abilities. In fact, several recent papers have shown just that: Individual differences in adults’, children’s, and infants’ ability to process nonsymbolic numerical magnitudes relate to performance on a wide range of formal math achievement tests (Bonny & Lourenco, 2013; Desoete, Ceulemans, De Weerdt, & Pieters, 2012; Gilmore, McCarthy, & Spelke, 2010; Gray & Reeve, 2014; Halberda, Ly, Wilmer, Naiman, & Germine, 2012; Halberda,

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Mazzocco, & Feigenson, 2008; Libertus, Feigenson, & Halberda, 2011, 2013; Libertus, Odic, & Halberda, 2012; Lonnemanna, Linkersd€ orfera, Hasselhorna, & Lindberg, 2011; Lourenco, Bonny, Fernandez, & Rao, 2012; Lyons & Beilock, 2011; Mazzocco, Feigenson, & Halberda, 2011a,b; Piazza et al., 2010; Starr, Libertus, & Brannon, 2013; vanMarle, Chu, Li, & Geary, 2014; for a review, see Feigenson, Libertus, & Halberda, 2013). To measure nonsymbolic numerical magnitude processing ability, many of these studies ask participants to determine which of two arrays of objects (typically dots on a computer screen) contains more objects (right panel of Figure 1). Arrays are typically presented too quickly for participants to count the dots individually, so they must instead rely on their intuitive sense of approximate magnitude. How well a person is able to complete this task is taken as a measure of the strength of their number sense. Using this method, for example, Halberda et al. (2012) showed that individuals with stronger (or more precise) representations of nonsymbolic numerical magnitude tend to report better math achievement scores—an effect that remains stable more or less throughout the life span and exists even after controlling for achievement scores in nonmathematical domains. In a similar vein, Libertus et al. (2011) showed that preschoolers’ number sense predicted math scores at the onset of formal math instruction and that performance on a dot-comparison task predicted college students’ math scores on a college entrance exam (i.e., their math SAT scores; Libertus et al., 2012). It is important to note, however, that these correlations do not disclose the causal direction between number sense and mathematical achievement. Seeking thus to draw a stronger causal claim, researchers have recently shown that approximate arithmetic training (e.g., estimating the sum of two or more dot arrays) improves symbolic math achievement scores in both adults (Park & Brannon, 2013, 2014) and in children (Hyde, Khanum, & Spelke, 2014). Taken together, these studies provide indirect evidence to suggest that a child’s (possibly innate) ability to represent nonsymbolic magnitudes forms an initial footing from which more sophisticated math abilities develop. In other words, these results lend some confidence to the exciting possibility that evolutionarily ancient neural systems (the approximate number system, in this case) are co-opted to help shape the way that cultural inputs (such as number symbols) underpin more sophisticated cognitive abilities (such as mathematics) (Dehaene & Cohen, 2007). On the other hand, considerable caution may still be warranted. First, it remains unclear precisely how this process occurs. Other studies have shown


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that training regimes using dot-comparison tasks fail to improve symbolic math performance (Dewind & Brannon, 2012; Park & Brannon, 2014; Wilson, Revkin, Cohen, Cohen, & Dehaene, 2006). This raises the distinct possibility that it is improved manipulation of nonsymbolic magnitudes (in an explicitly arithmetic context), and not simply increased precision of said magnitudes, that leads to improvement in symbolic math ability (see also Park & Brannon, 2014). Such a distinction may have crucial implications for early education: it may not be enough to simply expose young children to nonsymbolic magnitudes; rather, the benefits of such exposure may depend crucially on activities that expressly encourage children to manipulate those magnitudes in a mathematical context. Furthermore, a more detailed look at the correlations between nonsymbolic magnitude processing and math achievement shows that this effect has proven to be inconsistently replicated (De Smedt, Noe¨l, Gilmore, & Ansari, 2013). De Smedt et al. (2013) reviewed 25 different studies (18 with children and 7 with adult participants) and found that only a minority (7 of 18 with children, 4 of 7 with adults) showed a statistically significant relation between dot-comparison and symbolic math performance.1 This is quite likely due to the fact that the overall size of this effect is relatively small, as revealed by a recent meta-analysis (with an average r of about 0.20 for cross-sectional studies and 0.17 for longitudinal studies; Chen & Li, 2014). Perhaps most remarkable is the array of studies that have now shown that this relation is substantially reduced, or even entirely eliminated, once one controls for basic symbolic number processing abilities (Bartelet, Vaessen, Blomert, & Ansari, 2014; Brankaer, Ghesquie`re, & De Smedt, 2014; Castronovo & G€ obel, 2012; Fazio, Bailey, Thompson, & Siegler, 2014; Fuhs & McNeil, 2013; G€ obel, Watson, Lerva˚g, & Hulme, 2014; Holloway & Ansari, 2009; Kolkman, Kroesbergen, & Leseman, 2013; Lyons, Price, Vaessen, Blomert, & Ansari, 2014; Lyons & Beilock, 2011; Sasanguie, G€ obel, Moll, Smets, & Reynvoet, 2013; Szu˝cs, Devine, Soltesz, Nobes, & Gabriel, 2014; Toll & Van Luit, 2014; vanMarle et al., 2014). However, due to the highly variegated nature of these studies, it may be difficult to draw clear conclusions about the intervening role of basic symbolic number processing abilities. Different studies have employed different 1

By contrast, the authors reviewed 17 studies that measured the relation between symbolic number comparison and symbolic math—13 of which showed a significant effect.

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types of symbolic number tasks (e.g., number comparison, counting, or number ordering), which makes it unclear just which symbolic number skills are most crucial for underpinning more sophisticated math skills, such as mental arithmetic. Furthermore, different studies focus on different age ranges, which may add confusion because different skills may be more relevant at different points in development. Finally, not all studies control for the same factors—some might control for reading ability, basic processing speed, executive functioning, some combination thereof, or even none of these factors. To address this issue, Lyons et al. (2014) reported data from a single, large sample spanning six academic grades (1–6, with over 200 children in each grade), that included eight different numerical tasks, standardized mental arithmetic ability, as well as three control tasks—all of which were administered to all children in the sample. The authors were thus able to examine how a wide range of basic numerical abilities relate to mental arithmetic at several different time-points in development, all while controlling for nonnumerical factors (reading ability, basic processing speed, executive functioning, as well as within-grade age variation). Highly consistent with the Chen and Li (2014) meta-analysis, Lyons et al. (2014) showed an average zero-order correlation between nonsymbolic magnitude processing (i.e., dot comparison) and mental arithmetic ability of about r ¼ 0.24, which was statistically significant in each grade. However, after controlling for the other seven basic numerical tasks, including several symbolic tasks, these correlations were all rendered nonsignificant. By contrast, the symbolic processing tasks remained significant predictors of mental arithmetic ability, indicating that these symbolic abilities are more directly linked to more complex math processing than is nonsymbolic magnitude processing. Interestingly, the type of symbolic processing that best predicted arithmetic ability systematically changed with age: comparing relative symbolic quantities was more predictive in younger children (grades 1–2), whereas assessing relative order of symbolic quantities was more predictive in older children (starting in grade 3 and increasing thereafter through grade 6). Note also that these results remained significant even after controlling for the fact that the symbolic and arithmetic tasks are presented in the same visual format (i.e., Indo-Arabic numerals; see also Lyons & Beilock, 2011, for a similar result in adults). This suggests that it is not just symbolic number representation per se that is crucial for arithmetic, but how these symbols are used—and the critical symbolic skills may well shift over the course of development. In sum, a more direct and fruitful approach to understanding the emergence of


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sophisticated arithmetic abilities may be better focused on how children learn to understand and manipulate number symbols. A critical outstanding question is how young children first come to understand the numerical meaning of number symbols. Here, it is again tempting to assume that symbolic number understanding is bootstrapped from nonsymbolic magnitudes. However, even in kindergarteners and preschoolers, the current literature remains mixed. For instance, one may contrast a list of studies confirming a relation between nonsymbolic and symbolic number processing in kindergarten or younger children (Bonny & Lourenco, 2013; Gilmore et al., 2010; Gray & Reeve, 2014; Libertus et al., 2013; Mazzocco et al., 2011b; Starr et al., 2013) with a similar list of studies showing either the opposite (Bartelet et al., 2014; Fuhs & McNeil, 2013; Kolkman et al., 2013; Sasanguie, Defever, Maertens, & Reynvoet, 2014; Toll & Van Luit, 2014) or that basic symbolic number processing may provide the crucial intermediary step (vanMarle et al., 2014). Moreover, Mussolin, Nys, Content, and Leybaert (2014) have even provided evidence to suggest that developmental influence runs in the opposite direction—that improvement in symbolic number abilities predicts later accuracy in nonsymbolic magnitude comparison (see also Gelman & Gallistel, 2004). Specifically, Mussolin et al. (2014) measured 3–4-year-old children’s ability to process symbolic and nonsymbolic numbers at two different time-points 7 months apart (41 children successfully completed all tasks at both time-points). Results showed that symbolic performance at the first time-point predicted nonsymbolic performance at the second time-point, but not the other way around. In sum, though it is certainly tempting to conclude that the cultural acquisition of sophisticated number–symbol systems operates by co-opting a more evolutionarily ancient system of nonsymbolic magnitude representation, it remains poorly understood both whether and precisely how this process may occur. It may be that only a large scale study similar to Lyons et al. (2014), but longitudinal in design and beginning with children whom have yet to receive any formal schooling, can adequately lay the issue to rest.

4. THE RELATIONSHIP BETWEEN SYMBOLIC AND NONSYMBOLIC REPRESENTATIONS OF NUMERICAL MAGNITUDE The mixed evidence concerning the association between nonsymbolic numerical magnitude processing and children’s arithmetic achievement casts

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doubt on the assumption that symbolic number skills are scaffolded on their nonsymbolic counterparts. Specifically, one influential proposal suggests that the very meaning of number symbols is determined by direct reference to the corresponding nonsymbolic magnitude: “When we learn number symbols, we simply attach their arbitrary shapes to the relevant nonsymbolic quantity representations” (Dehaene, 2008, p. 552; see also, Dehaene, 1997; Gallistel & Gelman, 2000). As noted earlier, the appeal of this proposal is straightforward: the solution to the mystery of how number symbols ground their meaning in reality is that they are simply linked to their evolutionary precursors—i.e., the (possibly innate) representation of the corresponding nonsymbolic magnitude. Support for this view has been consistently echoed (e.g., Eger et al., 2009; Feigenson et al., 2004, 2013; Hubbard et al., 2008; Libertus & Brannon, 2009; Lyons & Ansari, 2009; Nieder & Dehaene, 2009; Piazza, Pinel, Le Bihan, & Dehaene, 2007), and the idea has been given explicit form in the computational model proposed by Verguts and Fias (2004). To understand their model, one can imagine different internal nodes, each of which represents a given number. If a given node shows the greatest degree of activity, then the model will “respond” with the number thus indicated. Consistent with the notion of a Gaussian tuning curve along a mental number line (discussed earlier; see also Figure 3), a nonsymbolic magnitude (such as an array of dots) will tend to maximally activate the node corresponding to the correct number of dots (e.g., “six”). Note that the surrounding nodes (“five” and “seven”) will also be activated—but crucially, on average, activation at these nodes will be less than at “six.” Continuing the pattern, “four” and “eight” will be activated, but even less so; and so on. The idea is that the underlying representation for a given nonsymbolic magnitude is not an exact quantity, but a probabilistic representation centered on the actual magnitude (this representation is thought to drive the numerical distance and ratio effects—see Section 1). Adding random perturbations to the model causes it to generate errors that mirror human behavior: The model will most often respond “six” to six dots, but it will sometimes respond “five” or “seven” and even occasionally “four” or “eight”. For a symbolic input (e.g., “6”), the model simply draws a direct link to the “six” node. In this way, symbolic representation is much more precise—both the model and humans make very few errors when dealing with symbolic inputs. The crucial point, however, is that the central node for six dots “six” is exactly the same node that “6” points to. It is in this way that the Verguts and Fias model makes explicit the view that symbolic and nonsymbolic numerical stimuli point to essentially the same underlying representation.


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Three lines of evidence are typically cited in support of the proposal that symbolic and nonsymbolic numerical stimuli draw from the same underlying representations. First, as discussed in the previous section, individual differences in nonsymbolic magnitude processing are related to more complex symbolic math abilities. Second, behavioral and neural signatures such as the distance and ratio effects (see Figure 2) are qualitatively similar for symbolic and nonsymbolic numbers (e.g., Buckley & Gillman, 1974; Dehaene, 2008). Third, neural evidence has pointed to similar substrates in the brain for symbolic and nonsymbolic number processing (Diester & Nieder, 2007, 2010; Eger et al., 2009; Fias, Lammertyn, Reynvoet, Dupont, & Orban, 2003; Piazza et al., 2007; though see Shuman & Kanwisher, 2004, for evidence to the contrary). Recently, several counterarguments to this proposal—and to the three lines of evidence outlined in the preceding paragraph—have emerged. First, with respect to the relation between individual differences in nonsymbolic processing and arithmetic, we reviewed several lines of evidence in the previous section that casts substantial doubt on the reliability, specificity, and meaning of this relation. Second, it is worth pointing out that ratio and distance effects are hallmarks of essentially any discrimination task, and is true for stimuli ranging from letter comparisons (which letter comes later in the alphabet; Van Opstal, Gevers, De Moor, & Verguts, 2008), to discriminations along the most basic of perceptual dimensions (such as odor discrimination in Drosophila; Parnas, Lin, Huetteroth, & Miesenb€ ock, 2013), to discriminations along relatively abstract, categorical variables (such as distinguishing between species of animal figures; Gilbert, Regier, Kay, & Ivry, 2008). Of course, one would be hard pressed to argue that the common signatures imply that letter sequences, odor representations in Drosophila, and representations of animal categories in humans are underlain by a common representation. There is thus a similar logical impasse (known more commonly as the fallacy of “reverse inference” in, for example, the neuroimaging literature; Poldrack, 2006) when attempting to argue that qualitatively similar numerical ratio and distance effects demonstrate a common representation for symbolic numbers and nonsymbolic magnitudes. Rather than relying on such indirect inference, Lyons, Ansari, and Beilock (2012) directly examined the behavioral implications of mixing numbers presented in symbolic and nonsymbolic formats. Specifically, adult participants were asked to directly compare a symbolic number with a nonsymbolic magnitude (an array of dots presented too quickly to count) and

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determine which represented the greater quantity. If the two visual formats essentially point to the same underlying representation, then performance should be little different than when comparing numbers in the same format (e.g., deciding which of two dot arrays contains more dots). Instead, results showed a very large cost of switching between formats (in particular, significantly longer response times). Note that the cost cannot simply be attributed to switching between two different types of visual stimuli, as no such cost was seen when switching between two symbolic formats: printed number words and Arabic numerals. In other words, results showed that integrating information across symbolic numbers and nonsymbolic magnitudes is much less efficient than would be expected if the two pointed to the same underlying representation. This indicates that symbolic and nonsymbolic numbers may in fact be represented more differently than had previously been assumed. It is important to note that this study was conducted with adults, which leaves open the possibility that young children’s symbolic and nonsymbolic number representations are more closely linked (with the dissociation emerging slowly over the course of ensuing development). On the other hand, we noted earlier that the evidence for a link between the two formats in kindergarteners and preschoolers is mixed, at best. In sum, symbolic and nonsymbolic numbers are probably less directly associated with one another than one would expect if the former were bootstrapped directly from the latter; however, the developmental processes remain only partially understood at present. Third, evidence for common neural processing of symbolic and nonsymbolic numbers appears to be far less convincing upon further examination. Perhaps one of the most influential studies showed cross-format fMRI adaptation (Piazza et al., 2007). Participants repeatedly saw a number presented in one format (e.g., the symbolic number “50”); then they saw a number in the other format (in this example, an array of dots). Sometimes the number in the new format would be the same magnitude (50 dots) and sometimes the number would change (e.g., 20 dots). The authors showed that activity in the parietal cortex was greater when the number changed than when it did not, indicating some degree of cross-format coding in this brain area. On the other hand, Cohen Kadosh et al. (2011) subsequently demonstrated that parietal brain areas are far more sensitive to changes in format than to changes in number. That is, the brain is perhaps more keenly tuned to the differences in numerical format than to their similarities. The logical impasse of reverse inference (mentioned earlier in the context of distance effects) applies to much of the neuroimaging evidence as


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well. In other words, simply showing that two tasks coactivate the same brain area does not imply that the processing utilized in that area is the same for both tasks because the same brain area may subserve many different functions depending on the exact pattern of activity seen in that area (see, e.g., Anderson, Kinnison, & Pessoa, 2013; Anderson and Penner-Wilger, 2013; Poldrack, 2006). In response, several papers have looked not at how much a particular brain area is activated, but instead at the spatial patterns of activity within a brain region.2 To date, four papers have directly assessed the notion of common distributed patterns of brain activity across symbolic and nonsymbolic number processing (Bulthe´, De Smedt, & Op de Beeck, 2014; Damarla & Just, 2013; Eger et al., 2009; Lyons et al., in press). Bulthe´ et al. (2014) found no evidence at any of the spatial scales they examined for similar distributed representations across symbolic and nonsymbolic numbers. They also failed to find evidence that successful classification of different numbers in one format was capable of generalizing to the other format. Damarla and Just’s (2013) results echoed those of Bulthe´ and colleagues. Lyons et al. (in press) also replicated Bulthe´ et al.’s central result: no evidence was found to indicate that the distributed pattern of neural activity for a given symbolic number—e.g., “6”—is related to the pattern of activity seen for the same number when presented nonsymbolically—six dots. Furthermore, Lyons et al. (in press) also showed that even the underlying representation structures—how the patterns of activity for different numbers relate to one another—are qualitatively different for symbolic and nonsymbolic numbers. Indeed, only one of the studies (Eger et al., 2009) found positive evidence indicating that spatial patterns of neural activity for symbolic and nonsymbolic numbers bear any relation to one another—and even in that case, decoding was unidirectional and only slightly above chance: 57% accuracy, where chance was 50%. Therefore, more fine-grained analysis of the underlying patterns of neural data provides scant evidence for the idea that there are similar representations in the brain for symbolic and nonsymbolic numbers. This may be partially explained by recent evidence indicating that the neural overlap seen for symbolic and nonsymbolic numbers depends on task demands. While number comparison tasks (which of two items is numerically greater) showed overlap in brain activity in a canonical number region 2

The specific spatial distribution of an activation pattern over multiple units (voxels) is by definition more specific than the activity of a single node or region. In theory, though the danger of reverse inference remains to some extent, it should thus be mitigated when considering spatial patterns of activity because these are less likely to be shared across highly disparate brain functions.

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(the intraparietal sulcus), this was not true for brain activity during symbolic and nonsymbolic numerical ordering tasks (are items in numerical order; Lyons & Beilock, 2013). This result calls into question whether neural overlap between symbolic and nonsymbolic number processing indicates a fundamentally common representation or a more transient effect that is more reflective of task demands than basic underlying representation. Thus, recent evidence has begun to erode a strong view of the notion that the meanings of number symbols are grounded in a direct reference to their nonsymbolic counterparts. An alternative explanation is that number symbols are initially linked exclusively via the exact, nonsymbolic quantities within the subitizing range (4)3—that is, numbers less than or equal to four may be mapped onto corresponding nonsymbolic magnitudes, but those outside this range are largely distinct from their nonsymbolic counterparts (Carey, 2004). Le Corre and Carey (2007) provided evidence consistent with the view that an initial understanding of symbolic numbers is tied to the subitizing system. A crucial step in the development of numerical understanding occurs when children grasp the “cardinality principle”—that counting to any number yields the number in the set, as indexed by the last number said, and that this principle can be extended, theoretically, to any number. The authors showed that 3–4-year-old children were able to map number words onto arrays of objects (essentially name the number of objects) within the subitizing range prior to acquiring an understanding of the cardinality principle. However, children at this age failed to consistently map corresponding number words onto sets containing more than four items until several months after acquiring the cardinality principle. This finding indicates that the ability to map number symbols (number words in this case) onto nonsymbolic magnitudes can occur prior to acquiring the cardinality principle, but only for numbers within the subitizing range. Le Corre and Carey (2007) interpreted this as evidence that symbol-magnitude mapping within the subitizing range is a critical precursor to grasping the numerical meanings of symbolic numbers more generally. Evidence in adults is consistent with this view: Lyons et al. (2012, reviewed earlier) showed that the cost of mixing symbolic and nonsymbolic 3

Subitizing refers to rapid, exact apprehension of the number of objects in a set without explicit counting (Dehaene & Cohen, 1994; Mandler & Shebo, 1982; Trick & Pylyshyn, 1994), is relatively stable over development (Schleifer & Landerl, 2011), and is limited to a capacity of about three or four items (a limit that, at least in adults, is related to the general processing capacity limit for visual shortterm memory; Luck & Vogel, 1997; Piazza, Fumarola, Chinello, & Melcher, 2011).


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formats for small numbers (4) is substantially smaller than that found for large numbers (>4; Lyons et al., 2012), suggesting that subitizable symbolic and nonsymbolic numbers may retain a link even into adulthood. How, then, do number symbols that refer to larger magnitudes—those beyond the subitizing range—acquire meaning? One idea is that the meanings of larger numbers are bootstrapped from an understanding of smaller number symbols (Carey, 2004). Just how this occurs remains up for debate (Ansari, 2008). Some researchers have suggested the grammatical structure of language plays a central role in understanding the representational structure of large symbolic numbers (Almoammer et al., 2013; Carey, 2004; Le Corre & Carey, 2007; Sarnecka, Kamenskaya, Yamana, Ogura, & Yudovina, 2007; Sullivan & Barner, 2014). Another possibility is that visuo-spatial processing is most crucial (e.g., Gunderson, Ramirez, Beilock, & Levine, 2012). In mapping numbers onto a visual-spatial number line, understanding that the spatial distance between integers remains constant on a linear scale helps children understand that the same is true for integers—even those that are uncountably large (Siegler & Ramani, 2008). Another, if related, view suggests that understanding magnitudes as ordered sequences allows us to reason about very large numbers with which one is unlikely to have much direct perceptual experience (e.g., one million; Lyons & Beilock, 2009, 2011). Crucially, these factors need not be mutually exclusive. They each operate from different formulations of a common assumption: that the meanings of larger number symbols are at best only loosely tied to their nonsymbolic counterparts (for a view that explicitly disagrees with this assumption; however, see Feigenson et al., 2013). On that front, a major unanswered question concerns the precise mechanism— linguistic, spatial, ordinal, nonsymbolic magnitudes, a combination thereof, or some yet-to-be discovered factor—that links larger number symbols to one another. As such, this question is now a central driver of research in the field of numerical cognition.

5. SUMMARY AND CONCLUSIONS Numbers play a critical role in our everyday lives, and acquiring numerical and mathematical skills is one of the central goals of formal education across the globe. Over the past three decades, researchers from the fields of Cognitive Science, Psychology, and Neuroscience have investigated how numbers are represented and processed in the brain and mind. A particular focus of this line of research has been on better understanding

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the foundations upon which the development of numerical and mathematical skills rest. To do this, researchers have sought to understand how sets of items (numerical magnitudes) are processed and represented from infancy onward. A large body of recent evidence has converged to suggest that humans share with other species the ability to approximately represent nonsymbolic numerical magnitude (e.g., arrays of dots). This ability has also been found in very young infants. Furthermore, brain-imaging evidence suggests the involvement of the parietal cortex during numerical magnitude processing in monkeys, young babies, children, and adults. In view of the large body of evidence supporting the theory that there exists both phylogenetic and ontogenetic continuity in the representation of nonsymbolic numerical magnitude, it has frequently been contended that this representation serves as the basis of acquisition of symbolic representations of numerical magnitude (e.g., number words and Arabic numerals) over the course of human development. Theories of the development of symbolic number processing (e.g., Dehaene, 2008) as well as training studies (e.g., Lyons & Ansari, 2009) and computational models (e.g., Verguts & Fias, 2004) are underpinned by the assumption that symbols, such as number words and digits acquire their meaning (i.e., become symbolic representations of numerical magnitude) by becoming connected to the approximate, nonsymbolic representations of numerical magnitude that can be found across species and can be detected early in human development. This theory is certainly compelling and intuitive. However, as this literature review demonstrates, the empirical studies that have examined its predictions have not provided robust evidence in support of a strong link between the nonsymbolic, approximate representation of numerical magnitude and number symbols. One approach to testing the predicted connection between nonsymbolic and symbolic representations of number has been to examine correlations between children’s nonsymbolic number discrimination abilities and their symbolic numerical and mathematical skills. If nonsymbolic representations of numerical magnitude provide the scaffold upon which more complex, symbolic numerical skills are built, then individual differences in nonsymbolic numerical magnitude representations should predict variability in children’s formal, symbolic numerical, and mathematical skills. The data summarized here, however, do not provide strong support for this prediction. Specifically, the correlations between nonsymbolic magnitude processing and measures of arithmetic achievement have been found to be mostly weak and have not been shown to explain unique variance over and above symbolic


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number processing skills. Some evidence even suggests that improvements in symbolic number processing precedes improvements in nonsymbolic processing. In addition to studies that have correlated nonsymbolic numerical magnitude processing measures with symbolic measures of numerical and mathematical achievement, researchers have examined the connection between symbolic and nonsymbolic representations in experimental studies using both behavioral and brain-imaging methods. As this review demonstrates, and similar to correlational studies, these investigations have not provided evidence in support of a strong connection between symbolic and nonsymbolic, approximate representations of numerical magnitude. If anything, the bulk of the evidence reviewed here indicates that the differences between symbolic and nonsymbolic numbers may well outweigh any similarities between the two formats. In light of our review of this research, we suggest that the “SymbolGrounding Problem” in the field of numerical cognition—that is how symbols acquire their meaning beyond associations with one another (Harnad, 1990)—has not been solved. The hypothesis that this process can be explained through the development of a strong connection between number symbols and the well-documented approximate system for the representation of numerical magnitude is not supported by available data from children and adults at both the cognitive and neural levels of analysis. Therefore, a major challenge for the field of numerical cognition will be to explore alternative solutions to the “Symbol-Grounding Problem,” such as the notion that it is through the connection with exact, nonsymbolic representations of number in the subitizing range (1–4) that children learn the rules of the number sequence and that these rules are then generalized to larger numbers without requiring a direct connection to nonsymbolic representations of numerical magnitudes. How children acquire and learn to manipulate symbolic numbers outside this range is currently an active and important area of research. A resolution to the “Symbol-Grounding Problem” will not only significantly improve our understanding of how children acquire sophisticated, symbolic representations of numerical magnitude that give them the potential to become the economists, engineers, and scientists of the future, but it will also have important educational implications. Our understanding of the processes by which children learn the meaning of number symbols will inform the best ways in which children will be assisted in this critical learning process by their teachers and caregivers.

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REFERENCES ˇ aucer, R., O’Donnell, T., et al. Almoammer, A., Sullivan, J., Donlan, C., Marusˇicˇ, F., Z (2013). Grammatical morphology as a source of early number word meanings. Proceedings of the National Academy of Sciences of the United States of America, 110(46), 18448–18453. Anderson, M. L., Kinnison, J., & Pessoa, L. (2013). Describing functional diversity of brain regions and brain networks. NeuroImage, 73, 50–58. Anderson, M. L., & Penner-Wilger, M. (2013). Neural reuse in the evolution and development of the brain: Evidence for developmental homology? Developmental Psychobiology, 55(1), 42–51. Ansari, D. (2008). Effects of development and enculturation on number representation in the brain. Nature Reviews. Neuroscience, 9(4), 278–291. Bartelet, D., Vaessen, A., Blomert, L., & Ansari, D. (2014). What basic number processing measures in kindergarten explain unique variability in first-grade arithmetic proficiency? Journal of Experimental Child Psychology, 117, 12–28. Bonny, J. W., & Lourenco, S. F. (2013). The approximate number system and its relation to early math achievement: Evidence from the preschool years. Journal of Experimental Child Psychology, 114(3), 375–388. Brankaer, C., Ghesquie`re, P., & De Smedt, B. (2014). Children’s mapping between nonsymbolic and symbolic numerical magnitudes and its association with timed and untimed tests of mathematics achievement. PLoS One, 9(4), e93565. Brannon, E. M., & Terrace, H. S. (1998). Ordering of the numerosities 1 to 9 by monkeys. Science, 282(5389), 746–749. Buckley, P. B., & Gillman, C. B. (1974). Comparisons of digits and dot patterns. Journal of Experimental Psychology, 103(6), 1131–1136. Bulthe´, J., De Smedt, B., & Op de Beeck, H. P. (2014). Format-dependent representations of symbolic and nonsymbolic numbers in the human cortex as revealed by multi-voxel pattern analyses. NeuroImage, 87, 311–322. Bynner, J., & Parsons, S. (1997). Does numeracy matter? London: The Basic Skills Agency. Cantlon, J. F. (2012). Math, monkeys and the developing brain. Proceedings of the National Academy of Sciences of the United States of America, 109, 10725–10732. Cantlon, J. F., & Brannon, E. M. (2006). Shared system for ordering small and large numbers in monkeys and humans. Psychological Science, 17(5), 401–406. Cantlon, J. F., Brannon, E. M., Carter, E. J., & Pelphrey, K. A. (2006). Functional imaging of numerical processing in adults and 4-y-old children. PLoS Biology, 4(5), e125. Carey, S. (2004). Bootstrapping and the origins of concepts. Daedalus, 133(1), 59–68. Castronovo, J., & G€ obel, S. M. (2012). Impact of high mathematics education on the number sense. PLoS One, 7(4), e33832. Chen, Q., & Li, J. (2014). Association between individual differences in nonsymbolic number acuity and math performance: A meta-analysis. Acta Psychologica, 148, 163–172. Cohen Kadosh, R., Bahrami, B., Walsh, V., Butterworth, B., Popescu, T., & Price, C. J. (2011). Specialization in the human brain: The case of numbers. Frontiers in Human Neuroscience, 5, 62. Damarla, S. R., & Just, M. A. (2013). Decoding the representation of numerical values from brain activation patterns. Human Brain Mapping, 34(10), 2624–2634. De Smedt, B., Noe¨l, M. P., Gilmore, C., & Ansari, D. (2013). How do symbolic and nonsymbolic numerical magnitude processing skills relate to individual differences in children’s mathematical skills? A review of evidence from brain and behavior. Trends in Neuroscience and Education, 2(2), 48–55. Dehaene, S. (1997). The number sense: How the mind creates mathematics. New York: Oxford University Press.


113

Dehaene, S. (2008). Symbols and quantities in parietal cortex: Elements of a mathematical theory of number representation and manipulation. In P. Haggard & Y. Rossetti (Eds.), Sensorimotor foundations of higher cognition (attention and performance) (pp. 527–574). New York: Oxford University Press. Dehaene, S., & Cohen, L. (1994). Dissociable mechanisms of subitizing and counting: Neuropsychological evidence from simultanagnosic patients. Journal of Experimental Psychology. Human Perception and Performance, 20(5), 958–975. Dehaene, S., & Cohen, L. (2007). Cultural recycling of cortical maps. Neuron, 56(2), 384–398. Desoete, A., Ceulemans, A., De Weerdt, F., & Pieters, S. (2012). Can we predict mathematical learning disabilities from symbolic and nonsymbolic comparison tasks in kindergarten? Findings from a longitudinal study. British Journal of Educational Psychology, 82(1), 64–81. Dewind, N. K., & Brannon, E. M. (2012). Malleability of the approximate number system: Effects of feedback and training. Frontiers in Human Neuroscience, 6, 68. Diester, I., & Nieder, A. (2007). Semantic associations between signs and numerical categories in the prefrontal cortex. PLoS Biology, 5, e294. Diester, I., & Nieder, A. (2010). Numerical values leave a semantic imprint on associated signs in monkeys. Journal of Cognitive Neuroscience, 22(1), 174–183. Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A., Klebanov, P., et al. (2007). School readiness and later achievement. Developmental Psychology, 43(6), 1428–1446. Eger, E., Michel, V., Thirion, B., Amadon, A., Dehaene, S., & Kleinschmidt, A. (2009). Deciphering cortical number coding from human brain activity patterns. Current Biology, 19(19), 1608–1615. Fazio, L. K., Bailey, D. H., Thompson, C. A., & Siegler, R. S. (2014). Relations of different types of numerical magnitude representations to each other and to mathematics achievement. Journal of Experimental Child Psychology, 123, 53–72. Feigenson, L., Dehaene, S., & Spelke, E. (2004). Core systems of number. Trends in Cognitive Sciences, 8(7), 307–314. Feigenson, L., Libertus, M. E., & Halberda, J. (2013). Links between the intuitive sense of number and formal mathematics ability. Child Development Perspectives, 7(2), 74–79. Fias, W., Lammertyn, J., Reynvoet, B., Dupont, P., & Orban, G. A. (2003). Parietal representation of symbolic and nonsymbolic magnitude. Journal of Cognitive Neuroscience, 15(1), 47–56. Fuhs, M. W., & McNeil, N. M. (2013). ANS acuity and mathematics ability in preschoolers from low-income homes: Contributions of inhibitory control. Developmental Science, 16(1), 136–148. Gallistel, C. R., & Gelman, R. (2000). Non-verbal numerical cognition: From reals to integers. Trends in Cognitive Sciences, 4(2), 59–65. Gelman, R., & Gallistel, C. R. (2004). Language and the origin of numerical concepts. Science, 306(5695), 441–443. Gilbert, A. L., Regier, T., Kay, P., & Ivry, R. B. (2008). Support for lateralization of the Whorf effect beyond the realm of color discrimination. Brain and Language, 105(2), 91–98. Gilmore, C. K., McCarthy, S. E., & Spelke, E. S. (2010). Nonsymbolic arithmetic abilities and mathematics achievement in the first year of formal schooling. Cognition, 115(3), 394–406. G€ obel, S. M., Watson, S. E., Lerva˚g, A., & Hulme, C. (2014). Children’s arithmetic development: It is number knowledge, not the approximate number sense, that counts. Psychological Science, 25(3), 789–798. Gray, S. A., & Reeve, R. A. (2014). Preschoolers’ dot enumeration abilities are markers of their arithmetic competence. PLoS One, 9(4), e94428.

114


Gunderson, E. A., Ramirez, G., Beilock, S. L., & Levine, S. C. (2012). The relation between spatial skill and early number knowledge: The role of the linear number line. Developmental Psychology, 48(5), 1229–1241. Halberda, J., Ly, R., Wilmer, J. B., Naiman, D. Q., & Germine, L. (2012). Number sense across the lifespan as revealed by a massive Internet-based sample. Proceedings of the National Academy of Sciences of the United States of America, 109(28), 11116–11120. Halberda, J., Mazzocco, M. M., & Feigenson, L. (2008). Individual differences in non-verbal number acuity correlate with maths achievement. Nature, 455(7213), 665–668. Harnad, S. (1990). The symbol grounding problem. Physica D, 42, 335–346. Holloway, I. D., & Ansari, D. (2009). Mapping numerical magnitudes onto symbols: The numerical distance effect and individual differences in children’s mathematics achievement. Journal of Experimental Child Psychology, 103(1), 17–29. Hubbard, E. M., Diester, I., Cantlon, J. F., Ansari, D., van Opstal, F., & Troiani, V. (2008). The evolution of numerical cognition: From number neurons to linguistic quantifiers. Journal of Neuroscience, 28(46), 11819–11824. Hyde, D. C., Boas, D. A., Blair, C., & Carey, S. (2010). Near-infrared spectroscopy shows right parietal specialization for number in pre-verbal infants. NeuroImage, 53(2), 647–652. Hyde, D. C., Khanum, S., & Spelke, E. S. (2014). Brief nonsymbolic, approximate number practice enhances subsequent exact symbolic arithmetic in children. Cognition, 131(1), 92–107. Izard, V., Dehaene-Lambertz, G., & Dehaene, S. (2008). Distinct cerebral pathways for object identity and number in human infants. PLoS Biology, 6(2), e11. Kolkman, M. E., Kroesbergen, E. H., & Leseman, P. P. M. (2013). Early numerical development and the role of nonsymbolic and symbolic skills. Learning and Instruction, 25, 95–103. Le Corre, M., & Carey, S. (2007). One, two, three, four, nothing more: An investigation of the conceptual sources of the verbal counting principles. Cognition, 105(2), 395–438. Libertus, M. E., & Brannon, E. M. (2009). Behavioral and neural basis of number sense in infancy. Current Directions in Psychological Science, 18(6), 346–351. Libertus, M. E., Feigenson, L., & Halberda, J. (2011). Preschool acuity of the approximate number system correlates with school math ability. Developmental Science, 14(6), 1292–1300. Libertus, M. E., Feigenson, L., & Halberda, J. (2013). Is approximate number precision a stable predictor of math ability? Learning and Individual Differences, 125, 126–133. Libertus, M. E., Odic, D., & Halberda, J. (2012). Intuitive sense of number correlates with math scores on college-entrance examination. Acta Psychologica, 141(3), 373–379. Lonnemanna, J., Linkersd€ orfera, J., Hasselhorna, M., & Lindberg, S. (2011). Symbolic and nonsymbolic distance effects in children and their connection with arithmetic skills. Journal of Neurolinguistics, 24(5), 582–591. Lourenco, S. F., Bonny, J. W., Fernandez, E. P., & Rao, S. (2012). Nonsymbolic number and cumulative area representations contribute shared and unique variance to symbolic math competence. Proceedings of the National Academy of Sciences of the United States of America, 109(46), 18737–18742. Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390(6657), 279–281. Lyons, I. M., & Ansari, D. (2009). The cerebral basis of mapping nonsymbolic numerical quantities onto abstract symbols: An fMRI training study. Journal of Cognitive Neuroscience, 21(9), 1720–1735. Lyons, I. M., Ansari, D., & Beilock, S. L. (2012). Symbolic estrangement: Evidence against a strong association between numerical symbols and the quantities they represent. Journal of Experimental Psychology. General, 141(4), 635–641.


115

Lyons, I. M., Ansari, D., & Beilock, S. L. (in press). Qualitatively different coding of symbolic and nonsymbolic numbers in the human brain. Human Brain Mapping, http://dx.doi.org/ 10.1002/hbm.22641. [Epub ahead of print]. Lyons, I. M., & Beilock, S. L. (2009). Beyond quantity: Individual differences in working memory and the ordinal understanding of numerical symbols. Cognition, 113(2), 189–204. Lyons, I. M., & Beilock, S. L. (2011). Numerical ordering ability mediates the relation between number-sense and arithmetic competence. Cognition, 121(2), 256–261. Lyons, I. M., & Beilock, S. L. (2013). Ordinality and the nature of symbolic numbers. Journal of Neuroscience, 33(43), 17052–17061. Lyons, I. M., Price, G. R., Vaessen, A., Blomert, L., & Ansari, D. (2014). Numerical predictors of arithmetic success in grades 1–6. Developmental Science, 17(5), 714–726. Mandler, G., & Shebo, B. J. (1982). Subitizing: An analysis of its component processes. Journal of Experimental Psychology. General, 111(1), 1–22. Mazzocco, M. M., Feigenson, L., & Halberda, J. (2011a). Impaired acuity of the approximate number system underlies mathematical learning disability (dyscalculia). Child Development, 82(4), 1224–1237. Mazzocco, M. M., Feigenson, L., & Halberda, J. (2011b). Preschoolers’ precision of the approximate number system predicts later school mathematics performance. PLoS One, 6(9), e23749. Moyer, R. S., & Landauer, T. K. (1967). Time required for judgements of numerical inequality. Nature, 215(2), 1519–1520. Mussolin, C., Nys, J., Content, A., & Leybaert, J. (2014). Symbolic number abilities predict later approximate number system acuity in preschool children. PLoS One, 9(3), e91839. Nieder, A., & Dehaene, S. (2009). Representation of number in the brain. Annual Review of Neuroscience, 32, 185–208. Park, J., & Brannon, E. M. (2013). Training the approximate number system improves math proficiency. Psychological Science, 24(10), 2013–2019. Park, J., & Brannon, E. M. (2014). Improving math with number sense training: An investigation of its underlying mechanism. Cognition, 133(1), 188–200. Parnas, M., Lin, A. C., Huetteroth, W., & Miesenb€ ock, G. (2013). Odor discrimination in Drosophila: From neural population codes to behavior. Neuron, 79(5), 932–944. Piazza, M., Facoetti, A., Trussardi, A. N., Berteletti, I., Conte, S., Lucangeli, D., et al. (2010). Developmental trajectory of number acuity reveals a severe impairment in developmental dyscalculia. Cognition, 116(1), 33–41. Piazza, M., Fumarola, A., Chinello, A., & Melcher, D. (2011). Subitizing reflects visuospatial object individuation capacity. Cognition, 121(1), 147–153. Piazza, M., Pinel, P., Le Bihan, D., & Dehaene, S. (2007). A magnitude code common to numerosities and number symbols in human intraparietal cortex. Neuron, 53(2), 293–305. Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10(2), 59–63. Sarnecka, B. W., Kamenskaya, V. G., Yamana, Y., Ogura, T., & Yudovina, Y. B. (2007). From grammatical number to exact numbers: Early meanings of ‘one’, ‘two’, and ‘three’ in English, Russian, and Japanese. Cognitive Psychology, 55(2), 136–168. Sasanguie, D., Defever, E., Maertens, B., & Reynvoet, B. (2014). The approximate number system is not predictive for symbolic number processing in kindergarteners. Quarterly Journal of Experimental Psychology, 67(2), 271–280. Sasanguie, D., G€ obel, S. M., Moll, K., Smets, K., & Reynvoet, B. (2013). Approximate number sense, symbolic number processing, or number-space mappings: What underlies mathematics achievement? Journal of Experimental Child Psychology, 114(3), 418–431.

116


Schleifer, P., & Landerl, K. (2011). Subitizing and counting in typical and atypical development. Developmental Science, 14(2), 280–291. Sekuler, R., & Mierkiewicz, D. (1977). Children’s judgments of numerical inequality. Child Development, 630–633. Shuman, M., & Kanwisher, N. (2004). Numerical magnitude in the human parietal lobe; tests of representational generality and domain specificity. Neuron, 44(3), 557–569. Siegler, R. S., & Ramani, G. B. (2008). Playing linear numerical board games promotes lowincome children’s numerical development. Developmental Science, 11(5), 655–661. Starr, A., Libertus, M. E., & Brannon, E. M. (2013). Number sense in infancy predicts mathematical abilities in childhood. Proceedings of the National Academy of Sciences of the United States of America, 110(45), 18116–18120. Sullivan, J., & Barner, D. (2014). Inference and association in children’s early numerical estimation. Child Development, 85(4), 1740–1755. Szu˝cs, D., Devine, A., Soltesz, F., Nobes, A., & Gabriel, F. (2014). Cognitive components of a mathematical processing network in 9-year-old children. Developmental Science, 17(4), 506–524. Toll, S. W., & Van Luit, J. E. (2014). Explaining numeracy development in weak performing kindergartners. Journal of Experimental Child Psychology, 124, 97–111. Trick, L. M., & Pylyshyn, Z. W. (1994). Why are small and large numbers enumerated differently? A limited-capacity preattentive stage in vision. Psychological Review, 101(1), 80–102. Van Opstal, F., Gevers, W., De Moor, W., & Verguts, T. (2008). Dissecting the symbolic distance effect: Comparison and priming effects in numerical and nonnumerical orders. Psychonomic Bulletin & Review, 15(2), 419–425. vanMarle, K., Chu, F. W., Li, Y., & Geary, D. C. (2014). Acuity of the approximate number system and preschoolers’ quantitative development. Developmental Science, 17(4), 492–505. Verguts, T., & Fias, W. (2004). Representation of number in animals and humans: A neural model. Journal of Cognitive Neuroscience, 16(9), 1493–1504. Wilson, A. J., Revkin, S. K., Cohen, D., Cohen, L., & Dehaene, S. (2006). An open trial assessment of “The Number Race”: An adaptive computer game for remediation of dyscalculia. Behavioral and Brain Functions, 2, 20. Xu, F., & Spelke, E. S. (2000). Larger number discrimination in 6-month-old infants. Cognition, 74, B1–B11.

CHAPTER FOUR

Developmental Origins of the Face Inversion Effect Cara H. Cashon1, Nicholas A. Holt Department of Psychological and Brain Sciences, University of Louisville, Louisville, Kentucky, USA 1 Corresponding author: e-mail address: [email protected]

Contents 1. The Face Inversion Effect 1.1 Definition of the Face Inversion Effect 2. Inversion Effects Over the First Year of Life 2.1 Effects of Inversion on Face Preference 2.2 Effects of Inversion on Face Recognition 2.3 Effects of Inversion on Face Processing 2.4 Effects of Inversion on Infants’ Scanning of Faces 2.5 Effects of Inversion on Face-Related Neural Responses 3. Making Sense of It All 3.1 Summary of Findings 3.2 What Is the Role of Experience? 3.3 Conclusion References

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Abstract A hallmark of adults’ expertise for faces is that they are better at recognizing, discriminating, and processing upright faces compared to inverted faces. We investigate the developmental origins of “the face inversion effect” by reviewing research on infants’ perception of upright and inverted faces during the first year of life. We review the effects of inversion on infants’ face preference, recognition, processing (holistic and second-order configural), and scanning as well as face-related neural responses. Particular attention is paid to the developmental patterns that emerge within and across these areas of face perception. We conclude that the developmental origins of the inversion effect begin in the first few months of life and grow stronger over the first year, culminating in effects that are commonly thought to indicate adult-like expertise. We posit that by the end of the first year, infants’ face-processing system has become specialized to upright faces and a foundation for adults’ upright-face expertise has been established. Developmental mechanisms that may facilitate the emergence of this upright-face specialization are discussed, including the roles that physical and social development may play in upright faces’ becoming more meaningful to infants during the first year.


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1. THE FACE INVERSION EFFECT Looking at an upside-down face is a strange visual experience. It elicits ambiguous feelings of familiarity and unsettlement. An inverted face can easily be identified as a face, but the viewing experience takes more mental effort than expected. It is challenging to resolve “the problem.” Children turn this experience into fun by turning upside-down to talk and “look funny” to others. Artists also play with inverted faces. For example, in the nineteenth century, artists created optical-illusion artwork for matchboxes that could be seen as one face when viewed upright and another face when turned upside-down. Researchers have extensively studied the upside-down face phenomenon known as the “face inversion effect.” Results have shown that adults are better at recognizing and discriminating upright faces than inverted faces (e.g., Valentine, 1988; Yin, 1969); they use more sophisticated processes, such as holistic and second-order configural processing, for upright faces than they do for inverted faces (e.g., Diamond & Carey, 1986; for reviews, see Maurer, Le Grand, & Mondloch, 2002; Rakover, 2013); they categorize them at different levels, treating upright faces at a subordinate level (i.e., as individual faces) (Tanaka, 2001). They even show specialized event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI) responses to upright faces (e.g., Kanwisher, Tong, & Nakayama, 1998; Rossion et al., 2000; for review, see Rossion & Gauthier, 2002). These are all hallmarks of adults’ expertise for upright faces and instantiations of the well-known face inversion effect. In this chapter, we will explore the developmental origins of the face inversion effect by reviewing research on infants’ perception of upright and inverted faces during the first year of life. To do so, we will compare the effects of inversion on various aspects of face perception. We will first discuss findings related to whether a bias for upright faces exists in newborns and infants. We will then review the literature on infants’ recognition, processing, and scanning of upright versus inverted faces. Throughout the chapter, we will pay particular attention to the developmental patterns that emerge within and across these areas of face perception. The existing research leads us to conclude that by the end of the first year, infants’ face-processing system has become specialized for upright faces and a foundation for adults’ upright-face expertise has been laid.

Origins of the Face Inversion Effect

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1.1. Definition of the Face Inversion Effect Interest in the face inversion effect started with its effects on face recognition. This line of research dates back to at least the 1960s. Brooks and Goldstein (1963), for example, demonstrated that 3- to 14-year-old children had more trouble recognizing their classmates when the pictures were presented upside-down than when the pictures were presented in an upright orientation. Later that decade, Yin (1969) published a classic set of studies that provided evidence that the inversion effect on face recognition in adults is due to factors that are and are not special to faces. For example, in one experiment, adults were tested on their recognition of upright and inverted face and nonface pictures. They were shown a series of black and white pictures of faces, houses, airplanes, and men in motion. During test, participants were presented with pairs of pictures and were asked to identify which picture in each pair they had seen before (i.e., looked familiar). The orientation of the stimuli was held constant from exposure phase to test phase. Using this old–new paradigm, Yin found that inversion had an effect on recognition overall, but it affected the recognition of faces disproportionately more than that of other mono-oriented objects (i.e., pictures of houses, airplanes, and men in motion). Adults produced more recognition errors when trying to identify inverted, familiar stimuli compared to upright, familiar stimuli (particularly for faces, houses, and men in motion). However, there was also a specific effect of inversion on faces. In the upright condition, adults produced fewer errors when identifying the familiar faces than when identifying the other images. In contrast, in the inverted condition, they produced a greater number of recognition errors with faces compared to nonfaces. Based on Yin’s classic study, the “face inversion effect” historically has referred to the fact that inversion disproportionately affects face recognition relative to nonface stimuli (Rakover, 2013; Valentine, 1988). However, more recently, researchers have begun using the term “face inversion effect” to refer only to differences between upright and inverted faces in recognition as well as other aspects of face processing (for review, see Rakover, 2013). Because we will be evaluating the different ways inversion has been shown to affect face perception in infants, we follow the latter use of the term “face inversion effect.” We will use the term “inversion effect” to apply to any instance in which inverting a face produces a difference in performance.

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2. INVERSION EFFECTS OVER THE FIRST YEAR OF LIFE 2.1. Effects of Inversion on Face Preference 2.1.1 Newborns Several of the earliest studies comparing newborns’ perception of upright and inverted faces were not intended as investigations into the effect of inversion on newborns’ face preferences. Instead, they were intended to further explore previous findings showing newborns preferred face-like to scrambled face stimuli (e.g., Goren, Sarty, & Wu, 1975). Nevertheless, several such studies included inverted face or face-like stimuli as comparison tests, which makes their results relevant to our review. One of the first of these studies was conducted by Johnson, Dziurawiec, Ellis, and Morton (1991, Experiment 2; see also Morton & Johnson, 1991). In this study, Johnson et al. used a tracking procedure to measure how far newborns would follow a visual stimulus as it moved from the center of their visual field toward the left and right sides. The researchers measured the degrees of arc that each infant’s head and eyes rotated away from midline while tracking the stimuli. Infants were tested with four hand-held paddles, presented one at a time, that were all roughly the shape of the external contour of a head but varied on the images that appeared in their centers. As illustrated in Figure 1, the internal portion of the paddles consisted of: (1) a schematic drawing of a face (Face), (2) a 3-dot face-like configuration in which two black dots were arranged above a single black dot (Config), (3) a face outline with a linear arrangement of the features from the schematic drawing (Linear), and (4) an upside-down version of the 3-dot pattern (Inverse). Johnson et al. (1991) found that 41 newborns, ranging in age from 15 to 69 min, tracked

Figure 1 Recreation of face stimuli used by Johnson et al. (1991, Experiment 2) to test newborns’ visual preferences. Adapted from Cognition, 40, M. H. Johnson, S. Dziurawiec, H. Ellis, and J. Morton, Newborns' preferential tracking of face-like stimuli and its subsequent decline, 1-19, Copyright (1991), with permission from Elsevier.


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the schematic Face stimulus marginally further with their eyes than the Config pattern, and significantly further than both the linear version of the same stimulus (Linear) and the upside-down 3-dot face pattern (Inverse). No difference in the degree of head-turning was found across the stimuli. The authors interpreted the eye-turning results as suggesting that infants are innately biased to attend to the basic structure of an upright face ( Johnson et al., 1991; see also Morton & Johnson, 1991). Complementary results have been found in newborns using a preferential-looking paradigm. Mondloch et al. (1999) presented newborns with a series of five pairs of images displayed on large rectangular cards. Experimenters, who were unaware of the stimuli being presented on each card, observed the direction of eye gaze by peering through a small hole in the center of the card. Based on the direction of infants’ first looks and the duration of infants’ looking toward the two sides, the experimenters were asked to determine which side infants preferred on each card. Among the set of cards used in this experiment, one card contained a pair of upright and inverted 3-dot face configurations, which were identical to the Config and Inverse stimuli used by Johnson et al. (1991). Mondloch et al. found that 9 out of 12 neonates, at an average age of only 53 min, displayed a visual preference for the upright, 3-dot, face-like configuration. Similar effects of inversion were found when these same 3-dot stimuli were presented to newborns on a monitor and the duration of their looks toward the stimuli was measured (Valenza, Simion, Macchi Cassia, & Umilta`, 1996, Experiment 1A). Using a preferential-looking paradigm, Valenza et al. used a projector to present pairs of face-like stimuli on a screen in front of infants. A video camera focused on the newborns’ faces allowed experimenters to code newborns’ eye movements frame by frame. Several different measures of visual attention were obtained. Valenza et al. found an effect of inversion on some, but not all, of these measures. No difference was found in the number of times infants shifted their attention or in their latencies to orient toward the upright and inverted face images. Differences were found, however, on measures related to how long infants looked toward the stimuli. Duration of first looks, duration of longest looks, and total fixation duration were all significantly longer for the upright 3-dot face image than the 3-dot inverted face image. These findings were replicated in a between-subjects version of the same task in which infants were shown either upright or inverted versions of the face image, one image at a time (Experiment 1B). The authors interpreted these findings as evidence that although the (upright) face-like pattern had no effect on infants’ orienting

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response, they did prefer the face-like pattern over the (inverted) nonface-like pattern, which supported Johnson and Morton’s (e.g., see Morton & Johnson, 1991) hypothesis that newborns have an innate bias for the structure of an upright face. These studies collectively demonstrate that across various measures and procedures, newborns attend more to upright faces—or at least to upright “face-like” stimuli—than to versions of those stimuli with the internal features rotated 180°. The findings of a later study by Macchi Cassia, Turati, and Simion (2004, Experiment 1) confirm that this preference pattern could be replicated with comparable, but more realistic, photographs of faces. Using a preferential-looking procedure, Macchi Cassia et al. presented newborns with grayscale photographs of female faces, one in the upright canonical orientation and the other with the internal features rotated 180°. Similar to the stimuli used in the previous studies, external features, such as the hair and ears, were excluded. Both number of orienting responses and duration of looking were found to be significantly greater toward the upright-face image than toward the image with the inverted internal features. In sum, an effect of inversion on newborns’ face preference has been found consistently across studies using different methods, measures, and face stimuli (see also effects of inversion on newborns’ perception of attractive faces; Slater, Quinn, Hayes, & Brown, 2000). The pattern that emerges suggests that infants are born with a bias to attend to the upright, internal features of faces compared to inverted, internal features of faces. 2.1.2 Underlying Mechanisms for Newborns’ Upright-Face Preference What might account for this upright-face bias in newborns? Several mechanisms have been proposed and reviewed extensively elsewhere (Bednar & Miikkulainen, 2003; Johnson, 2005, 2011; Morton & Johnson, 1991; Simion, Leo, Turati, Valenza, & Dalla Barba, 2007; Turati, 2004). Among them, Morton and Johnson (1991; see also Johnson, 2005, 2011) proposed that infants are born with a face-specific subcortical mechanism that specifies the structure of a conspecific face pattern. This mechanism is thought to orient infants’ attention to face stimuli in the environment in order to support the development and specialization of the face-processing system. In contrast, Bednar and Miikkulainen (2003) posited that newborns’ preference for upright faces could ultimately originate from a nonface-specific learning mechanism paired with genetically specified prenatal input. According to their view, a self-organizing face learning system begins prenatally in the form of a genetically specified and internally generated neural 3-dot


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representation that is not face specific. As an explanation for how prenatal input may be generated to produce a representation in the visual cortex, authors cite evidence in favor of the argument that during prenatal development embryos experience a state similar to rapid-eye-movement sleep. During this state, spontaneous neural activity, in the form of pontogeniculo-occipital waves, is generated. Essentially, Bednar and Miikkulainen present a computational model that demonstrates that this pattern of prenatal neuronal firing ensures that the face system arrives equipped with a rudimentary face representation. The model accurately replicates results from several studies with neonates, which seems to indicate that genetically specific prenatal input can produce a neonatal bias toward patterns that resemble upright faces (or perhaps top-heavy patterns more generally). Simion and colleagues proposed that the innate bias for upright faces observed in newborns is driven by a domain-general bias in the visual system for top-heavy stimuli. Simion, Valenza, Macchi Cassia, Turati, and Umilta` (2002) first demonstrated the top-heavy preference in newborns using nonface-like stimuli. They presented newborns with patterns of black squares that had more elements in the top half (e.g., five black squares arranged in the shape of the letter “T”) paired with inverted, or bottom heavy, versions of the same stimuli. They found reliable evidence that newborns preferred the top-heavy patterns to the bottom-heavy equivalent patterned stimuli. Subsequently, Macchi Cassia and colleagues tested whether this preference for top-heavy stimuli extended to faces (Macchi Cassia et al., 2004; Experiments 2 and 3). In Experiment 2 of their study, they presented newborns with two modified, scrambled versions of realistic female faces they used in their first experiment, described earlier. These scrambled face stimuli consisted of all the same facial features but in slightly different arrangements. In both configurations, the nose was turned in a horizontal position in the center of the face. However, one stimulus had more features in the upper half of the face, that is, the two eyes, which were rotated 90°, appeared in the forehead area. The other stimulus had more features in the bottom half of the face, that is, the eyes, which again were rotated, appeared in the chin and jaw area. The mouth was placed opposite the eyes in both faces, respectively. When these scrambled face images were presented sideby-side to newborns, Macchi Cassia et al. found a visual preference for the top-heavy arrangement. Critically, in their third and last experiment, Macchi Cassia and colleagues compared newborns’ visual attention toward a real upright face and the top-heavy scrambled face image. This was used to test whether

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newborns’ preference for upright faces was stronger than their preference for top-heavy stimuli and, therefore, unique to faces. Notably, the newborns did not show a preference for the upright faces or top-heavy nonface stimuli indicating that their attention was drawn equally to both stimuli. Together, these findings were taken as evidence that newborns’ preference for faces is driven by a nonface-specific perceptual bias (see also Macchi Cassia, Valenza, Simion, & Leo, 2008; Macchi Cassia et al., 2004; Simion, Macchi Cassia, Turati, & Valenza, 2001, 2003; Simion et al., 2002, 2007; Turati, 2004). 2.1.3 3 Months and Beyond At around 3 months of age, infants also exhibit a preference for upright faces, but the preference appears to have become more face specific. In a series of three experiments, Turati, Valenza, Leo, and Simion (2005) tested 3-month-olds’ visual preferences for: (1) upright, black-and-white photographs of female faces, with the hair removed, versus identical faces with the internal features rotated 180°, (2) top-heavy versus bottom-heavy nonface patterns, and (3) a real face image versus a top-heavy, scrambled version of the same image. The results of Experiment 1 were clear. Like newborns, 3-month-olds preferred the upright to inverted faces. The results of Experiment 2 were mixed. Three-month-olds who were tested with a pattern containing four black stars arranged above a single black star, or four elements in the top half and one in the bottom half, demonstrated a preference for the top-heavy pattern over the inverted, bottom-heavy version. However, infants who were tested with a top-heavy T-shaped pattern, which included three elements in the top half of the configuration, did not demonstrate a preference for the upright version of those stimuli. The results of these first two experiments suggest that infants’ general preference for top-heavy stimuli may be waning by 3 months of age, while their preference for upright faces is not. The results of Experiment 3 support this interpretation. Three-month-olds preferred upright faces to scrambled, top-heavy versions of those faces. Turati et al. took their findings as evidence that around 3 months of age, a facespecific preference for upright faces begins to emerge. Additional evidence for this developmental change comes from a recent study by Chien (2011). Chien presented a group of 3- to 5.5-month-old infants with 10 pairs of images, upright and inverted, which consisted of several top-heavy geometric patterns (e.g., Simion et al., 2002), face-like 3-dot patterns (e.g., Turati, Simion, Milani, & Umilta`, 2002), as well as grayscale images of top-heavy real faces (e.g., Macchi Cassia et al., 2004). Chien reasoned that if infants have a general top-heavy bias at this age, they should


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show a consistent preference for top-heavy stimuli across all stimulus types. Instead of showing this pattern of results, infants only displayed a preference for the upright images of real faces (those with and without the hair removed) over their inverted counterparts. They preferred the upright version of only one of the seven 3-dot and nonface geometric patterns. Based on these results, Chien concluded that infants’ upright-face preference around this age, 3–5.5 months, is no longer the result of a top-heavy bias. At this age, the preference for upright faces has become face specific. In sum, newborns’ preference for upright faces can be attributed to a general perceptual bias for top-heavy stimuli (see also Macchi Cassia et al., 2008) that develops into a more face-specific preference before 6 months. Thus, the results of studies on newborns’ and slightly older infants’ upright-face preferences demonstrate a shift from a general perceptual bias toward one that is more specialized for upright faces.

2.2. Effects of Inversion on Face Recognition The detrimental effect of inversion on adults’ face recognition is a wellestablished finding and a hallmark of adults’ expertise for upright faces (for reviews, see Rossion & Gauthier, 2002; Valentine, 1988). Studies of the effects of inversion on face recognition with infants during the first year of life, however, have produced mixed results. The first evidence that inversion affects infants’ face recognition comes from a classic study by Fagan (1972, Experiment 1). Using a familiarization/recognition test procedure, infants around 5–6 months of age were shown a pair of identical blackand-white pictures of faces for 1–2 min during familiarization. During the recognition test phase, infants were shown pairs of faces that included the familiar target face and a novel face for 5–10 s. Longer looking at the novel face was taken as evidence that infants recognized the target face as a familiar stimulus and discriminated the familiar and novel faces. Seventeen pairs of twins were tested. For one baby in each twin set, all the faces were presented in the upright orientation. For the sibling, all the faces were rotated 180°. Fagan found a reliable effect of inversion on the face recognition performance of these 5-month-old twins. The infants in the upright condition looked longer at the novel face indicating that they recognized the familiar face and discriminated the novel from the familiar face. Infants in the inverted condition did not show this pattern. Fagan reported on several other important studies in that chapter including an attempt to replicate the findings of his first experiment, but with

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76 singletons around 5–6 months of age (Experiment 3). Surprisingly, only the female infants in that follow-up study replicated the findings from his first experiment and showed the inversion effect. In fact, the male infants in Experiment 3 failed to show evidence of recognizing faces when presented in either orientation. Fagan also investigated this inversion effect on a group of younger, 4-month-old, singletons. He again found that infants failed to discriminate the two test faces even when presented in the upright orientation. However, he did find that they could discern an upright face from an inverted face during test, suggesting they are sensitive to the orientation of faces at this younger age (see also Fagan & Shepherd, 1979; McGurk, 1970; Watson, 1966). Turati, Sangrigoli, Ruel, and de Schonen (2004) conducted a series of experiments with 4-month-olds and found that infants only display a face recognition inversion effect under certain conditions. In their first experiment, 4-month-olds were habituated (i.e., received habituation trials until their looking time decreased by 50% on three consecutive trials relative to their first three trials) to a black-and-white photograph of a face presented on a monitor. In the test phase, the familiar (habituated) face was presented next to a novel face. The models wore shower caps over their hair and were looking to the side in a ¾ pose. Half of the infants were shown faces in the upright orientation throughout habituation and test; half of the infants were shown inverted faces throughout. Turati et al. found that 4-month-olds were not affected by inversion on this test and could recognize and, in contrast to Fagan’s results, discriminate the faces in both the upright and inverted orientations. In Experiment 2, Turati et al. found an inversion effect for the first time with 4-month-olds. In this experiment, infants were habituated to four versions of the same model that varied in expression (neutral and smiling) and angle (forward-facing, ¾ turn, or sideways). The test phase was the same as that in Experiment 1. In this more difficult task, which required infants to form a representation of the model’s face based on the invariant features displayed during habituation, an inversion effect was found. Four-month-olds recognized the familiar model during test, but only when the faces were presented in the upright orientation. Turati et al. concluded that the inconsistent results found across these experiments with 4-month-olds could be due to differences in the tasks (e.g., habituation to criterion vs. familiarization with a fixed number of trials) or stimuli used (e.g., external cues absent or present). In sum, there is evidence that 4-month-olds are sensitive to the orientation of faces, but the evidence for whether 4- to 6-month-olds


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demonstrate a face inversion effect of recognition is mixed. Fagan (1972) failed to find any evidence for face recognition at 4 months in either orientation. In contrast, Turati et al. (2004) found that 4-month-olds showed an inversion effect under certain conditions, and Fagan found it in some, but not all, 5- to 6-month-olds. These findings suggest that an inversion effect on face recognition is emerging around 4–6 months of age. However, there is not clear evidence that this inversion effect is robust at this age. The testing methods that were used in these studies varied and may account for some of the disparate findings. Future research should include the same testing method with infants across multiple age groups so that a clear developmental pattern can be found.

2.3. Effects of Inversion on Face Processing Maurer et al. (2002) reviewed studies on face perception and classified three types of configural face processing that are used by adults to process upright faces: first-order configural, holistic, and second-order configural processing. First-order configural processing refers to identifying a face as a face based on the general arrangement of the internal features, which for an upright face is the arrangement of two eyes above a nose that are above a mouth. Holistic processing refers to integrating facial features into a single face representation or gestalt (Maurer et al., 2002; Rossion, 2008). Secondorder configural processing refers to attending to the metric spacing between features. A hallmark of adults’ expertise for upright faces is that they engage in more configural processing (all three definitions) of upright faces than they do for inverted faces (for reviews, see Maurer et al., 2002; Rossion, 2008). We discussed young infants’ sensitivity to first-order configuration of upright faces from birth at the beginning of this chapter. Next, we will review the research on infants’ holistic and second-order configural processing of upright and inverted faces, including related findings on the “Thatcher illusion” in infants. 2.3.1 Holistic Processing The results of several studies on infants’ holistic face processing converge to show that it develops during infancy (Cashon & Cohen, 2004; Cashon, Ha, Allen, & Barna, 2013; Cohen & Cashon, 2001; Schwarzer, Zauner, & Jovanovic, 2007; Turati, Di Giorgio, Bardi, & Simion, 2010). The question that we will address next is: does holistic face processing become specialized for upright faces during the first year?

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The development of holistic processing of upright and inverted faces has been tested in infants using the face “switch” task (Cohen & Cashon, 2001). In this task, infants are habituated to two faces, shown one at a time. During the test phase, infants are shown a familiar, novel, and “switch” test face. The switch test face is created by switching features between the two habituation faces to create a new face that consists of familiar features displayed in a novel combination. As shown in the example stimuli presented in Figure 2, the eyes, nose, and mouth of Habituation Face A can be “cut” and “pasted” onto Habituation Face B to create the switch test face. Infants’ duration of looking time at the switch test face relative to the familiar test face is the key to inferring holistic processing. If infants show a preference for the switch over familiar test face (e.g., Habituation A), it is concluded that infants noticed the novel combination of familiar features and, thus,

Figure 2 Example of face stimuli used in the habituation “switch” task (e.g., Cohen & Cashon, 2001). In this task, infants are habituation to two faces (e.g., Habituation A and Habituation B), and then tested with a familiar, switch, and novel face. Only the switch test face is shown here. This switch face was made by combining the internal features of Habituation A with the external features of Habituation B. Note. The images are shown here in black and white, but are typically presented in color to infants. Adapted from Infant perception and cognition: recent advances, emerging theories, and future directions, L. M. Oakes, C. H. Cashon, M. Casasola, and D. H. Rakison (Eds.), by C. H. Cashon, Development of specialized face perception in infants: an information-processing perspective, pp. 69–83, Copyright © 2011. By permission of Oxford University Press, USA.


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processed the faces holistically. If infants do not show a difference in looking time between these two test faces, it is taken as evidence that they processed the faces featurally—that is, they processed the internal and external features (or subset of those features) independently of one another. Using this procedure, Cohen and Cashon (Cashon & Cohen, 2004; Cohen & Cashon, 2001) tested infants between 3 and 7 months. They found that holistic face processing develops between 3 and 4 months, but the processing found at each of those ages is not orientation specific (Cashon & Cohen, 2004). Two groups of infants around 6 months of age (5.5–6 and 6–6.5 months) were also tested but, again, no clear evidence of an effect of inversion on holistic processing was found. In fact, the performance of the older 6-month-olds, who were on average 6.25 months, closely resembled that of the 3-month-olds in that they failed to process faces in either orientation holistically. The first age at which a clear effect of inversion on holistic face processing was found was 7 months (Cohen & Cashon, 2001, see also Cashon & Cohen, 2003 for a similar result with 10-month-olds). As shown in Figure 3, holistic face processing develops by 4 months of age and becomes upright-face specific at 7 months. It can be seen very clearly that from 3 to 4 months, infants’ face processing changes from featural to holistic and this developmental change occurs for both upright and inverted

Figure 3 An illustration of the development of holistic processing for upright and inverted faces in infants between 3 and 7 months of age. Note. This illustration is based on data reported by Cashon and Cohen (2004) and Cohen and Cashon (2001). Based on The development of face processing in infancy and early childhood: current perspectives, O. Pascalis and A. Slater (Eds.), C. H. Cashon and L. B. Cohen, The construction, deconstruction, and reconstruction of infant face perception, pp. 58–68, Copyright (2003), Nova Science Publishers.

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faces. The figure also illustrates that after 4 months, the ability to process faces holistically appears to steadily decline for inverted faces; however, it declines only temporarily for upright faces (i.e., around 6 months). By 7 months of age, holistic processing of upright faces has returned. Cashon and Cohen (Cashon, 2011; Cashon & Cohen, 2003, 2004) interpreted this U-shaped pattern for upright faces as indicating that infants’ face-processing system is undergoing a reorganization around 6 months of age. It was hypothesized that this reorganization occurs as infants begin to attribute new meaning, or special status, to upright faces and may be related to infants’ learning to sit (see also Cashon, Ha, Allen, et al., 2013). From this perspective, the reorganization appears to be complete and established around 7 months—the age at which the inversion effect on holistic processing was found. Another finding consistent with specialized holistic processing after 7 months was found in a more recent study by Ferguson, Kulkofsky, Cashon, and Casasola (2009). In this study, which was an investigation of the effects of race and inversion on holistic face processing, Caucasian 4-month-olds (Experiment 2) and 8-month-olds (Experiment 1) were tested using the switch face paradigm (Cashon & Cohen, 2004; Cohen & Cashon, 2001). When presented with own- and other-race upright faces, 4-montholds processed them holistically. In contrast, 8-month-olds processed upright, own-race faces holistically, but did not do so for inverted, own-race faces or other-race faces in either orientation. These findings provide further evidence for infants’ face-processing system being specialized for upright faces around 7–8 months of age. (They also suggest that their holistic face processing is specialized for own-race faces around this age as well.) Taken together, these results provide evidence that when tested using the habituation switch task, holistic processing of upright and inverted faces develops between 3 and 4 months of age (see also Turati et al., 2010), but it is not specialized for upright faces until 7–8 months. This pattern is similar to findings in other domains and areas of face perception in which infants are broadly tuned early in development, but become more specialized, or attuned, to the kinds of input they experience most often (for reviews, see Anzures et al., 2013; Cashon, 2011; Cashon & DeNicola, 2011; Maurer & Werker, 2014; Scott, Pascalis, & Nelson, 2007). 2.3.2 Second-Order Configural Processing The results of several studies suggest that infants are sensitive to the spacing between facial features, or second-order configural information, in faces (e.g., Quinn & Tanaka, 2009; Thompson, Madrid, Westbrook, &


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Johnston, 2001). Is there evidence that this sensitivity becomes upright-face specific during the first year? Bhatt, Bertin, Hayden, and Reed (2005) tested 3- and 5-month-olds’ sensitivity to second-order configural information in upright faces (Experiment 2) and in a separate experiment tested 5-month-olds’ sensitivity in inverted faces (Experiment 4). The stimuli that were used in these experiments were colored, clip-art, female face images. Infants were habituated to a face with the spacing between the eyes as well as between the nose and mouth increased (see Figure 4 for an example of a face that has had the spacing modified). In the test phase, infants were shown the unaltered and modified versions of the face. When tested with upright faces, Bhatt et al. (2005: Experiment 2) found that 5-month-olds, but not 3-month-olds, noticed the changes in spacing between the familiar and novel face stimuli. However, Bhatt et al. (2005, Experiment 4) also found that when 5-month-old infants were tested with inverted faces, they did not notice the spacing changes. The authors acknowledged that in those studies the face stimuli they used included spacing changes between features that were large and may have fallen outside the normal range for faces. To address this concern, Hayden, Bhatt, Reed, Corbly, and Joseph (2007) tested 5- and 7-month-olds’ sensitivity to second-order configural information with stimuli that were colored photographs of female faces and fell within the normal range. Using a movement-enhanced discrimination procedure, which we will not describe here but has been found to be a more sensitive procedure than a traditional novelty preference paradigm (see Bhatt, Hayden, Reed,

Figure 4 Example of faces that differ in their second-order configural information. The face on the left is unaltered, while the spacing between the eyes and between the nose and mouth has been increased in the face on the right. (Created by authors.)

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Bertin, & Joseph, 2006), Hayden et al. found that both 5- and 7-month-olds detected changes in second-order configural information in upright faces. In a second experiment, 5-month-olds were tested with upright and inverted faces, and an inversion effect was found. Based on these findings, Hayden, Bhatt, and colleagues concluded that at least by 5 months of age, infants exhibit one of the hallmark patterns of adults’ expert upright-face processing—that is, sensitivity to second-order configural information in upright, but not inverted, faces. This inversion effect was replicated in 9-month-olds whose sensitivity to spacing changes in male faces was tested using a familiarization/preferential-looking paradigm (Zieber et al., 2013). The results of this study do not inform us about the development of that sensitivity. However, the findings do provide additional support that infants in at least the latter half of the first year are sensitive to metric spacing changes in upright, but not inverted, faces. 2.3.3 Thatcher Illusion Thatcherized faces are faces that have the eyes and mouths rotated 180°, counter to the orientation of the rest of the face (see Figure 5). Adults view Thatcherized faces as grotesque when the upright version with inverted eyes and mouth are paired with an upright, unaltered version of the face (e.g., Thompson, 1980). Adults also find it easier to discriminate the Thatcherized and unaltered faces when they are upright compared to when they are inverted (e.g., Thompson, 1980). This effect is known as the “Thatcher illusion.” It has been suggested that the Thatcher illusion is the result of adults’ using second-order configural information to process upright, but not, inverted faces (e.g., Bartlett & Searcy, 1993; Diamond & Carey, 1986; but see Rakover, 1999, 2013). One challenge to testing infants on the Thatcher illusion is that infants cannot report whether they find the upright Thatcherized face to be grotesque. Thus, infant tests of the Thatcher illusion are based on whether infants can discriminate a Thatcherized face from an unaltered face, and whether that ability to discriminate differs for upright and inverted faces. For example, Bertin and Bhatt (2004) tested 6-month-olds’ ability to discriminate an unaltered, colored clip-art face image from a Thatcherized version of the same face using a habituation paradigm. During habituation, infants were shown two cards with identical faces (both Thatcherized or unaltered) on the left and right sides of the stage. During test, they were shown a Thatcherized face and an unaltered face. Depending on the habituation condition, the Thatcherized face served as either the familiar or novel


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Figure 5 Example of the “Thatcher illusion.” The faces on the left are unaltered, while those on the right have been “Thatcherized” (i.e., the eyes and nose have been rotated 180°). (Created by authors.)

test stimulus. Bertin and Bhatt found that 6-month-olds discriminated between the two test faces in the upright condition, but not in the inverted condition. Bhatt et al. (2005, Experiment 1) then tested 3-month-olds using the same method and clip-art stimuli as those used with 6-month-olds (Bertin & Bhatt, 2004). They found that unlike 6-month-olds, 3-month-olds did not discriminate between the Thatcherized and unaltered faces in either orientation. Together, these findings suggest that: (1) sensitivity to the Thatcherized faces develops between 3 and 6 months, and (2) inversion does not affect this sensitivity until 6 months. The results of a recent study with newborns, however, are inconsistent with the findings of Bhatt and colleagues. Using a habituation paradigm and

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stimuli made from photographs of female faces as opposed to clip-art face images, Leo and Simion (2009) found that newborns discriminated between the Thatcherized and unaltered faces when they were presented in the upright orientation, but not when they were presented in the inverted orientation. Leo and Simion (2009) interpreted their findings as evidence that newborns are born with sensitivity to second-order configural information in upright, but not inverted, faces. They offered two possible explanations for their contradictory results: (1) they used real photographs of women rather than clip-art images, which could make it easier for younger infants to succeed on the task, or (2) their findings might be, yet, another example of younger infants’ having abilities that older infants or children appear to lack. Either of these is plausible, but there is another possible interpretation. A potential drawback of using a paired-presentation discrimination paradigm to test the Thatcher illusion in infants is that to succeed on the task, processing the second-order configural information is not necessary. Newborns need only notice a difference in one feature between the Thatcherized and unaltered faces to produce a novelty preference. Leo and Simion’s findings could be, for example, the result of newborns’ attending only to the shape of the eyes. Rakover (2013) argues that the strangeness of seeing inverted eyes could be a driving force in the Thatcher illusion. In looking closely at Leo and Simion’s stimuli, the eyes in three of the four faces are unnatural, or strange. Consider the traits of the eyes shown in Figure 5.1 The eyes in Face A are inverted; the eyes in Face B are upright, but flipped horizontally so that the outside corners of the eyes are located close to the nose; and the eyes in Face C are both inverted and flipped horizontally. The eyes in Faces A and B are each comprised of one unusual trait, whereas the eyes in Face C are comprised of two unnatural traits. Because discrimination tasks rely on infants’ noticing that a difference exists, there is a clear confound. A more salient difference exists between the face stimuli in the upright condition than in the inverted condition. In other words, babies may discriminate the faces only in the upright condition not because they are sensitive to the second-order configural information, but because the difference between the eyes of the altered and unaltered faces is more dramatic in the upright condition. 1

The faces shown in Figure 5 were created by the authors of this chapter, but they are similar to the face stimuli that were used by Leo and Simion (2009) in the relevant ways being discussed. The faces shown here differ from theirs in that they are male faces.


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In sum, infants’ holistic face processing follows the same developmental trajectory for upright and inverted faces between 3 and 4 months of age. By 7–8 months, infants’ holistic processing has become upright-face specific. Infants’ sensitivity to second-order configural changes emerges by around 5 months of age and has been shown to be affected by inversion when tested with faces that vary in the metric distances between features. Studies of infants’ ability to discriminate between Thatcherized and unaltered faces, which is also thought to reflect sensitivity to second-order configural processing, have produced mixed results. One study found an inversion effect in 6-month-olds, but not 3-month-olds; yet another study found an inversion effect in newborns. It is not clear that successful discrimination between the Thatcherized and unaltered upright faces in these studies is based on infants’ having expert-like second-order configural processing skills for upright, but not inverted, faces. As previously discussed, infants could instead be responding to salient feature differences (e.g., shape of an eye) that do not require sensitivity to second-order configural information. This is an empirical question and could be tested in future studies.

2.4. Effects of Inversion on Infants’ Scanning of Faces The increasing availability and use of eye-tracking systems have offered an avenue for investigating qualitative changes in infants’ visual scanning behaviors that occur as infants develop greater expertise with upright over inverted faces across the first year of life and beyond. Eye tracking is an exceptionally useful tool for examining: (1) how infants visually explore stimuli and (2) which specific areas or features are most important to infants during the processing or encoding of visual information. In order to determine the regions of an image that are being scanned, typically eye-tracking studies utilize areas of interest (AOIs) that are constructed around predetermined regions. For example, with face stimuli, researchers may choose to create AOIs around each of the internal features of the face (both eyes, nose, and mouth), with one or more AOIs constructed around the external regions (hair, forehead, ears, chin, etc.). The number of fixations or the duration of looking time can then be measured for each AOI to determine if any consistent patterns exist across infants. Via these measures, eye tracking can help us better understand the specific information to which infants attend and process while exploring different types of faces. To date, only a few studies have examined infants’ scanning of upright versus inverted faces (Gallay, Baudouin, Durand, Lemoine, & Lećuyer, 2006; Kato &

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Konishi, 2013; Oakes & Ellis, 2013), but their results have uncovered some interesting differences in the ways that infants visually explore faces of different orientations across development. The first study to use eye-tracking technology to address potential scanning differences for upright and inverted faces in infants was conducted by Gallay et al. (2006). Gallay et al. recorded the scanning behaviors of a group of 4-month-olds while they were habituated to a face. Each infant was tested in two counterbalanced sessions: one with upright faces and one with inverted faces. The total looking time and number of trials to habituate did not differ for the orientation conditions, indicating that infants attended and habituated similarly to both orientations. Although these measures were not affected by face inversion, orientation did play a role in how infants explored the faces. To examine infants’ scanning, Gallay et al. constructed three AOIs: one box around the eyes, one box around the nose and mouth combined, and one U-shaped AOI that included both cheeks connected by a small portion underneath the mouth. Based on these AOIs, Gallay et al. calculated the total duration of looking and the percentage of overall looking time in each area. They found that 4-month-olds spent more time scanning the internal regions of upright faces than the internal regions of inverted faces. Moreover, when viewing upright faces, infants spent more time looking at the nose/mouth region of upright faces compared to the nose/mouth region of inverted faces. Infants spent the majority of their time exploring the internal region of inverted faces by attending to the eyes. However, the amount of time fixated in the eye region did not differ for upright and inverted faces. These results clearly show that inversion affects the scanning of faces at 4 months. However, only one age group was tested, which does not allow us to place this pattern of results into a developmental context. Two recent studies that have examined developmental changes in infants’ scanning of upright and inverted faces were conducted by Kato and Konishi (2013) and Oakes and Ellis (2013). Kato and Konishi (2013) tested 6-, 8.5-, 11-, and 13.5-month-olds and adults with stimuli that were upright and inverted versions of a black-and-white schematic face (i.e., Fantz, 1961). They presented each image once for 30 s to infants and 10 s to adults. They found effects of inversion on infants’ scanning of faces and on their face preferences. The scanning finding is very straightforward. Infants scanned the internal features more while viewing the upright face than the inverted face. The effect of inversion on infants’ face preferences differed by age. Infants’ preference shifted from the upright face at 6 months to the inverted face at 13.5 months. Infants from the middle two age groups, 8.5 and 11 months, showed no


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preference for faces in either orientation. The developmental pattern that emerges here is similar to the pattern of changes in attention that are observed in infants during the process of habituation (Cohen, 2004; Hunter & Ames, 1988). They share a trajectory that can be described as going from a familiarity preference (i.e., upright faces), to showing no preference, to showing a preference for novelty (i.e., inverted faces). One other study examined infants’ scanning patterns developmentally with more realistic stimuli. Oakes and Ellis (2013) investigated developmental changes in scanning patterns for upright and inverted faces in 4.5-, 6.5-, 8-, and 12.5-month-olds. Importantly, their study used 48 photographs of real faces that differed in gender and race. Infants were randomly assigned to either an upright or inverted face condition and then viewed at least 16, and as many as 96, trials that lasted for 3 s each. Oakes and Ellis constructed AOIs of equal size for the upper (eyes), middle (nose), and lower (mouth) internal regions of the faces. Median fixation durations were calculated for each AOI on each trial, and then these medians were averaged for each infant. On this measure, infants scanned the internal region significantly more than the external region regardless of age or orientation. A developmental shift was observed between 8 and 12.5 months in infants’ scanning within the internal areas of upright faces, such that infants began to look less at the eyes and more at the mouth. This pattern was not found for the inverted orientation. Oakes and Ellis also measured gaze patterns using the proportion of scanning relative to the size of each AOI. Based on this measurement, effects of orientation on scanning patterns were found to be different across development. The 4.5- and 6.5-month-olds looked significantly longer at the eye regions of both upright and inverted faces. In contrast, the 8- and 12.5-month-olds scanned the eyes, nose, and mouth regions of upright faces generally equally given their sizes, with 12.5-month-olds looking only significantly more than would be expected toward the mouth. For inverted faces, these older infants scanned similarly to the younger infants, with greater looking toward the eyes and less looking toward the mouth than would be expected. Comparing the two younger and the two older age groups, there appears to be a developmental shift toward attending more to the mouth for upright faces. According to Oakes and Ellis (2013), this shift may be attributed to the increased significance of the mouth as a source of linguistic information and may suggest that in the latter part of the first year of life, infants have an expectation that upright faces are meaningful sources of social or linguistic information (see also Cashon & Cohen, 2003, 2004; Rakover, 2013).

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It is also worth noting that Kato and Konishi (2013) did not find the same trend observed by Oakes and Ellis (2013) in which older infants demonstrated greater attention to the mouth and less attention to the eyes. This discrepancy may be explained by the fact that the duration that infants were allowed to scan the faces differed dramatically between the two studies (3 s versus 30 s). Also, this discrepancy may be explained by the fact that Oakes and Ellis used real photographs of faces whereas Kato and Konishi used black-and-white schematic face images, which likely convey less social significance. It is certainly possible that infants would produce different patterns of scanning for socially relevant realistic faces compared to unrealistic line drawn face stimuli. In sum, these results suggest that differences in the scanning of upright and inverted faces are already observable by 4 months of age, and new scanning patterns emerge for upright, but not inverted, faces in 8- to 12.5-month-olds. This shift may reflect that infants at these older ages attribute more meaning to upright faces than to inverted faces. Furthermore, it appears that one indicator of increased expertise for upright faces may be greater scanning of the inner features of upright compared to inverted faces over development in the first year. Importantly, as Oakes and Ellis note, the possibility remains open that younger infants may scan upright and inverted faces in the same manner, but still process them differently. Thus, in future research, eye-tracking measures need to be further utilized in conjunction with tasks that examine infants’ processing abilities before any strong conclusions can be made in this regard. In the next section, we explore how the brain responds to upright and inverted faces during infancy. Although only a handful of studies have been conducted on this topic, their findings provide evidence of specialization for upright faces in the brain by the end of the first year.

2.5. Effects of Inversion on Face-Related Neural Responses 2.5.1 Near-Infrared Spectroscopy In adults, studies utilizing fMRI have identified a region in the fusiform gyrus that shows higher activation to faces than other visual stimuli (Kanwisher, McDermott, & Chun, 1997). Activity in this region, which has been named the “fusiform face area” (FFA), has been found to be bilateral in some cases and greater on the right in others (see Haxby, Hoffman, & Gobbini, 2000 for review). While upright faces activate the FFA specifically (Kanwisher et al., 1998), inverted faces have been


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shown to activate both the FFA and adjacent object recognition areas of the cortex (Haxby et al., 1999). Due to methodological restrictions, no fMRI study has examined face inversion effects with infants. However, with new technological advances, near-infrared spectroscopy (NIRS) has made it possible to study brain responses in infants using a method that is somewhat analogous to fMRI. NIRS is a neuroimaging method that can measure changes in hemodynamic activity in the brain using optic fibers placed over the scalp. This method is ideal for measuring brain activity in infants because, unlike fMRI, NIRS does not require the participant’s head to remain in a fixed position during stimulus presentation. Using NIRS, Otsuka et al. (2007) tested 10 infants between 5 and 8 months of age (mean age was around 6 months) while they viewed images of upright faces, inverted faces, and vegetables. The researchers observed increases in oxyhemoglobin and total hemoglobin concentrations in the right temporal area that began 3 s poststimulus onset for upright, but not inverted, faces. Otsuka et al. interpreted their findings as providing the first evidence for hemispheric differences in infants’ responses to face orientation. Two other NIRS studies have found inversion effects on 7- to 8-montholds’ hemodynamic responses to Arcimboldo faces (Kobayashi et al., 2012) and point-light displays of facial movements (Ichikawa, Kanazawa, Yamaguchi, & Kakigi, 2010). Together, these results provide evidence for some level of specialization in the brain for upright faces, as measured by NIRS, by at least 7–8 months of age. Future studies are needed to determine whether and how these neural responses become specialized for upright faces in infancy. 2.5.2 Event-Related Potentials In adults, the N170 is a face-sensitive ERP component characterized by a negative deflection in wave amplitude that occurs around 170 ms after the presentation of a face (Bentin, Allison, Puce, Perez, & McCarthy, 1996; George, Evans, Fiori, Davidoff, & Renault, 1996). Inverted faces elicit a later and larger N170 response relative to upright faces in adults (e.g., de Haan, Pascalis, & Johnson, 2002; Eimer & McCarthy, 1999; Rossion et al., 2000). In infants, two components have been found to respond differently to upright and inverted faces: the P400 and N290. Across two studies using the same task, the ERP responses of infants at 3, 6, and 12 months of age were measured while viewing full-color images of either female human

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or macaque monkey faces that appeared in both upright and inverted orientations. The results of these two studies show that the P400 is sensitive to the orientation of human faces and the species of faces by 3 months of age (Halit, De Haan, & Johnson, 2003). At 6 months, the P400 responds differentially to the orientation of faces regardless of species (de Haan et al., 2002). By 12 months, the P400 responds selectively to the orientation of human faces, but not monkey faces (Halit et al., 2003). In contrast, the N290 does not respond differentially to face orientation at 3 or 6 months, although at both ages it responds to species (de Haan et al., 2002; Halit et al., 2003; but see Peykarjou & Hoehl, 2013). By 12 months, however, the N290 responds differentially to the orientation of human faces, but not monkey faces, even though it also responds differentially based on species (Halit et al., 2003). In sum, the ERP results provide evidence for developmental changes in brain-related responses to upright and inverted faces. These findings show that by 3 months, the P400 is affected by face inversion. However, it is not until by 12 months of age that the N290 and P400 components have become selectively responsive to upright, human faces. It has been suggested that, combined, these two components signify the infant precursor of the adult N170 (e.g., Luyster, Wagner, Vogel-Farley, Tager-Flusberg, & Nelson, 2011). Collectively, these ERP findings point to the conclusion that over the first year infants are developing an expertise for upright faces that is beginning to more closely resemble that of adults.

3. MAKING SENSE OF IT ALL Our goal was to shed light on the developmental origins of the inversion effect. We reviewed studies that demonstrated effects of inversion on different aspects of infant face perception during the first year, including face preferences, face recognition, holistic and second-order configural face processing, scanning of faces, and neural correlates of face perception. Looking at all of these findings across the first year, a pattern emerges that suggests the effects of inversion become more robust during this period. By the latter half of the first year, infants have developed a foundational level of upright-face expertise. Below, we will summarize the main findings of our review.

3.1. Summary of Findings At birth, an effect of inversion is seen on infants’ face preference, but that preference appears to be driven by general perceptual biases, such as a bias for top-heavy stimuli (e.g., Simion et al., 2002; see also congruency bias


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Macchi Cassia et al., 2008). This effect is seen again at 3–5.5 months of age, but at this point appears to be more face specific, as is true for adults (Chien, 2011). By at least 4 months, inversion affects face-scanning patterns (Gallay et al., 2006). In 4- to 6-month-olds, the effect of inversion on face recognition is seen but only under certain conditions. The mixed findings suggest that the face recognition inversion effect is developing around this time but is not yet robust (Fagan, 1972; Turati et al., 2004; see also Rose, Jankowski, & Feldman, 2008). Inversion is shown to affect infants’ sensitivity to second-order configural changes of faces in 5-month-olds, but not 3-month-olds (Bhatt et al., 2005; Hayden et al., 2007). Results are mixed when sensitivity to upright and inverted Thatcherized faces have been tested in infants between 0 and 6 months (Bertin & Bhatt, 2004; Leo & Simion, 2009). On the other hand, at 7–8 months, but not at younger ages, there is clear evidence that inversion affects holistic face processing (Cashon & Cohen, 2004; Cohen & Cashon, 2001; Ferguson et al., 2009). Similarly, although inversion effects on scanning of faces are seen in 4.5- and 6.5month-olds, new scanning patterns emerge for upright, but not inverted, faces in 8- to 12.5-month-olds. Finally, near the end of the first year, evidence for upright-face specialization has been found in infants’ neural responses. By around 7–8 months of age, infants’ hemodynamic responses differ for upright and inverted faces, and by 12 months of age, infants’ N290 and P400 ERP components selectively respond to upright, human faces. Thus, the developmental origins of the inversion effect begin in the first few months of life and grow stronger over the first year, culminating in effects that are commonly thought to indicate adult-like expertise. In this way, the development of the inversion effect is similar to that of perceptual expertise that occurs in other areas such as language, music, and other aspects of face perception (for reviews, see Anzures et al., 2013; Cashon, 2011; Cashon & DeNicola, 2011; Maurer & Werker, 2014; Scott et al., 2007).

3.2. What Is the Role of Experience? 3.2.1 Does the Amount of Exposure Matter? Is the upright-face expertise that develops in infants over the first year driven by differential experience with upright and inverted faces? Studies from the past 10–15 years show that infants’ perception of different classes of faces is shaped by the kinds of faces they see. For instance, infants typically prefer female faces over male faces by 3 months of age. However, this preference is reversed when infants are primarily reared by male caregivers (Quinn, Yahr, Kuhn, Slater, & Pascalis, 2002). Also at 3 months, infants who live

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in a racially homogenous environment prefer faces of the race they see most often, whereas same-aged infants who live in a racially diverse environment show no preference (Bar-Haim, Ziv, Lamy, & Hodes, 2006; Kelly et al., 2005, 2007). Exposure to picture books of monkey faces between 6 and 9 months of age can preserve the ability to recognize monkey faces, which would normally disappear by 9 months (Pascalis, de Haan, & Nelson, 2002; Pascalis et al., 2005; Scott & Monesson, 2009). These studies illustrate that an infant’s face perception system can be shaped by experience. However, they do not address the specific question of whether differential experience with upright and inverted faces leads to upright-face expertise in infancy. Sugden and Moulson (2014) recently conducted an important study that directly investigated whether infants are exposed more to upright than inverted faces early in life. They used a tiny spy camera, concealed in a button on a headband on each infant’s head, to capture the visual environment typically experienced at home by 1- and 3-month-olds. Approximately 20–25 h of infant-perspective videos were obtained for each age group and coded second-by-second for the orientations of faces infants saw (i.e., upright, sideways, and inverted). On average, 25% of infants’ visual experience contained a face. Of that 25%, 87% of the faces appeared in the upright orientation, and only 6% of the faces were inverted. In other words, 22% of infants’ everyday visual experiences included upright faces. In contrast, only 1.5% of the input contained inverted faces. Thus, not only are infants born with a predisposition to attend to upright faces, but the vast majority of the faces they see are upright faces. Experience with upright faces is presumably an important factor in the development of infants’ upright-face expertise. Indirect evidence in support of this idea comes from recent studies of atypical populations. For instance, in contrast to how typical adults process faces, adults who experienced visual deprivation in the first few months of life (due to congenital cataracts) do not process upright and inverted faces differently (Robbins, Maurer, Hatry, Anzures, & Mondloch, 2012). On the other hand, toddlers who have Williams syndrome, a neurodevelopmental disorder that is associated with cognitive deficits but also with an extreme interest in faces (e.g., Mervis et al., 2003; Riby & Hancock, 2009), show an expert-like pattern of upright holistic face processing (Cashon, Ha, DeNicola, & Mervis, 2013). This finding was expected based on the assumption that their unique interest in faces would lead to extensive experience with upright faces and subsequent expertise. These findings are not direct evidence of the effects of experience


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on the development of upright-face expertise, yet they are consistent with such a view. In the future, researchers should directly investigate the role of experience in the development of infants’ upright-face expertise. 3.2.2 Does the Nature of Experience Matter? Is more visual experience with upright compared to inverted faces enough for infants to become upright-face-experts by the end of the first year? Mere exposure alone may be enough to produce some effects of inversion, such as the upright-face-specific preference that is found at 3 months. However, we hypothesize that the emerging expertise for upright faces found late in the first year is not driven by mere exposure, but instead by the increasing sophistication of infants’ social, physical, linguistic, and cognitive abilities. As infants develop, they elicit new responses from upright faces and engage with them in new ways, which ultimately results in infants’ perceiving upright, but not inverted, faces as important sources of linguistic, emotional, and social information. In other words, upright faces become meaningful as infants develop and become more capable of having complex social interactions. One example of this connection between developing motor and social abilities and face perception comes from a set of studies by Libertus and Needham (2011, 2014). They have shown that scaffolded reaching experiences (with “sticky mittens”) at 3 months of age can induce an increased interest in faces, and that this effect is dependent on social interaction from caregivers. Based on this evidence, they have suggested that the emergence of reaching behaviors early in the first year of life may bring about new opportunities for prosocial behaviors, such as object sharing. An example that specifically links motor development to upright-face processing comes from a study conducted by Cashon, Ha, Allen, et al. (2013). In their study, Cashon et al. measured the sitting abilities of 111 infants and classified them into four sitting stages based on age and sitting performance in the lab. Infants around 22–25 weeks old were classified as either nonsitters (infants who could not sit without support) or near sitters (infants who could not sit independently for 10 s). Infants around 27–32 weeks old were classified as either new sitters (infants who could sit independently for 10 s) or expert sitters (infants who could sit independently for 10 s and their parents reported they had been able to do so for 5 or more weeks). These researchers found that the nonsitters and expert sitters, the least and most developed in terms of sitting ability, processed the upright faces holistically, but near and new sitters did not. These results replicate the U-shaped results related to age found by Cashon and Cohen (2004) discussed earlier.

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However, in this more recent study, Cashon, Ha, DeNicola, et al. (2013) found a quadratic trend for sitting ability that was present even when age was held constant statistically. One of the explanations posited by the authors was that, “learning to sit independently may contribute to their learning about the significance of upright faces, which temporarily causes the face-processing system to become overloaded until it reorganizes with the new information about upright faces” (Cashon, Ha, DeNicola, et al., 2013, p. 807). One possibility is that with sitting, new social interactions become possible, which contributes to a deeper meaning of upright faces. In fact, recent evidence demonstrates that infants’ developing motor skills influence the ways that parents interact with their babies (Karasik, Tamis-LeMonda, & Adolph, 2014). The idea that new opportunities for social interaction influence the meaning of upright faces is further supported by indirect evidence from the facescanning results presented by Oakes and Ellis (2013). They found a shift in infants’ scanning patterns between 6.5 and 8 months of age that may be related to emerging language skills and, thus, an increase in the social significance of upright faces around this time. While this is only indirect evidence, collectively, these studies are consistent with the idea that there is an important link between social interaction and the development of uprightface expertise in infancy. Future studies are needed to explore how upright faces may become more social or meaningful to infants as they learn to sit. Future studies are also needed to explore ways in which the upright-face perception and sensorimotor systems may be connected (see Cashon, Ha, DeNicola, et al., 2013). This could help shed light on how upright faces become more meaningful to infants.

3.3. Conclusion In this chapter, we explored the developmental origins of the face inversion effect. Although infants show a bias to attend to upright faces at birth, the face inversion effect is far from fully formed. We reviewed results showing that inversion affects many different aspects of face perception in infants. Near the end of the first year, infants have clearly developed a more adult-like expertise for upright faces. This is not to say that the inversion effect is mature at the end of the first year and stops developing. In fact, there is considerable evidence indicating that during childhood uprightface expertise grows stronger, likely because of improvements in faceprocessing skills and other cognitive abilities (e.g., Carey & Diamond,


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1977; Flin, 1985; de Heering, Rossion, & Maurer, 2012). However, the improvements seen later in development are incremental relative to the gains made over the first year.

REFERENCES Anzures, G., Quinn, P. C., Pascalis, O., Slater, A. M., Tanaka, J. W., & Lee, K. (2013). Developmental origins of the other-race effect. Current Directions in Psychological Science, 22, 173–178. http://dx.doi.org/10.1177/0963721412474459. Bar-Haim, Y., Ziv, T., Lamy, D., & Hodes, R. M. (2006). Nature and nurture in own-race face processing. Psychological Science, 17, 159–163. http://dx.doi.org/10.1111/j.14679280.2006.01679.x. Bartlett, J. C., & Searcy, J. (1993). Inversion and configuration of faces. Cognitive Psychology, 25, 281–316. http://dx.doi.org/10.1006/cogp.1993.1007. Bednar, J. A., & Miikkulainen, R. (2003). Learning innate face preferences. Neural Computation, 15, 1525–1557. http://dx.doi.org/10.1162/089976603321891792. Bentin, S., Allison, T., Puce, A., Perez, E., & McCarthy, G. (1996). Electrophysiological studies of face perception in humans. Journal of Cognitive Neuroscience, 8, 551–565. http://dx.doi.org/10.1162/jocn.1996.8.6.551. Bertin, E., & Bhatt, R. S. (2004). The Thatcher illusion and face processing in infancy. Developmental Science, 7(4), 431–436. Bhatt, R. S., Bertin, E., Hayden, A., & Reed, A. (2005). Face processing in infancy: Developmental changes in the use of different kinds of relational information. Child Development, 76, 169–181. http://dx.doi.org/10.1111/j.1467-8624.2005.00837.x. Bhatt, R. S., Hayden, A., Reed, A., Bertin, E., & Joseph, J. (2006). Infants’ perception of information along object boundaries: Concavities versus convexities. Journal of Experimental Child Psychology, 94, 91–113. Brooks, R. M., & Goldstein, A. G. (1963). Recognition by children of inverted photographs of faces. Child Development, 34, 1033–1040. Carey, S., & Diamond, R. (1977). From piecemeal to configurational representation of faces. Science, 195, 312–314. Cashon, C. H. (2011). Development of specialized face perception in infants: An information-processing perspective. In L. M. Oakes, C. H. Cashon, M. Casasola, & D. H. Rakison (Eds.), Infant perception and cognition: Recent advances, emerging theories, and future directions (pp. 69–83). New York, NY: Oxford. Cashon, C. H., & Cohen, L. B. (2003). The construction, deconstruction, and reconstruction of infant face perception. In O. Pascalis & A. Slater (Eds.), The development of face processing in infancy and early childhood: Current perspectives (pp. 55–68). Hauppauge, NY: Nova Science Publishers. Cashon, C. H., & Cohen, L. B. (2004). Beyond U-shaped development in infants’ processing of faces: An information-processing account. Journal of Cognition and Development, 5, 59–80. http://dx.doi.org/10.1207/s15327647jcd0501_4. Cashon, C. H., & DeNicola, C. A. (2011). Is perceptual narrowing too narrow? Journal of Cognition and Development, 12, 159–162. http://dx.doi.org/10.1080/15248372.2011.563483. Cashon, C. H., Ha, O. R., Allen, C. L., & Barna, A. C. (2013). A U-shaped relation between sitting ability and upright face processing in infants. Child Development, 84, 802–809. Cashon, C. H., Ha, O.-R., DeNicola, C. A., & Mervis, C. B. (2013). Toddlers with Williams syndrome process upright but not inverted faces holistically. Journal of Autism and Developmental Disorders, 43, 2549–2557. http://dx.doi.org/10.1007/s10803-0131804-0.

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Chien, S. (2011). No more top-heavy bias: Infants and adults prefer upright faces but not topheavy geometric or face-like patterns. Journal of Vision, 11, 1–14. http://dx.doi.org/ 10.1167/11.6.13. Cohen, L. B. (2004). Uses and misuses of habituation and related preference paradigms. Infant and Child Development, 13, 349–352. http://dx.doi.org/10.1002/icd.355. Cohen, L. B., & Cashon, C. H. (2001). Do 7-month-old infants process independent features or facial configurations? Infant and Child Development, 10, 83–92. http://dx.doi.org/10. 1002/icd.250. de Haan, M., Pascalis, O., & Johnson, M. H. (2002). Specialization of neural mechanisms underlying face recognition in human infants. Journal of Cognitive Neuroscience, 14, 199–209. http://dx.doi.org/10.1162/089892902317236849. de Heering, A., Rossion, B., & Maurer, D. (2012). Developmental changes in face recognition during childhood: Evidence from upright and inverted faces. Cognitive Development, 27, 17–27. Diamond, R., & Carey, S. (1986). Why faces are and are not special: An effect of expertise. Journal of Experimental Psychology. General, 115, 107–117. Eimer, M., & McCarthy, R. A. (1999). Prosopagnosia and structural encoding of faces: Evidence from event-related potentials. Neuroreport, 10, 255–259. Fagan, J. F. (1972). Infants’ recognition memory for faces. Journal of Experimental Child Psychology, 14, 453–476. Fagan, J. F., & Shepherd, P. A. (1979). Infants’ perception of face orientation. Infant Behavior & Development, 2, 227–233. http://dx.doi.org/10.1016/S0163-6383(79)80028-4. Fantz, R. L. (1961). The origin of form perception. Scientific American, 204, 66–72. http://dx. doi.org/10.1038/scientificamerican0561-66. Ferguson, K. T., Kulkofsky, S., Cashon, C. H., & Casasola, M. (2009). The development of specialized processing of own-race faces in infancy. Infancy, 14, 263–284. http://dx.doi. org/10.1080/15250000902839369. Flin, R. H. (1985). Development of face recognition: An encoding switch? British Journal of Psychology, 76, 123–134. Gallay, M., Baudouin, J., Durand, K., Lemoine, C., & Lećuyer, R. (2006). Qualitative differences in the exploration of upright and upside-down faces in four-month-old infants: An eye-movement study. Child Development, 77, 984–996. http://dx.doi.org/10.1111/ j.1467-8624.2006.00914.x. George, N., Evans, J., Fiori, N., Davidoff, J., & Renault, B. (1996). Brain events related to normal and moderately scrambled faces. Cognitive Brain Research, 4, 65–76. http://dx.doi. org/10.1016/0926-6410(95)00045-3. Goren, C., Sarty, M., & Wu, P. (1975). Visual following and pattern discrimination of facelike stimuli by newborn infants. Pediatrics, 56, 544–549. Halit, H., De Haan, M., & Johnson, M. H. (2003). Cortical specialisation for face processing: Face-sensitive event-related potential components in 3- and 12-month-old infants. NeuroImage, 19, 1180–1193. Haxby, J., Hoffman, E., & Gobbini, M. (2000). The distributed human neural system for face perception. Trends in Cognitive Sciences, 4, 223–233. Haxby, J. V., Ungerleider, L. G., Clark, V. P., Schouten, J. L., Hoffman, E. A., & Martin, A. (1999). The effect of face inversion on activity in human neural systems for face and object perception. Neuron, 22, 189–199. http://dx.doi.org/10.1016/S0896-6273(00) 80690-X. Hayden, A., Bhatt, R. S., Reed, A., Corbly, C. R., & Joseph, J. E. (2007). The development of expert face processing: Are infants sensitive to normal differences in second-order relational information? Journal of Experimental Child Psychology, 97, 85–98. http://dx. doi.org/10.1016/j.jecp.2007.01.004.


147

Hunter, M. A., & Ames, E. W. (1988). A multifactor model of infant preferences for novel and familiar stimuli. In C. Rovee-Collier & L. P. Lipsitt (Eds.), Advances in infancy research: Vol. 5. (pp. 69–95). Westport, CT: Ablex Publishing. Ichikawa, H., Kanazawa, S., Yamaguchi, M. K., & Kakigi, R. (2010). Infant brain activity while viewing facial movement of point-light displays as measured by near-infrared spectroscopy (NIRS). Neuroscience Letters, 482, 90–94. Johnson, M. H. (2005). Subcortical face processing. Nature Reviews. Neuroscience, 6, 766–786. http://dx.doi.org/10.1038/nrn1766. Johnson, M. H. (2011). Face processing as a brain adaptation at multiple timescales. The Quarterly Journal of Experimental Psychology, 64, 1873–1888. http://dx.doi.org/ 10.1080/17470218.2011.590596. Johnson, M. H., Dziurawiec, S., Ellis, H., & Morton, J. (1991). Newborns’ preferential tracking of face-like stimuli and its subsequent decline. Cognition, 40, 1–19. http://dx. doi.org/10.1016/0010-0277(91)90045-6. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. The Journal of Neuroscience, 17, 4302–4311. Kanwisher, N., Tong, F., & Nakayama, K. (1998). The effect of face inversion on the human fusiform face area. Cognition, 68, B1–B11. Karasik, L. B., Tamis-LeMonda, C. S., & Adolph, K. E. (2014). Crawling and walking infants elicit different verbal responses from mothers. Developmental Science, 17, 388–395. Kato, M., & Konishi, Y. (2013). Where and how infants look: The development of scan paths and fixations in face perception. Infant Behavior & Development, 36, 32–41. http://dx.doi. org/10.1016/j.infbeh.2012.10.005. Kelly, D. J., Liu, S., Ge, L., Quinn, P. C., Slater, A. M., Lee, K., et al. (2007). Cross-race preferences for same-race faces extend beyond the African versus Caucasian contrast in 3-month-old infants. Infancy, 11, 87–95. Kelly, D. J., Quinn, P. C., Slater, A. M., Lee, K., Gibson, A., Smith, M., et al. (2005). Threemonth-olds, but not newborns, prefer own-race faces. Developmental Science, 8, F31–F36. Kobayashi, M., Otsuka, Y., Nakato, E., Kanazawa, S., Yamaguchi, M. K., & Kakigi, R. (2012). Do infants recognize the Arcimboldo images as faces? Behavioral and nearinfrared spectroscopic study. Journal of Experimental Child Psychology, 111, 22–36. Leo, I., & Simion, F. (2009). Face processing at birth: A Thatcher illusion study. Developmental Science, 12, 492–498. http://dx.doi.org/10.1111/j.1467-7687.2008.00791.x. Libertus, K., & Needham, A. (2011). Reaching experience increases face preference in 3-month-old infants. Developmental Science, 14, 1355–1364. Libertus, K., & Needham, A. (2014). Encouragement is nothing without control: Factors influencing the development of reaching and face preference. Journal of Motor Learning and Development, 2, 16–27. Luyster, R. J., Wagner, J. B., Vogel-Farley, V., Tager-Flusberg, H., & Nelson, C. A., III (2011). Neural correlates of familiar and unfamiliar face processing in infants at risk for autism spectrum disorders. Brain Topography, 24, 220–228. Macchi Cassia, V., Turati, C., & Simion, F. (2004). Can a nonspecific bias toward top-heavy patterns explain newborns’ face preference? Psychological Science, 15, 379–383. http://dx. doi.org/10.1111/j.0956-7976.2004.00688.x. Macchi Cassia, V., Valenza, E., Simion, F., & Leo, I. (2008). Congruency as a nonspecific perceptual property contributing to newborns’ face preference. Child Development, 79, 807–820. Maurer, D., Le Grand, R., & Mondloch, C. J. (2002). The many faces of configural processing. Trends in Cognitive Sciences, 6, 255–260. http://dx.doi.org/10.1016/S13646613(02)01903-4.

148


Maurer, D., & Werker, J. F. (2014). Perceptual narrowing during infancy: A comparison of language and faces. Developmental Psychobiology, 56, 154–178. http://dx.doi.org/ 10.1002/dev.21177. McGurk, H. (1970). The role of object orientation in infant perception. Journal of Experimental Child Psychology, 9, 363–373. Mervis, C. B., Morris, C. A., Klein-Tasman, B. P., Bertrand, J., Kwitny, S., Appelbaum, L. G., et al. (2003). Attentional characteristics of infants and toddlers with Williams syndrome during triadic interactions. Developmental Neuropsychology, 23, 243–268. Mondloch, C. J., Lewis, T. L., Budreau, D., Maurer, D., Dannemiller, J. L., Stephens, B. R., et al. (1999). Face perception during early infancy. Psychological Science, 10, 419–422. http://dx.doi.org/10.1111/1467-9280.00179. Morton, J., & Johnson, M. H. (1991). CONSPEC and CONLERN: A two-process theory of infant face recognition. Psychological Review, 98, 164–181. http://dx.doi.org/ 10.1037/0033-295X.98.2.164. Oakes, L. M., & Ellis, A. E. (2013). An eye-tracking investigation of developmental changes in infants’ exploration of upright and inverted human faces. Infancy, 18, 134–148. http:// dx.doi.org/10.1111/j.1532-7078.2011.00107.x. Otsuka, Y., Nakato, E., Kanazawa, S., Yamaguchi, M. K., Watanabe, S., & Kakigi, R. (2007). Neural activation to upright and inverted faces in infants measured by near infrared spectroscopy. NeuroImage, 34, 399–406. Pascalis, O., de Haan, M., & Nelson, C. A. (2002). Is face processing species-specific during the first year of life? Science, 296, 1321–1323. http://dx.doi.org/10.1126/ science.1070223. Pascalis, O., Scott, L. S., Kelly, D. J., Shannon, R. W., Nicholson, E., Coleman, M., et al. (2005). Plasticity of face processing in infancy. Proceedings of the National Academy of Sciences of the United States of America, 102, 5297–5300. Peykarjou, S., & Hoehl, S. (2013). Three-month-olds’ brain responses to upright and inverted faces and cars. Developmental Neuropsychology, 38, 272–280. http://dx.doi. org/10.1080/87565641.2013.786719. Quinn, P., & Tanaka, J. (2009). Infants’ processing of featural and configural information in the upper and lower halves of the face. Infancy, 14, 474–487. http://dx.doi.org/ 10.1080/15250000902994248. Quinn, P. C., Yahr, J., Kuhn, A., Slater, A. M., & Pascalis, O. (2002). Representation of the gender of human faces by infants: A preference for female. Perception, 31, 1109–1122. http://dx.doi.org/10.1068/p3331. Rakover, S. S. (1999). Thompson’s Margaret Thatcher illusion: When inversion fails. Perception, 28, 1227–1230. http://dx.doi.org/10.1068/p2774. Rakover, S. S. (2013). Explaining the face-inversion effect: The face-scheme incompatibility (FSI) model. Psychonomic Bulletin & Review, 20, 665–692. http://dx.doi.org/10.3758/ s13423-013-0388-1. Riby, D. M., & Hancock, P. J. B. (2009). Do faces capture the attention of individuals with Williams syndrome or autism? Evidence from tracking eye movements. Journal of Autism and Developmental Disorders, 39, 421–431. http://dx.doi.org/10.1007/S10803-0080641-Z. Robbins, R. A., Maurer, D., Hatry, A., Anzures, G., & Mondloch, C. J. (2012). Effects of normal and abnormal visual experience on the development of opposing aftereffects for upright and inverted faces. Developmental Science, 15, 194–203. http://dx.doi.org/ 10.1111/j.1467-7687.2011.01116.x. Rose, S., Jankowski, J., & Feldman, J. (2008). The inversion effect in infancy: The role of internal and external features. Infant Behavior & Development, 31, 470–480. http://dx.doi. org/10.1016/j.infbeh.2007.12.015.


149

Rossion, B. (2008). Picture-plane inversion leads to qualitative changes of face perception. Acta Psychologica, 128, 274–289. http://dx.doi.org/10.1016/j.actpsy.2008.02.003. Rossion, B., & Gauthier, I. (2002). How does the brain process upright and inverted faces? Behavioral and Cognitive Neuroscience Reviews, 1, 63–75. http://dx.doi.org/10.1177/ 1534582302001001004. Rossion, B. B., Gauthier, I. I., Tarr, M. J., Despland, P. P., Bruyer, R. R., Linotte, S. S., et al. (2000). The N170 occipito-temporal component is delayed and enhanced to inverted faces but not to inverted objects: An electrophysiological account of face-specific processes in the human brain. Neuroreport, 11, 69–74. http://dx.doi.org/ 10.1097/00001756-200001170-00014. Schwarzer, G., Zauner, N., & Jovanovic, B. (2007). Evidence of a shift from featural to configural face processing in infancy. Developmental Science, 10, 452–463. Scott, L. S., & Monesson, A. (2009). The origin of biases in face perception. Psychological Science, 20, 676–680. http://dx.doi.org/10.1111/j.1467-9280.2009.02348.x. Scott, L. S., Pascalis, O., & Nelson, C. A. (2007). A domain-general theory of the development of perceptual discrimination. Current Directions in Psychological Science, 16, 197–201. http://dx.doi.org/10.1111/j.1467-8721.2007.00503.x. Simion, F., Leo, I., Turati, C., Valenza, E., & Dalla Barba, B. (2007). How face specialization emerges in the first months of life. Progress in Brain Research, 164, 169–185. http://dx.doi. org/10.1111/j.1467-7687.2007.00599.x. Simion, F., Macchi Cassia, V., Turati, C., & Valenza, E. (2001). The origins of face perception: Specific versus non-specific mechanisms. Infant and Child Development, 10, 59–66. Simion, F., Macchi Cassia, V., Turati, C., & Valenza, E. (2003). Non-specific perceptual biases at the origins of face processing. In O. Pascalis & A. Slater (Eds.), The development of face processing in infancy and early childhood: Current perspectives (pp. 13–25). Hauppauge, NY: Nova Science Publishers. Simion, F., Valenza, E., Macchi Cassia, V., Turati, C., & Umilta`, C. (2002). Newborns’ preference for up-down asymmetrical configurations. Developmental Science, 5, 427–434. http://dx.doi.org/10.1111/1467-7687.00237. Slater, A., Quinn, P. C., Hayes, R., & Brown, E. (2000). The role of facial orientation in newborn infants’ preference for attractive faces. Developmental Science, 3, 181–185. http://dx.doi.org/10.1111/1467-7687.00111. Sugden, N. A., & Moulson, M. C. (2014). It may be a topsy-turvy world for infants, but faces are still upright most of the time. In Poster presented at the biennial international conference on infant studies, Berlin. Tanaka, J. W. (2001). The entry point of face recognition: Evidence for face expertise. Journal of Experimental Psychology, 130, 534–543. http://dx.doi.org/10.1037/0096-3445.130.3.534. Thompson, P. (1980). Margaret Thatcher: A new illusion. Perception, 9, 483–484. Thompson, L. A., Madrid, V., Westbrook, S., & Johnston, V. (2001). Infants attend to second-order relational properties of faces. Psychonomic Bulletin & Review, 8, 769–777. Turati, C. (2004). Why faces are not special to newborns: An alternative account of the face preference. Current Directions in Psychological Science, 13, 5–8. http://dx.doi.org/10.1111/ j.0963-7214.2004.01301002.x. Turati, C., Di Giorgio, E., Bardi, L., & Simion, F. (2010). Holistic face processing in newborns, 3-month-old infants, and adults: Evidence from the composite face effect. Child Development, 81, 1894–1905. http://dx.doi.org/10.1111/j.1467-8624.2010.01520.x/full. Turati, C., Sangrigoli, S., Ruel, J., & de Schonen, S. (2004). Evidence of the face inversion effect in 4-month-old infants. Infancy, 6, 275–297. http://dx.doi.org/10.1207/ s15327078in0602_8. Turati, C., Simion, F., Milani, I., & Umilta`, C. (2002). Newborns’ preference for faces: What is crucial? Developmental Psychology, 38, 875–882. http://dx.doi.org/10.1037/00121649.38.6.875.

150


Turati, C., Valenza, E., Leo, I., & Simion, F. (2005). Three-month-olds’ visual preference for faces and its underlying visual processing mechanisms. Journal of Experimental Child Psychology, 90, 255–273. http://dx.doi.org/10.1016/j.jecp.2004.11.001. Valentine, T. (1988). Upside-down faces: A review of the effect of inversion upon face recognition. British Journal of Psychology, 79, 471–491. http://dx.doi.org/10.1111/j.20448295.1988.tb02747.x. Valenza, E., Simion, F., Macchi Cassia, V., & Umilta`, C. (1996). Face preference at birth. Journal of Experimental Psychology Human Perception and Performance, 22, 892–903. http://dx.doi.org/10.1037/0096-1523.22.4.892. Watson, J. S. (1966). Perception of object orientation in infants. Merrill-Palmer Quarterly, 12, 73–94. Yin, R. (1969). Looking at upside-down faces. Journal of Experimental Psychology, 81, 141–145. Zieber, N., Kangas, A., Hock, A., Hayden, A., Collins, R., Bada, H., et al. (2013). Perceptual specialization and configural face processing in infancy. Journal of Experimental Child Psychology, 116, 625–639. http://dx.doi.org/10.1016/j.jecp.2013.07.007.

CHAPTER FIVE

Early Testimonial Learning: Monitoring Speech Acts and Speakers Elizabeth Stephens*, Sarah Suarez*, Melissa Koenig*,1

*Institute of Child Development, University of Minnesota, 51 E. River Pkwy, Minneapolis, MN 55455 1 Corresponding author: e-mail address: [email protected]

Contents 1. Epistemic Vigilance: Can Testimonial Learning Exist Without It? 2. Children's Evaluations of Speaker Messages 2.1 The Developmental Precursors to Coherence-Checking 2.2 Children's Treatment of Labeling Errors 2.3 Children's Treatment of Grammatical Errors 2.4 Children's Treatment of Inconsistent, Illogical, and Improbable Statements 2.5 Children's Treatment of Factual and Episodic Errors 2.6 When Children Encounter Message Conflicts: Other Observations 2.7 Interim Conclusion 3. Children's Evaluations of Speakers 3.1 Natural Pedagogy 3.2 Core Dimensions of Speakers 3.3 Negativity Bias 4. Concluding Thoughts Acknowledgment References

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Abstract Testimony provides children with a rich source of knowledge about the world and the people in it. However, testimony is not guaranteed to be veridical, and speakers vary greatly in both knowledge and intent. In this chapter, we argue that children encounter two primary types of conflicts when learning from speakers: conflicts of knowledge and conflicts of interest. We review recent research on children's selective trust in testimony and propose two distinct mechanisms supporting early epistemic vigilance in response to the conflicts associated with speakers. The first section of the chapter focuses on the mechanism of coherence checking, which occurs during the process of message comprehension and facilitates children's comparison of information communicated through testimony to their prior knowledge, alerting them to inaccurate, inconsistent, irrational, and implausible messages. The second section focuses on source-monitoring processes. When children lack relevant prior knowledge with which to evaluate Advances in Child Development and Behavior, Volume 48 ISSN 0065-2407 http://dx.doi.org/10.1016/bs.acdb.2014.11.004

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testimonial messages, they monitor speakers themselves for evidence of competence and morality, attending to cues such as confidence, consensus, access to information, prosocial and antisocial behavior, and group membership.

Much of early learning depends on others, and the social transmission of information presents us with rich opportunities to learn from and about others. Socially transmitted information is often communicated through others’ testimony (i.e., those statements that present their contents as true). Although all listeners of testimony, including children, are interested in acquiring reliable information, the competence and intentions of speakers are more variable, and may not align with listeners’ interests (Faulkner, 2011; Williams, 2000). Here, we argue that all listeners encounter two primary types of conflict in speakers: conflicts of knowledge and conflicts of interest. To avoid the risks engendered by these conflicts, listeners must exercise some degree of epistemic vigilance (Sperber et al., 2010). In this chapter, we focus on two sets of cognitive mechanisms that support vigilance towards the conflicts presented by speakers. As we discuss in the first half of the chapter, children protect themselves against conflicts of information through the processes of message comprehension: some initial “coherence-checking” occurs whenever children interpret a new message, alerting them to conflicts between the message and their already established knowledge and beliefs. In the second half of the chapter, we discuss a separate set of mechanisms of vigilance engaged through source monitoring: when lacking relevant prior knowledge, children actively monitor speakers themselves for indicators of incompetence or conflicts of interest.

1. EPISTEMIC VIGILANCE: CAN TESTIMONIAL LEARNING EXIST WITHOUT IT? As argued by Sperber et al. (2010), the stable existence of communication depends upon benefits for senders and receivers alike (Sperber & Wilson, 1986). That is, communication serves the purposes of speakers who engage in conversations to influence listeners, and listeners who are dependent on communicated information. However, given the asymmetric goals of speakers and listeners, and the resulting epistemic vulnerability of listeners, most agree that true testimonial knowledge depends on the presence or practice of epistemic vigilance. For example, in classical epistemology, reductionists have argued that beliefs acquired via testimony do not

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automatically qualify as knowledge in and of themselves. Rather, testimonial beliefs must be justified by other true beliefs that have been acquired by testimony, perception, inference, or memory (Adler, 2002; Coady, 1992; Faulkner, 2003; Fricker, 1995; Hume, 1748/1995; van Cleve, 2006). Reductionists further argue that knowledge from others’ testimony is made possible by the faculties of the mind that support our experiences: our ability to monitor the speaker’s beliefs and intentions, our attention to prior correspondences between messages and facts, and our licensed inferences about future correspondences. In contrast, antireductionists, such as Reid (1764/1983), view our trust in testimonial information as intrinsically justified, similar to our trust in information gained from perception, inference, or memory (Coady, 1992; Reid, 1764/1983). Modern antireductionist perspectives argue that listeners are entitled to accept testimony unless there is clear reason not to do so, such as evidence of conflictual message contents or suspicious sources (Davidson, 1984; Lewis, 1969). Both views hold the minimal requirement that candidates for testimonial knowledge must come to their own conclusions about speaker trustworthiness (McMyler, 2007). Reductionists require listeners to entertain positive reasons for their beliefs, whereas antireductionists require counterfactual sensitivity to negative evidence against testimony (Goldberg & Henderson, 2006). In line with these ideas, we are interested in the various ways in which testimonial learning is supported by processes of epistemic vigilance (Cole, Harris, & Koenig, 2012; Sperber et al., 2010; Woolley & Ghossainy, 2013), and what such processes involve. In other words, we are trying to capture what may be involved in the “species of reasoning” that Hume argued was so common, useful and necessary to human life (Hume, 1748/1995). For Hume, “the maxim by which we commonly conduct ourselves in our reasonings is. . .that where there is an opposition of arguments, we ought to give preference to such as are founded on the greatest number of past observations” (p. 124). This sage advice depends on evaluating our observations, but what kinds of observations should we evaluate? And how should they inform our judgments of whether to accept a given claim? As we review here, our ability to critically evaluate testimonial messages given what we know is part of what allows us to accept communicated information as trustworthy. It also allows us to mistrust the source when appropriate, either on moral or intellectual grounds. When the risks of trusting information are low—for example, when communicated information is of little relevance or import to us—perhaps vigilance is as well. However, when the risks of accepting misinformation are significant and the issue is one that we care about, perhaps vigilance is heightened.

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What, exactly, are these risks? Sperber et al. (2010) argued that “the major problem posed by communicated information has to do not with the competence of others, but with their interests and their honesty (p. 361),” and, further, that the existence of dishonesty is what makes epistemic vigilance indispensable in communicative contexts. Although we agree that the problem of deliberate misinformation characterizes an important risk, and that the consequences of accepting such information are potentially grave, we disagree that this is the central risk encountered when learning from testimony. Instead, the central, and likely first, risk encountered by testimonial learners is that of well-meaning but less-than-fully knowledgeable informants. Speakers vary greatly in their competence, yet are free to share the information they desire, to whomever they wish, and in whatever way they choose. Even within the context of our most intimate relationships (e.g., with partners, parents, children, close friends), where trust is high and conflicts of interest are relatively minimal, the risk of misinformation based on poorly informed sources remains. Testimony is only as reliable as the beliefs of the speakers communicating it. Because of such risks, epistemic defense mechanisms are necessary even early in life, at the start of language comprehension. As reviewed in the following section, children rely on one such defense mechanism, referred to by some as “coherence-checking,” to compare incoming information to existing knowledge and beliefs (Mercier & Sperber, 2009, 2011).

2. CHILDREN'S EVALUATIONS OF SPEAKER MESSAGES 2.1. The Developmental Precursors to CoherenceChecking Before infants can detect problems or conflicts in testimonial messages, are they completely vulnerable to the risks of misinformation? Recent studies point to early protective mechanisms that likely precede children’s detection of problematic messages. Work on early social referencing indicates that infants invest more attention in, and adjust their behavior more in accordance with, individuals who respond to their environment in a coherent manner. Findings on infant gaze-following behaviors indicate that referential expectations about speaker gestures and speech develop early in life, as does the expectation that these cues will be interrelated or co-referential. By 6 months, infants selectively follow the gaze of an agent who shared direct eye contact with them or spoke to them in infant-directed speech (Senju & Csibra, 2008). By 12 months, infants follow the gaze of novel entities that


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feature a face, show evidence of contingent behavior, or both ( Johnson, Slaughter, & Carey, 1998). When behaviors such as gaze and deictic gestures co-occur with a speaker’s act of labeling, Gliga and Csibra (2009) demonstrate that 13-month-olds expect such actions to be co-referential and to converge on a single object. How do infants react to speakers who violate these referential expectations? Tummeltshammer, Wu, and Kirkham (2013) exposed 8-month-old infants to one face that consistently gazed in the direction of target objects in varying corners of a square screen, and a second face that inconsistently gazed in the direction of the objects. At test, infants were presented with one of the original faces gazing at a corner of an otherwise empty screen. They found that infants were more likely to follow the gaze of the previously consistent gazer. Thus, it appears that by 8-months of age, infants invest more of their attention in individuals who respond contingently to their environment than individuals who fail to do so. Tummeltshammer, Wu, Sobel, and Kirkham (2014) extended these findings with a similar set of studies that compared infants’ reactions to faces or arrows that varied in reliability. Infants consistently searched in boxes containing animal animations that were either reliably or unreliably cued by faces or arrows. When presented with novel boxes, only those exposed to reliable faces followed the cues. Interesting questions remain for future research regarding the types of characteristics (animacy, presence of eyes, face, contingent behavior) that elicit infants’ expectations of future reliability. There is also evidence that infants have expectations about emotional cues and take them into account when engaging in gaze-following behaviors: Chow, Poulin-Dubois, and Lewis (2008) found that 14-month old infants preferred to follow the gaze of a looker who had expressed happiness looking inside a container with a toy inside, rather than one who expressed happiness at an empty container. A second experiment found that infants did not generalize their knowledge of looker unreliability to another, “naı¨ve” looker, indicating that they adjusted their gaze-following behavior in accordance with their experience with a particular individual. Based on this constellation of findings, it seems that contingent gaze might be one of the earliest behaviors used by learners to infer a model’s general competence. Specifically, infants’ existing expectations about the coherent use of gaze and other related referential and emotional cues seem to guide their attention, and perhaps even their learning. Many interesting questions remain. For example, it is unclear whether infants might put more stock in certain cues than others. Furthermore, whether infants’ treatment of

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incommensurate referential cues stems from their representations of agents, and the causal links between these various cueing behaviors, remains an important empirical question. As 6- to 9-month-old infants use these skills to develop a working understanding of a few common nouns (Bergelson & Swingley, 2012), they place themselves in a position to evaluate certain claims made by speakers that include those nouns. In the following subsections, we focus on infants’ and young children’s applications of their prior knowledge (i.e., “coherence checking”) by reviewing existing research on children’s treatment and evaluations of speaker messages. We focus on children’s treatment of messages containing labeling errors, grammatical errors, inconsistency, among other types of errors, and illustrate how children’s growing knowledge base and logical understanding is employed in their speaker evaluations.

2.2. Children's Treatment of Labeling Errors Studies of very young children’s detection of and reactions to overt labeling errors point to the existence of some “automatic” processes of vigilance by 16 months of age. Early in language acquisition, infants detect messages that contradict what they know and explicitly reject and deny such messages. Koenig and Echols (2003) found that 16-month-old infants looked significantly longer at a speaker when she labeled familiar objects incorrectly than when she did so correctly. In addition to looking longer at false labeling events, many infants made explicit attempts to correct the inaccurate labeler. In work by Gliga and Csibra (2009), 13-month-old infants watched while a speaker directed their gaze to, pointed at, and labeled an object hidden behind a screen. When the screen was removed, and the previously hidden object revealed, infants looked longer when the object failed to correspond to the comments and referential actions of the speaker. Thus, well before they have accumulated a large vocabulary, infants appreciate the referential nature of pointing and naming, and demonstrate behaviors to indicate they expect such signals to convey accurate and relevant information about objects. When do infants apply information about speaker accuracy to learning contexts? Koenig and Woodward (2010) found that 24-month-olds treated inaccuracy as a feature of a particular individual and formed only short-lived and fragile word-object representations when presented with novel labels from a previously inaccurate labeler. Similar effects are seen in preschoolers who fail to retain novel word-object links from speakers who declare their


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ignorance (Sabbagh & Shafman, 2009). In combination with the heightened attention infants give to false labelers (Gliga & Csibra, 2009; Koenig & Echols, 2003), children’s poor memory for the messages that incompetent and ignorant speakers provide raises important questions concerning children’s memory for problematic speakers themselves. Perhaps young children have better memories for the identities of incompetent speakers than for competent ones. Consistent with this possibility, Corriveau and Harris (2009b) found that 3- and 4-year-olds maintained their avoidance of a previously inaccurate labeler after a week’s delay, suggesting that they can form enduring memories of speakers based on their labeling performance. These findings suggest that the detection of inaccurate messages might lead to enhanced memory for incompetent sources and poor memory for the information they present. Children’s early employment of coherence-checking goes beyond the rejection or negation of messages containing labeling errors. It also influences children’s learning decisions. For example, Koenig, Cle´ment, and Harris (2004) introduced 3- and 4-year-old children to two labelers, one who consistently labeled familiar objects correctly, and another who labeled these objects incorrectly. The speakers then presented conflicting novel labels in reference to unfamiliar objects. The authors found that children who were able to monitor the speakers’ labeling accuracy selectively learned the novel labels provided by the more reliable informant. Koenig and Harris (2005) found that 3- and 4-year-olds also preferred a previously accurate labeler over one who expressed ignorance. Quine and Ullian (1970) argue that our ability to reason about the processes contributing to a speaker’s falsehood underlies our “temerity” in treating a statement as false. Such reasoning can also be seen in young children, whose selective learning is not only influenced by the presence of testimonial errors, but also by the magnitude of the errors. For example, Kondrad and Jaswal (2012) found that 4- and 5-year-olds preferred to learn new labels from an inaccurate informant whose labeling errors were “closer” to a correct answer than from one whose answers were bizarre (e.g., referring to a partially obscured picture of a comb as a “brush” vs. as a “thunderstorm”). Relatedly, Einav and Robinson (2010) demonstrated that 4- to 7-year-olds’ sensitivity to error magnitude, as evidenced by a selective preference to learn from a speaker who made “smaller” errors, depended on their age and the error type: for quantifiable numerical errors (i.e., an incorrect number of dots on a card), children of all ages displayed sensitivity to error magnitude. However, only the 6- and 7-year-olds displayed such

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sensitivity to semantic errors varying in magnitude (e.g., calling a lion “a tiger” vs. “a mouse”). These findings indicate that for label learning, children’s semantic knowledge serves as a reference against which incoming information is compared. They also indicate that children have ideas about what counts as a more or less plausible semantic error. Coherence checking is not just a “violation-detection” device that treats all violations equally— instead, it is informed by and sensitive to a growing body of semantic, factual, and psychological knowledge. Children’s growing knowledge base includes a developing understanding of agents and minds. For example, Koenig and Echols (2003) found that false labeling was treated differently by 16-month-olds depending on whether it came from a speaker looking at the objects, from a speaker looking away from the objects, or from a machine. Infants manifested their surprise by looking longest towards the incorrect labeler who had visual access to the objects and the correct labeler who did not have visual access to the objects. Nurmsoo and Robinson (2009a)’s findings also indicate how children’s developing social knowledge and labeling knowledge interact. Three- to 5-year-olds avoided accepting new information from a previously inaccurate labeler, even though the speaker had only erred while blindfolded. Preschool-aged children might be confused about how to adjust their learning in response to a speaker who violates their expectations about co-reference by labeling objects without gazing at them. Researchers have also explored how linguistic information interacts with statements that violate or cohere with children’s factual knowledge. Noveck, Ho, and Sera (1996) examined how grammatical information interacts with perceptual knowledge, examining the influence of epistemic modals of varying force on children’s judgments about object location. Building on Hirst and Weil’s (1982) findings that 5-year-olds detect differences in force between modals like must, should, and may, they presented children with true and false statements with varying modal forms. They found that 5-, 7-, and 9-year-olds who were presented with true and false statements about hidden objects’ locations did not endorse false statements, even when they were more forceful. Evidently, social learners rely on their own prior factual knowledge and access to information to a great extent, even in the face of linguistic information that could sway their learning decisions. Together, these findings point to children’s ability to monitor, evaluate, and selectively learn from labelers who differ in accuracy. Most significantly, they illustrate the critical role that children’s existing knowledge of labels and


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their referents plays in their ability to engage in conflict-detection, which protects them from accepting misinformation. So far, we have reviewed research on children’s treatment of speakers who provide erroneous labels for highly familiar objects and concepts, which are often overt categorical errors representing violations of a fundamental form of knowledge shared by all speakers of a language. How do children treat errors that are not as obvious or easily detected? Are they more forgiving of other kinds of errors?

2.3. Children's Treatment of Grammatical Errors Research on children’s learning from speakers who vary in grammatical accuracy gives us further insight into the role of children’s prior knowledge and experience in epistemic vigilance. Jaswal, McKercher, and VanderBorght (2008), for instance, found that children endorsed novel labels from a previously accurate labeler, but did not endorse her irregular plural or past tense forms of novel nouns and verbs; instead, they endorsed regular plural or past tense forms of the novel nouns and verbs, even when they were provided by an unreliable source. In an additional experiment, informant reliability was established by a speaker’s consistent use of correct or incorrect morphology. The same endorsement pattern was found: children endorsed novel, regular morphology provided by a formerly unreliable morphologist over the irregular morphology provided by the formerly reliable morphologist. Corriveau, Pickard, and Harris (2011) argue that this pattern of results underscores the importance of children’s own experience with morphology. That is, children likely adopted the morphological forms of a previously inaccurate source because they weight their own knowledge of morphological regularities more heavily than a handful of instances of a source’s inaccuracy. To explore this possibility, they presented 4-year-olds with two speakers of differing labeling and grammatical accuracy who offered novel words of equally probable morphology. In this scenario, children selectively endorsed the accurate labeler and grammarian when learning both novel labels and novel morphology. Thus, children’s selective learning from previously reliable informants extends to different domains of language, so long as the informants’ claims are probable in light of children’s prior linguistic experience. A set of experiments by Sobel and Macris (2013) further illustrate how cues about speaker reliability interact with information about linguistic regularity. Four-year-olds were presented with two lexically accurate speakers who differed in their appropriate use of subject–verb agreement. When

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asked to learn object labels, older (but not younger) 4-year-olds relied on the syntactically accurate speaker to learn new labels for novel objects. Interestingly, when they observed speakers who were syntactically accurate but differed in lexical accuracy, all 4-year-olds used the speakers’ lexical accuracy to guide how they learned novel lexical information and novel irregular plurals. However, they did not rely on the lexically accurate speaker to learn irregular past tense forms. So, depending on the fidelity of the linguistic domain, and the kinds of exceptions that occur, reliability information alone may not always be enough. The processes of monitoring regularity information encoded in language and monitoring speaker accuracy interact.

2.4. Children's Treatment of Inconsistent, Illogical, and Improbable Statements When do children treat logically inconsistent statements as false? Previous research has indicated that the ability to detect logically inconsistent message content is evident by age 6. Unsurprisingly, there is age-related variability in children’s inconsistency-detecting competence, as well as variability based on the form that inconsistent statements take. Braine and Rumain (1981) found that most 5- and 6-year-olds were able to detect inconsistencies between statements offered by two puppets about the hidden contents of a box. Similarly, Ruffman (1999) found that when children were presented with two pairs of statements, where one featured a contradiction (e.g., “It’s very yummy.”/“It’s very yucky.”), and the other did not (e.g., “It’s very yummy.”/“It’s green.”), 6-year-olds successfully identified the pair of statements that did not “make sense.” Morris and Hasson (2010) found that children demonstrated the ability to determine whether two states were at odds (e.g., “There is a sticker in the box.” and “The box is empty.”) before they could detect statements’ syntax-based inconsistencies (e.g., “There is a sticker in the box and there is not a sticker in the box.”). However, even for 7- to 8-year-olds, syntax-based inconsistencies were more difficult than nonsyntax-based inconsistencies. Recent work from our laboratory suggests that children as young as four exhibit more skill than previously thought in making explicit judgments about logical consistency when presented with semantic or conceptual inconsistencies, such as “Today I saw a ball that was the smallest ball ever and it was the biggest ball ever at the same time” (Doebel, Koenig, & Rowell, 2011). While 4-year-olds detected the inconsistent statements and reported that they “did not make sense,” only 5-year-olds preferred to learn from an informant who previously made consistent statements,


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indicating that detecting inconsistency was not sufficient for making selective learning choices based on that information. This may reflect a failure on the part of 4-year-olds to connect inconsistency with irrationality or unreliability. Additionally, work on children’s treatment of messages on improbable and impossible events yields insight into the role of children’s prior knowledge in monitoring the acceptability of statements (for a review, see Woolley & Ghossainy, 2013). Shtulman and Carey (2007), for example, found that while adults easily distinguished between improbable (e.g., a person drinking onion juice) and impossible (e.g., a person eating lighting) scenarios, children between the ages of 4 and 8 showed strong inclinations toward skepticism. That is, they were likely to judge improbable events as impossible and did not show adult-like response patterns until age 8. In recent work by Corriveau, Chen, and Harris (2014), 5- and 6-year-old children were asked to make judgments about the reality status of protagonists in realistic, religious, and fantastical stories. Children from secular backgrounds were more likely than those from religious backgrounds to deny that the protagonist in religious stories was a real person. This supports the possibility that children’s skepticism towards improbable and impossible scenarios is in part tied to their lack of exposure and experience with speakers who discuss and endorse miraculous possibilities.

2.5. Children's Treatment of Factual and Episodic Errors So far we have reviewed research on children’s treatment of fundamental violations of word-object referents, statements containing grammatical mistakes and irregularities, and statements indicating inconsistency or logical deficiencies. Here, we examine the role of prior knowledge in coherence-checking and epistemic vigilance from the literature on children’s treatment of factual and episodic errors. Although we have reviewed much evidence indicating that toddlers and preschoolers detect apparent conflicts, there are circumstances under which they trust testimony that conflicts with their knowledge. Ganea, Koenig, and Millet (2011) found that 30-month-olds persisted in accepting claims from an unreliable informant about the location of a hidden toy; it was not until 36 months of age that children adjusted their willingness to update their beliefs based upon the past reliability of the speaker. Jaswal (2010) reported on other factors that bolster children’s developing abilities to engage in epistemic vigilance. When 30-month-olds were presented with

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testimony that conflicted with their naı¨ve beliefs or recent observations about the location of a treat, they were less trusting when they simultaneously saw evidence of its incorrectness or had convergent evidence confirming their initial beliefs. Therefore, if toddlers’ confidence in their existing beliefs can be reinforced or directly confirmed, they are more vigilant against false testimony. Cle´ment, Koenig, and Harris (2004) investigated children’s selective learning from testimony about perceptual information and reported two key findings. First, when asked to predict what a previously accurate source would say about a new object, both age groups predicted a correct report. However, when asked to predict what a previously inaccurate source would say, 4-year olds predicted future errors whereas 3-year-olds were less sure and more willing to predict a correct report. Second, there were limits to children’s trust in previously reliable informants when firsthand perceptual information was available. When object color was visible to children, both 3- and 4-year-olds did not endorse a previously reliable speaker’s incorrect testimony about its appearance. Fitneva, Lam, and Dunfield (2013) also found that 6-year-old children flexibly adjusted their dependence on other speakers (“experts” in this case) depending on their own access to information. That is, children relied on their own access to objects to learn about the visible properties of unfamiliar animals, but deferred to experts when learning about the animals’ hidden properties. Thus, even in early childhood, children monitor the nature of a claim, the kind of evidence required to support it, the gravity of speakers’ errors and the processes that explain them. Children’s testimonial learning decisions reflect sensitivity to the many considerations that go into our evaluations of people’s statements. As Quine and Ullian put it: “Acceptability depends, as always, on a weighing of the total evidence” (p. 63).

2.6. When Children Encounter Message Conflicts: Other Observations In this chapter, we have discussed the various ways in which children treat messages that conflict with their existing knowledge, focusing on the types of errors they encounter. In this section, we raise more general questions about conflict-detection across different domains of knowledge. First, we compare differences in children’s treatment of different kinds of message conflicts. Second, we discuss the apparent developmental gap between the ability to engage in conflict-detection and the emergence of competent


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selective learning. Finally, we consider the scope of epistemic vigilance once a conflict has been detected. Why does children’s treatment of discordant testimony—ranging from acceptance to explicit rejection and avoidance—seem to vary depending on the type of information communicated? For example, Jaswal and colleagues have shown that preschoolers continue to trust an adult who provides inaccurate information about the location of a highly desirable object ( Jaswal, 2010; Jaswal, Croft, Setia, & Cole, 2010). In contrast, many have shown that preschoolers avoid inaccurate informants: For example, they prefer previously accurate over inaccurate informants when learning new object labels ( Jaswal & Neely, 2006; Koenig & Harris, 2005; Koenig et al., 2004), and they even block learning from speakers who have mislabeled familiar objects (Koenig & Woodward, 2010). In response to such discrepancies, Koenig and Stephens (2014) have argued for the importance of certain conceptual or content-driven differences in testimony, such as key differences between “transient-episodic” claims (i.e., assertions of facts tied to a specific time and place) and “semantic–conceptual” claims (i.e., assertions about generalizable, conventional, scientific, or conceptual knowledge). They point to evidence that children are more vigilant in response to violations of commonly held, semantic, or cultural knowledge than to violations of transient, episodic knowledge, and show that children’s responses to episodic errors are less robust. This may be because episodic violations are less unusual, more readily explained by plausible processes, or nonrecurrent (or some combination of these). Further evidence for this comes from Stephens and Koenig (under review), who found that preschoolers initially exposed to differentially accurate semantic testimony were more likely to selectively learn from a previously accurate informant than children who were exposed to differentially accurate episodic testimony. These findings speak against a homogeneous treatment of testimony, and furthermore, point to children’s understanding that certain kinds of errors are more informative about a source’s competence than others. Another key observation about epistemic vigilance can be made from our review of children’s treatment of messages conflicting with their own knowledge: the ability to make explicit judgments about a speaker’s knowledge does not necessarily coincide with appropriately selective learning choices. For example, as described by Koenig and Echols (2003), 16-month-olds were able to detect and even correct mistaken labels for familiar objects. However, studies on 24-month-olds’ label learning indicate that their selective learning in response to these errors is still developing.

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Although, Koenig and Woodward found evidence for selective learning and memory in 24-month-olds, Krogh-Jespersen and Echols (2012) showed that children were willing to learn novel labels from both previously accurate and inaccurate speakers (for evidence of this delay between conflict detection and responsive learning beyond label-learning, see Cle´ment, Koenig, and Harris, 2004; Doebel et al., 2011). These conflicts suggest that children’s heightened monitoring of speakers based on the veracity of their statements does not automatically lead to avoidance during subsequent learning. Several factors might underlie children’s ability to modulate their learning in response to detected errors. First, selective learning might require cognitive skills that go above and beyond simply detecting message conflicts. A vast literature on children’s developing inhibitory control, for example, indicates that there is a major developmental shift in children’s ability to inhibit a prepotent response between ages 3 and 5 (see Carlson, 2005; Carlson, Moses, & Hix, 1998). Children with poor inhibitory control might be failing to suppress their tendency towards accepting speakers’ testimony, even testimony that conflicts with their own prior knowledge ( Jaswal et al., 2014). Another explanation is that children’s general understanding of the reasons underlying unreliability or states of knowledge is still developing. For example, work by Robinson and colleagues shows that 3- and 4-year-olds take into account an agent’s access to evidence when deciding whether to accept their claims (Robinson & Whitcombe, 2003) and will even excuse mistakes if they are due to limitations in perceptual access (Nurmsoo & Robinson, 2009b). However, blindfolded informants present new challenges to children, and they seem befuddled when asked to choose between two very odd speakers who differ in their epistemic sins: one who wears a blindfold when naming objects (and gets them wrong) and one who names them inaccurately with no blindfold (Nurmsoo & Robinson, 2009a). Children might rightfully check the coherence of such claims, and they withhold their trust until further explanations for this aberrant behavior are found. Children’s general cognitive abilities, as well as their more domain-specific knowledge about human agents are likely at play, and much work remains to shed more light on such factors that come together to support children’s learning decisions. Importantly, it could be that enhanced monitoring provoked by false utterances results in a more general sensitivity to the learning context— including social and nonsocial aspects of the environment. Kidd, Palmeri, and Aslin (2013), found that children’s ability to delay gratification to receive


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greater awards was affected by the reliability of the experimenter and perhaps of the testing situation. Children were originally told that they could play with some old, used materials or wait until the researcher brought to the room more exciting, brand-new materials. Half of those who waited were then given the disappointing news that the new materials (brand new crayons, stickers) were unavailable after all. When later tested, these children were more reluctant to delay receiving a reward than were those provided with the promised materials. Remaining questions include whether children in the unreliable condition primarily blamed their disappointments on the experimenter (who indeed apologized), or whether they associated more general aspects of the context with their thwarted expectations (e.g., the room, the experimental set-up, the conversational style used in experiments).

2.7. Interim Conclusion Through the course of language development, children become increasingly able to exploit their growing knowledge base to minimize the risks of misinformation signaled by testimonial messages. Use of the selective trust paradigm clarifies that children recognize inaccurate messages, identify the person responsible and avoid learning from inaccurate sources in the future. Furthermore, children’s treatment of unreliable testimony gives us insight not only into the mechanisms involved in their selective social learning, but also into the heart of what they know. We have reviewed extensive empirical evidence that children’s existing knowledge of word-object referents, syntactic and morphological regularities, logical possibilities, and factual and episodic information empowers them to optimize their learning and minimize risk via a coherence-checking or conflict-detection mechanism. Furthermore, we have described, and have raised questions about, the nature of these mechanisms and how they might develop, how they might be applied in different domains of knowledge, and how they might support epistemic vigilance. However, children often lack existing content knowledge to critically evaluate speakers’ claims. How do children protect themselves in these instances? In addition to the cognitive processes involved in coherencechecking, which allow children to detect conflicts of information or beliefs in testimonial messages, children must be equipped with cognitive mechanisms that support the monitoring of the speaker. Such mechanisms monitor for variations in speaker competence, warmth, group membership, and use

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of pedagogical cues. In the following section, we outline candidate cognitive processes involved in evaluating, monitoring, and remembering speakers, review current frameworks of early social learning that emphasize children’s treatment of source information, and review recent findings from the selective trust literature that elucidate the characteristics of sources that young children are most attentive to and likely to take into account when making selective learning decisions.

3. CHILDREN'S EVALUATIONS OF SPEAKERS Many of the child’s most relied upon informants are those that they interact with regularly: siblings, parents, friends, and perhaps extended family and grandparents. These informants are the sources of much of what children come to know, as well as central sources of information regarding when the truth is likely to be disguised or withheld, and when such decisions are based in ignorance, etiquette, fear, or deviousness. These central sources also, of course, misjudge, misconstrue, misinterpret, and misremember. For these reasons, it will be useful for children to keep track of speakerspecific information, remember the sources of one’s information, and engage in source monitoring processes. Although children have generally been characterized as poor monitors of source information relative to adults, they tend to perform best at the types of tasks most relevant to successful social learning: tasks requiring them to distinguish between self- and other-generated information or to distinguish between multiple dissimilar agents (see Roberts, 2002). For example, 4-year-olds performed as well as adults when determining which of two distinct voices had spoken particular words (Lindsay, Johnson, & Kwon, 1991), and even 3-year-olds performed at above-chance levels when determining whether they learned about the contents of a drawer by looking, being told, or making an inference from a clue (Gopnik & Graf, 1988). Recent research from our laboratory further suggests that preschoolers’ external source monitoring performance might be specifically enhanced when discriminating between multiple human speakers relative to other types of sources. Preschoolers demonstrated enhanced source monitoring performance when they learned about the locations of a hidden treat from various speakers (e.g., “A girl in a green shirt, blue shirt, or gray shirt.”) as opposed to from various sensory modalities (e.g., “Did she tell you the name, show you a picture, or make the sound?”) (Stephens, Corriveau, & Koenig, 2013). There are several reasons why children perform particularly well on these types of source monitoring tasks.


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According to the Source Monitoring Framework ( Johnson, Hashtroudi, & Lindsay, 1993), source attributions are based on qualitative characteristics of memory. Distinguishing between external sources (e.g., my mom vs. my friend) is likely easier for young children than distinguishing between internal sources (e.g., my thinking about writing a note vs. actually writing a note) because they encode more qualitative characteristics of external events that can serve as informative source cues (e.g., the voices of different individuals, what they were wearing, what they were looking or pointing at) and facilitate their successful source attributions and potentially, social learning decisions. Second, young children likely rely on source memory processes to make selective learning decisions. Whitcombe and Robinson explored implicit source memory processes in 3- and 4-year-olds and found that while most preschoolers consistently accepted information from a better-informed source (Whitcombe & Robinson, 2000) and updated their beliefs on the basis of that source’s testimony (Robinson & Whitcombe, 2003), many failed to explicitly report source information. Similarly, Haigh and Robinson (2009) showed that preschoolers often performed poorly when asked “how they knew” information about a hidden toy, but nonetheless correctly recalled several source-related details about the learning event, including whether they touched or saw the toy and whether they were better informed than another source. Thus, children might remember and utilize source information during selective learning tasks before they can even explicitly report source information. Young children do sometimes demonstrate explicit source memory, however, and show particularly good performance when their focus is directed towards external sources, as opposed to themselves, during encoding. Crawley, Newcombe, and Bingman (2010), for instance, found that 4- and 6-year-old children more accurately recalled which of two speakers had spoken several statements when they were instructed to focus on the emotions or features of the speakers as they delivered the statements. In typical selective learning tasks, the incompetence or immorality of speakers likely attracts children’s attention to the source, resulting in similarly enhanced source memory in these tasks. Third, successfully monitoring and responding to problematic speakers in selective learning tasks likely relies on executive function skills. Drummy and Newcombe (2002) showed that 4-year-olds’ source memory performance was associated with their performance on an executive function task, even after controlling for intelligence. Similarly, Rajan, Cuevas, and Bell (2014) showed that executive function predicted 4- and

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6-year-olds’ source memory performance over and above language ability and age. In concert with work reviewed earlier, these findings suggest that executive function may play a role in monitoring and remembering speakerspecific information as well as in rejecting the testimony presented by incompetent and immoral speakers, once detected. Finally, young children might rely on low-level facial processing capacities to make social evaluations and learning decisions. Cogsdill, Todorov, Spelke, and Banaji (2014) found that 3- to 6-year-old children consistently inferred character traits such as trustworthiness, competence and dominance based on the features of faces and did so with substantial consensus. Interestingly, the attributions of the younger children were similar to those of adults, and by 5 years of age, children’s attributions were as consistent as adults’. Children also consistently made social evaluations of faces, judging them as “nice” or “mean” in accordance with how trustworthy, competent, and dominant they appeared. Further evidence suggests that children’s perceptual judgments of facial trustworthiness influence their actual trust behavior. Ewing, Caulfield, Read, and Rhodes (2014) found that 5- and 10-year-old children selectively trusted individuals perceived as trustworthy in an economic game. These rapid judgments of individuals based on scant visual experience have real consequences for children’s evaluations of and interactions with speakers. In summary, to avoid accepting misinformation when unable to evaluate speaker messages, children likely turn their attention to characteristics of speakers themselves, recruiting cognitive processes such as source monitoring, source memory, executive function, and face processing. In what follows, we review current frameworks of social learning that emphasize specific aspects of children’s attention to source characteristics. Specifically, we discuss natural pedagogy, core dimensions of speakers, and the negativity bias, highlighting their varying perspectives on the characteristics of sources that matter most to children.

3.1. Natural Pedagogy According to the natural pedagogy perspective of early learning, human communication evolved to promote the transfer of generalizable knowledge between individuals (Csibra & Gergely, 2009). From this perspective, experts are particularly motivated to share generalizable information with novices, and novices are innately prepared to receive generalizable information from others. Consistent with these ideas, adults and older children often


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tailor their presentation of information to facilitate generic learning in pedagogical contexts (e.g., Gelman, Ware, Manczack, & Graham, 2013), and even very young infants appear sensitive to adults’ ostensive pedagogical cues such as direct eye contact (Farroni, Csibra, Simion, & Johnson., 2002) and referential cues such as pointing and labeling (Gliga & Csibra, 2009). Proponents of natural pedagogy argue that young children are not only biased to attend to these ostensive–referential signals, but are also biased to accept and generalize information communicated in ostensive–referential contexts (Csibra & Gergely, 2009). Thus, from this perspective, children are predisposed to trust testimony in pedagogical contexts and should demonstrate low monitoring and high acceptance and generalization of novel information in response to the pedagogical signals that speakers communicate. Empirical evidence indicates that children are indeed inclined to accept novel testimony from unknown speakers in pedagogical contexts. Substantial research on word learning demonstrates that infants and very young children readily learn and generalize adults’ labels for novel objects (e.g., Baldwin, 1993). Likewise, studies of categorization have shown that older infants and preschoolers will accept and make inferences based on an unfamiliar adult’s testimony about the classification of objects, even when it directly conflicts with their own perceptual judgments ( Jaswal, 2004; Jaswal & Markman, 2007). Moreover, children’s willingness to accept testimony about unexpected classifications is not restricted to the preschool years, nor is it restricted to a single culture. Chan and Tardif (2013) found that American and Chinese kindergarteners and second-graders tended to categorize ambiguous exemplars according to a teacher’s inaccurate testimony, even when the teacher was no longer present. According to the natural pedagogy perspective, adults are likely to provide accurate information, and children are entitled to trust it when it is delivered in a pedagogical context. Therefore, a critical task in early development is learning to discriminate between information communicated in pedagogical contexts and information communicated in other contexts such as pretense, in which adults often intentionally provide inaccurate testimony to children. Recent research indicates that, in contrast with their behavior in pedagogical contexts, children do not automatically accept and generalize novel information communicated by adults in pretend scenarios. For example, Sutherland and Friedman (2013) found that 3- and 4-year-olds consider the plausibility of novel claims made in pretend scenarios and refuse to generalize implausible claims or claims that conflict with their prior knowledge to “real life” scenarios.

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In general, consistent with a natural pedagogy perspective, when children have no information about unfamiliar speakers, have no relevant prior knowledge that bears on the message, and encounter messages in the presence of clear ostensive–referential cues, they tend to accept what they are told. However, children most often have some prior knowledge, either about some aspect of the world or the speaker who is addressing them, and much of the evidence reviewed earlier indicates that they bring that knowledge to bear when evaluating testimony. Indeed, it may be that children generally accept novel information from speakers unless they have reasons not to, and under those conditions, children reject or revise previously held testimony-based beliefs when a trusted speaker proves problematic (e.g., Scofield & Behrend, 2008). Children’s sensitivity to considerations or reasons that “defeat” a piece of testimony, along with a capacity to question or doubt the testimony, are critical to developing complete accounts of social learning. If listeners, both child and adult, enjoy some presumptive entitlement to accept testimony under overtly pedagogical conditions, they do so only because this presumption can be suspended when a piece of testimony or a speaker elicits a conflict.

3.2. Core Dimensions of Speakers What type of evidence do children monitor and take into account when evaluating potential sources of information? When faced with unfamiliar speakers, adults and children need to determine, first, whether a speaker intends to provide accurate and helpful information, and second, whether the speaker has the ability to provide accurate and helpful information. Consistent with these considerations, current conceptions of social cognition and perception argue that adults’ evaluations of others are based almost entirely on the core dimensions of moral warmth (i.e., friendliness, helpfulness) and competence (i.e., intelligence, efficacy) (Fiske, Cuddy, & Glick, 2007). Young children seem to similarly monitor and modulate their social learning based on evidence of speakers’ moral warmth and competence, and appropriately suspend trust when faced with evidence of speaker immorality or incompetence. 3.2.1 Competence As discussed in the first section of this chapter, preschoolers can often rely on prior knowledge to monitor the coherence of speakers’ messages. Incoherent messages often clearly signal speaker incompetence, and children avoid learning from speakers with histories of blatant inaccuracy or irrationality.


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However, when young children lack relevant content knowledge to engage in coherence checking, they seem to exploit a number source-specific sociopragmatic cues to infer an unfamiliar speaker’s competence. For example, children monitor speakers’ admissions of ignorance and appear to block long-term learning and generalization from ignorant speakers. Sabbagh and Shafman (2009) presented preschoolers with a confident labeler or an admittedly ignorant one. Children who were exposed to the ignorant labeler inhibited the formation of an enduring word-object association after detecting the ignorant speaker’s incompetence. Jaswal and Malone (2007) similarly found that preschoolers were less likely to take a speaker’s testimony into account when making inferences about the function of an ambiguous object when the speaker expressed uncertainty about the object’s classification. Young children understand the connection between seeing and knowing (e.g., O’Neill, 1996) and monitor speakers’ relevant access to information to evaluate their situational competence during learning (see Nurmsoo & Robinson, 2009a,b). Furthermore, preschoolers understand that a speaker with relevant knowledge ceases to be effective when not in a position to use or act on that knowledge. Kushnir, Wellman, and Gelman (2008) found that preschoolers trusted a more knowledgeable informant when deciding which of two blocks “made a toy go” only when that informant was allowed to see and select a specific block for himself. Children also take into account speakers’ more specific competencies, or expertise, as well. Even infants appear to appreciate differences in expertise. For example, 12-months-old prefer to look at an experimenter, as opposed to their mothers, when presented with strange toys in a lab setting (Stenberg, 2009), suggesting that they expect the experimenter to have more information about the lab scenario. Preschoolers also discriminate between speakers with labeling expertise and those with mechanical expertise, systematically asking a previously competent labeler for the names of novel objects but asking a previously competent “fixer” to repair malfunctioning toys (Kushnir, Vredenburgh, & Schneider, 2013). Furthermore, young children expect that an object’s creator will possess particular expertise about its intended function. Using ambiguous objects, Jaswal (2006) found that young children were more likely to infer an object’s function based on an unexpected label if the labeler claimed to be the object’s creator. Finally, young children recognize that individuals of different ages, such as children and adults, have expertise in different information domains. VanderBorght and Jaswal (2009) found that preschoolers expect adults to be more competent than

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children when communicating the nutritional value of foods, but expect children to be more competent than adults when communicating about toys. All of these epistemic considerations underscore children’s ability to engage in the kind of epistemic reasoning that testimonial learning requires. Finally, young children monitor, interpret, and exploit bystanders’ reactions to testimony as indirect indices of a speaker’s competence. Young children most often endorse a speaker whose testimony is received with nonverbal signs of approval as opposed to disapproval from bystanders, even when the bystanders are no longer present (Fusaro & Harris, 2008). Additionally, preschoolers take into account the consensus of the majority when inferring speaker competence. When asked to identify the referent of a novel label, preschoolers tend to select the object indicated by the majority over that indicated by a lone dissenter (Corriveau, Fusaro, & Harris, 2009). In summary, even when young children lack relevant prior knowledge to directly evaluate the competence of a speaker’s testimony, they monitor sources for alternative indicators of competence. By preschool, children track speakers’ admissions of ignorance, inhibiting long-term learning from ignorant speakers. They also make inferences based on their understandings of knowledge acquisition and divisions of cognitive labor about how likely speakers are to know specific types of information given their present position and specific areas of expertise. Finally, preschoolers call on precocious social cognitive skills to assess other people’s evaluations of a given speaker and retain and incorporate that information into their own selective learning decisions. Children’s sensitivity to these various ways in which speakers demonstrate their competence is impressive, and it calls for further research concerning the types of adjustments they make when their trust in competent informants is challenged. It can happen that trusted sources will lose their authority, and it would be useful to know if children’s adjustments are sweeping or calibrated, and how far children can monitor the beliefs that might require reassessment (for work in this direction, see Scofield & Behrend, 2008). 3.2.2 Moral Warmth Children show sensitivity to behavioral indicators of moral warmth beginning early in infancy (e.g., Hamlin & Wynn, 2011; Kuhlmeier, Wynn, & Bloom, 2003; Vaish, Carpenter, & Tomasello, 2009). Hamlin, Wynn, and Bloom (2007), for example, found that infants as young as 6 months of age preferred to reach for a previously helpful agent as opposed to a previously hindering agent, suggesting that even very early social evaluations


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are influenced by individuals’ past treatment of others. However, not until preschool do children take into account moral information when deciding whether to learn from speakers. Mascaro and Sperber (2009) presented 3- and 4-year-olds with two puppets, one described as “mean” and the other described as “nice.” Both age groups were more likely to trust the “nice” puppet over the “mean” puppet, indicating that children as young as 3 years of age are able to track general information about speakers’ moral warmth and use that global trait information to modulate their levels of epistemic trust. Four-year-olds, but not 3-year-olds, also appropriately rejected the claims of a puppet described as a “big liar who always tells lies,” indicating a clear developmental shift in the types of epistemic inferences drawn from specific immoral behavior. Similarly, Vanderbilt, Liu, and Heyman (2011) demonstrated that 3-year-olds readily accepted advice about a treat’s location from speakers who consistently tricked or helped others in the past, whereas 4- and 5-year-olds only accepted advice from those who consistently helped previous finders, again indicating a shift in children’s understanding of the implications of immoral behavior for social learning. Older children begin to prioritize speakers’ intentions over other information when deciding whom to accept novel information from. Liu, Vanderbilt, and Heyman (2013) demonstrated that 5- and 6-year-olds more often trusted speakers who had previously demonstrated good intentions towards a finder than bad intentions, regardless of the correctness of the information they communicated about the locations of hidden treats, underscoring the importance of moral considerations in older children’s learning decisions. When young children lack direct cues to a speaker’s moral warmth or good intentions, they exploit indirect cues to speaker morality to make selective learning decisions. One salient indirect indicator of speakers’ intentions is their group membership (e.g., Killen, Margie, & Sinno, 2006). Individuals often attribute moral warmth to their in-group, and such attributions are associated with increased helping and facilitative behaviors (see Fiske et al., 2007). Young children appear highly sensitive to group membership information when learning from speakers, perhaps inferring that members of their in-group will be particularly motivated to provide them with relevant and valuable information. For example, all else being equal, preschoolers generally prefer to accept testimony from their mothers over strangers (Corriveau, Fusaro, et al., 2009; Corriveau, Meints, & Harris, 2009; Corriveau, Harris, et al., 2009), familiar teachers over unfamiliar teachers (Corriveau & Harris, 2009a), and native-accented speakers over foreign-accented speakers (Kinzler, Corriveau, & Harris, 2011). Children’s learning is not only

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influenced by socially relevant group membership information but also by arbitrary, uninformative affiliations. Macdonald, Schug, Chase, and Barth (2013) showed that 4-year-olds did not preferentially endorse the labels provided by a previously reliable out-group member over those of an unreliable in-group member, even though group membership was randomly assigned and bore no social significance. Hetherington, Hendrickson, and Koenig (2014) explored the specific influences of group membership and moral behavior on children’s social preferences and learning decisions. They assigned 4- and 5-year-old children to arbitrary groups and found that preschoolers’ preferences for and willingness to share with an in-group member were substantially reduced when she behaved antisocially, but they still preferred to learn from the antisocial in-group member as opposed to a neutral out-group member. These findings suggest that children’s moral judgments, like their competence judgments, are not absolute nor unqualified but, rather, nuanced, responsive, and specific. Although young children monitor agents’ moral behavior and dislike immoral agents, they do not necessarily view in-group members’ immoral actions as indicative of their quality as potential informants. Instead, they maintain a preference to learn from immoral in-group members, even when they are disliked. Clearly, young children’s social evaluations, like adults,’ rely heavily on evidence of moral warmth. Monitoring of agents’ moral warmth is evident as early as infancy, and older preschoolers recognize its implications for testimonial learning situations. Although speakers’ moral behavior is significant to children very early in development, the consideration of its importance in testimonial learning situations seems to occur later in development. Shafto, Eaves, Navarro, and Perfors (2012) demonstrated that the computational model best capturing 3-year-olds’ performance on selective trust tasks makes inferences about speakers’ knowledge only and neglects to make inferences surrounding speakers’ intentions. In contrast, 4-year-olds’ performance was best captured by a model that took into account both speakers’ knowledge and intentions, suggesting a clear developmental shift in the tendency to monitor speakers’ moral behavior in testimonial learning scenarios and to inhibit learning from immoral or deceptive individuals.

3.3. Negativity Bias In general, adults weigh negative events and information more heavily than positive events or information, a “negativity bias” or “positive–negative asymmetry effect” that has been consistently and ubiquitously found in


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the domains of attention, memory, emotion, learning, and impression formation, among others (see Baumeister, Bratslavsky, Finkenauer, & Vohns, 2001). Such a bias is also evident in infants and young children (see Vaish, Grossmann, & Woodward, 2008) and has recently been proposed to participate in children’s selective learning decisions (Koenig & Doebel, 2012). Here, we provide further evidence that indicators of speaker incompetence or immorality are better monitored, evaluated, and remembered by children than are indicators of competence or morality. Fiske et al. (2007) argue that individuals generally heed positive evidence of competence and overlook instances of incompetence. However, young children seem to demonstrate a clear negativity bias when evaluating speakers’ competence. Corriveau, Meints, et al. (2009), for example, demonstrated that 4-year-olds preferentially endorsed a previously accurate labeler over a neutral speaker, and a neutral speaker over a previously inaccurate labeler. Three-year-olds, however, displayed a clear negativity bias: they showed no preference for the testimony of a previously accurate labeler over that of a neutral speaker, but preferentially endorsed a neutral speaker over a previously inaccurate labeler. This pattern suggests that the youngest age group predominantly monitored speakers for signs of incompetence. Consistent with this finding, young preschoolers penalize speakers who make very few errors. Pasquini, Corriveau, Koenig, and Harris (2007) presented children with two speakers who differed in relative accuracy, with one always making fewer labeling errors than the other. Four-year-olds preferentially endorsed novel testimony provided by the more accurate speaker in every condition. Three-year-olds, in contrast, were highly attentive to instances of inaccuracy and only preferred the more accurate speaker when she had made no errors in the past, exhibiting striking mistrust after exposure to a single testimonial error. Doubt toward incompetent speakers was also apparent in research conducted by Koenig and Jaswal (2011), who presented preschoolers with two speakers, one of whom lacked experience with dogs and made errors in labeling their breeds and one whose knowledge was unremarkable and who expressed neutral preferences for the dogs. Preschoolers avoided the incompetent source and preferentially accepted the neutral speaker’s testimony regarding not only novel dogs but also novel objects, indicating a global avoidance of the incompetent source. Often, in everyday life, children (and adults) lack access to multiple informative episodes of a stranger’s competence or incompetence, and must decide whether or not to accept their testimony based on a single encounter. Fitneva and Dunfield (2010) examined 4-year-olds, 7-year-olds, and adults’ selective

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learning decisions after observing a single instance of informants accurately or inaccurately responding to a question about a storybook scene. When requested to indicate who they wanted to ask about additional elements of the scene, 7-year-olds and adults most often selected the previously accurate informant, indicating that a single instance of inaccuracy sufficed to provoke their negative speaker evaluations. Taken together, these studies suggest that when presented with indicators of competence, preschoolers are more likely to monitor for evidence of incompetence as opposed to competence. Inaccuracies, even if few in number or restricted in content, provoke negative social evaluations, epistemic vigilance, and expectations of generalized incompetence in young children. Young children also seem disproportionately attentive to immoral behavior relative to moral behavior. Kinzler and Shutts (2008) showed 3- and 4-year-olds a series of faces described as frequently committing mean or nice actions. Both age groups showed enhanced memory for faces described as “mean” compared to those described as “nice,” as well as enhanced recollection for the specific actions the “mean” faces were said to have committed (Baltazar, Shutts, & Kinzler, 2012). This suggests a clear bias in young children to attend to and remember individuals associated with immoral as opposed to moral behavior, one that might be evolutionary adaptive (e.g., Cosmides & Tooby, 1992). As discussed previously, preschoolers take into account moral information when deciding whether to learn from speakers and prefer to learn from those described as “nice” as opposed to those described as “mean” (e.g., Mascaro & Sperber, 2009). However, it is unclear whether young children’s selective trust in a moral speaker is attributable to a specific preference for a moral speaker or vigilance against an immoral speaker. Doebel and Koenig (2013) investigated this issue by familiarizing preschoolers to either a moral speaker or an immoral speaker, each of whom was paired with a neutral speaker. Children exhibited a clear negativity bias when asked to discriminate the nicer of the two speakers: they showed enhanced performance when discriminating between immoral and neutral behavior relative to when discriminating between moral and neutral behavior. Thus, preschool-aged children appear to demonstrate a negativity bias when monitoring speakers for moral behavior, showing heightened face memory and source memory for immoral relative to moral individuals, and heightened attention to immoral behavior when making social evaluations. However, young children’s learning from speakers does not appear to be driven by negativity or positivity biases. When provided with evidence of


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speaker morality in the absence of other relevant speaker characteristics, findings suggest that, by 4 years of age, children generally prefer to learn from an individual judged to be nicer (Doebel & Koenig, 2013; Mascaro & Sperber, 2009). Yet, when children receive additional information about speakers, evidence of their good intentions and moral warmth enters into a wider network of considerations. As Hetherington and colleagues’ (2014) findings clarify, young children do not always defer to the informant judged to be nicer. Instead, they take into account group membership, and considered it to be a more relevant consideration in this learning context than the speaker’s prior antisocial behavior. Children might similarly demonstrate a learning preference for speakers with questionable intentions who nonetheless have more authority, expertise, or access to information.

4. CONCLUDING THOUGHTS To sum, we have argued here that infants and young children first encounter and detect messages that conflict with their prior knowledge, before they encounter and detect individuals who present conflicts of interest. Such message conflicts are likely central to igniting the testimonial learning processes for three reasons: First, such conflicts may serve to raise the very possibility of false language to infants (Koenig & Woodward, 2010). Second, once the possibility of false testimony is raised, our review of recent research suggests that children treat errors differently depending on the type of conflict involved. Conflicts of meaning (e.g., calling a ball “a shoe”; “Here is a brushes”) and conflicts of logic (e.g., “the ball is X and it is not X”; “1 + 1 ¼ 6”) elicit not only a quick rejection but also a ready appraisal of the speakers who produce such statements. Conflicts of transient (“The treat is in the red box.”) or episodic facts (e.g., “The ball is soft.”) elicit less rejection and an interest in the kind of access that the speaker had when making the claim (Brosseau-Liard & Birch, 2011). Further, claims that are not internally inconsistent but that violate aspects of the child’s experience and knowledge of the physical world (e.g., miraculous or impossible claims) invite different interpretations depending on the child’s prior experience (Corriveau et al., 2014). While more work is needed, research investigating different types of “message conflicts” suggests that children are interested in the kind of backing or support that a claim receives and also, that what backing suffices depends upon the claim it is to meant to support. Third and finally, these message conflicts elicit appraisals of the speaker. These conflicts

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likely set in motion processes described earlier that support those parts of the reasoning process that target the identity of the speaker, the group or part of the population that made the claim, and how they came to hold it.

ACKNOWLEDGMENT This chapter was supported by the National Institute of Health under a Ruth L. Kirschstein National Research Service Award (grant number 5T32HD007151) from the NICHD to E. Stephens and by the National Science Foundation (NSF award #1024298) to M. Koenig.

REFERENCES Adler, J. (2002). Belief’s own ethics. Cambridge, MA: MIT Press. Baldwin, D. A. (1993). Infants’ ability to consult the speaker for clues to word reference. Journal of Child Language, 20, 395–418. Baltazar, N. C., Shutts, K., & Kinzler, K. D. (2012). Children show heightened memory for threatening social actions. Journal of Experimental Child Psychology, 112, 102–110. Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohns, K. D. (2001). Bad is stronger than good. Review of General Psychology, 5, 323–370. Bergelson, E., & Swingley, D. (2012). At 6–9 months, human infants know the meanings of many common nouns. Proceedings of the National Academy of Sciences of the United States of America, 109(9), 3253–3258. Braine, M. D., & Rumain, B. (1981). Development of comprehension of “or”: Evidence for a sequence of competencies. Journal of Experimental Child Psychology, 31(1), 46–70. Brosseau-Liard, P., & Birch, S. A. (2011). Epistemic states and traits: Preschoolers appreciate the differential informativeness of situation-specific and person-specific cues to knowledge. Child Development, 82, 1788–1796. Carlson, S. M. (2005). Developmentally sensitive measures of executive function in preschool children. Developmental Neuropsychology, 28(2), 595–616. Carlson, S. M., Moses, L. J., & Hix, H. R. (1998). The role of inhibitory processes in young children’s difficulties with deception and false belief. Child Development, 69(3), 672–691. Chan, C. C., & Tardif, T. (2013). Knowing better: The role of prior knowledge and culture in trust in testimony. Developmental Psychology, 49, 591–601. Chow, V., Poulin-Dubois, D., & Lewis, J. (2008). To see or not to see: Infants prefer to follow the gaze of a reliable looker. Developmental Science, 11(5), 761–770. Cle´ment, F., Koenig, M., & Harris, P. (2004). The ontogenesis of trust. Mind & Language, 19(4), 360–379. Coady, C. A. J. (1992). Testimony: A philosophical study. Oxford: Clarendon Press. Cogsdill, E. J., Todorov, A. T., Spelke, E. S., & Banaji, M. R. (2014). Inferring character from faces: A developmental study. Psychological Science, 25, 1132–1139. Cole, C. A., Harris, P. L., & Koenig, M. A. (2012). Entitled to trust? Philosophical frameworks and evidence from children. Analyse & Kritik, 34(2), 195–216. Corriveau, K. H., Chen, E. E., & Harris, P. L. (2014). Judgments about fact and fiction by children from religious and nonreligious backgrounds. Cognitive Science, 1–30. Corriveau, K. H., Fusaro, M., & Harris, P. L. (2009). Going with the flow: Preschoolers prefer nondissenters as informants. Psychological Science, 20, 372–377. Corriveau, K., & Harris, P. L. (2009a). Choosing your informant: Weighing familiarity with recent accuracy. Developmental Science, 12, 426–437. Corriveau, K., & Harris, P. L. (2009b). Preschoolers continue to trust a more accurate informant 1 week after exposure to accuracy information. Developmental Science, 12(1), 188–193.


179

Corriveau, K. H., Harris, P. L., Meins, E., Fernyhough, C., Arnott, B., Elliot, L., et al. (2009). Young children’s trust in their mother’s claims: Longitudinal links with attachment security in infancy. Child Development, 80, 750–761. Corriveau, K. H., Meints, K., & Harris, P. L. (2009). Early tracking of informant accuracy and inaccuracy. British Journal of Developmental Psychology, 27, 331–342. Corriveau, K. H., Pickard, K., & Harris, P. L. (2011). Preschoolers trust particular informants when learning new names and new morphological forms. British Journal of Developmental Psychology, 29(1), 46–63. Cosmides, L., & Tooby, J. (1992). Cognitive adaptations for social exchange. In J. Barkow, L. Cosmides, & J. Tooby (Eds.), The adapted mind: Evolutionary psychology and the generation of culture (pp. 163–228). New York, NY: Oxford University Press. Crawley, S. L., Newcombe, N. S., & Bingman, H. (2010). How focus at encoding affects children’s source monitoring. Journal of Experimental Child Psychology, 105, 273–285. Csibra, G., & Gergely, G. (2009). Natural pedagogy. Trends in Cognitive Sciences, 13, 148–153. Davidson, D. (1984). Truth and interpretation. New York, NY: Clarendon. Doebel, S., & Koenig, M. A. (2013). Children’s use of moral behavior in selective trust: Discrimination versus learning. Developmental Psychology, 49, 462–469. Doebel, S., Koenig, M. A., & Rowell, S. (2011). Young children detect logical inconsistency. In Poster presented at the 2011 biennial meeting of the society for research in child development, Montreal, QC. Drummy, A. B., & Newcombe, N. S. (2002). Developmental changes in source memory. Developmental Science, 5, 502–513. Einav, S., & Robinson, E. J. (2010). Children’s sensitivity to error magnitude when evaluating informants. Cognitive Development, 25(3), 218–232. Ewing, L., Caulfield, F., Read, A., & Rhodes, G. (2014). Perceived trustworthiness of faces drives trust behavior in children. Developmental Science. http://dx.doi.org/10.1111/ desc.12218. Farroni, T., Csibra, G., Simion, F., & Johnson, M. H. (2002). Eye contact detection in humans from birth. Proceedings of the National Academy of Sciences of the United States of America, 99(14), 9602–9605. Faulkner, P. (2003). The epistemic role of trust. In R. Falcone, S. Barber, L. Korba, & M. Singh (Eds.), Trust, reputation, and security: Theories and practice (pp. 30–38). Berlin: Springer. Faulkner, P. (2011). Knowledge on trust. Oxford: Oxford University Press. Fiske, S. T., Cuddy, A. J., & Glick, P. (2007). University dimensions of social cognition: Warmth and competence. Trends in Cognitive Sciences, 11, 77–83. Fitneva, S. A., & Dunfield, K. A. (2010). Selective information seeking after a single encounter. Developmental Psychology, 46, 1380–1384. Fitneva, S. A., Lam, N. H., & Dunfield, K. A. (2013). The development of children’s information gathering: To look or to ask? Developmental Psychology, 49(3), 533–542. Fricker, E. (1995). Critical notice: Telling and trusting: Reductionism and anti-reductionism in the epistemology of testimony. Mind, 104, 393–411. Fusaro, M., & Harris, P. L. (2008). Children assess informant reliability using bystanders’ non-verbal cues. Developmental Science, 11, 771–777. Ganea, P., Koenig, M. A., & Millet, K. (2011). Changing your mind about things unseen: Toddlers’ sensitivity to prior reliability. Journal of Experimental Psychology, 109, 445–453. Gelman, S. A., Ware, E. A., Manczack, E. M., & Graham, S. A. (2013). Children’s sensitivity to the knowledge expressed in pedagogical and nonpedagogical contexts. Developmental Psychology, 49, 491–504. Gliga, T., & Csibra, G. (2009). One-year-old infants appreciate the referential nature of deictic gestures and words. Psychological Science, 20(3), 347–353.

180


Goldberg, S., & Henderson, D. (2006). Monitoring and anti-reductionism in the epistemology of testimony. Philosophy and Phenomenological Research, 72(3), 600–617. Gopnik, A., & Graf, P. (1988). Knowing how you know: Young children’s ability to identify and remember the source of their beliefs. Child Development, 59, 1366–1371. Haigh, S. N., & Robinson, E. J. (2009). What children know about the source of their knowledge without reporting it as the source. European Journal of Developmental Psychology, 6, 318–336. Hamlin, J. K., & Wynn, K. (2011). Young infants prefer prosocial to antisocial others. Cognitive Development, 26, 30–39. Hamlin, J. K., Wynn, K., & Bloom, P. (2007). Social evaluation by preverbal infants. Nature, 450, 557–559. Hetherington, C., Hendrickson, C., & Koenig, M. (2014). Reducing an in-group bias in preschool children: The impact of moral behavior. Developmental Science, 17, 1042–1049. http://dx.doi.org/10.1111/desc.12192. Hirst, W., & Weil, J. (1982). Acquisition of epistemic and deontic meaning of modals. Journal of Child Language, 9(3), 659–666. Hume, D. (1748/1995). Of miracles. Section X, An inquiry concerning human understanding (pp. 117–141). Upper Saddle River, NJ: Prentice Hall Publishing. Jaswal, V. K. (2004). Don’t believe everything you hear: Preschoolers’ sensitivity to speaker intent in category induction. Child Development, 75, 1871–1885. Jaswal, V. K. (2006). Preschoolers’ favor the creator’s label when reasoning about an artifact’s function. Cognition, 99, B83–B92. Jaswal, V. K. (2010). Believing what you’re told: Young children’s trust in unexpected testimony about the physical world. Cognitive Psychology, 61(3), 248–272. Jaswal, V. K., Croft, A. C., Setia, A. R., & Cole, C. A. (2010). Young children have a specific, highly robust bias to trust testimony. Psychological Science, 21, 1541–1547. Jaswal, V. K., & Malone, L. S. (2007). Turning believers into skeptics: 3-year-olds’ sensitivity to cues to speaker credibility. Journal of Cognition and Development, 8, 263–283. Jaswal, V. K., & Markman, E. M. (2007). Looks aren’t everything: 24-month-olds’ willingness to accept unexpected labels. Journal of Cognition and Development, 8, 93–111. Jaswal, V. K., McKercher, D. A., & VanderBorght, M. (2008). Limitations on reliability: Regularity rules in the English plural and past tense. Child Development, 79(3), 750–760. Jaswal, V. K., & Neely, L. A. (2006). Adults don’t always know best: Preschoolers use past reliability over age when learning new words. Psychological Science, 17, 757–758. Jaswal, V. K., Perez-Edgar, K., Kondrad, R. L., Palmquist, C. M., Cole, C. A., & Cole, C. E. (2014). Can’t stop believing: Inhibitory control and resistance to misleading testimony. Developmental Science, 17, 965–976. http://dx.doi.org/10.1111/desc.12187. Johnson, M. K., Hashtroudi, S., & Lindsay, D. S. (1993). Source monitoring. Psychological Bulletin, 114, 3–28. Johnson, S., Slaughter, V., & Carey, S. (1998). Whose gaze will infants follow? The elicitation of gaze-following in 12-month-olds. Developmental Science, 1(2), 233–238. Kidd, C., Palmeri, H., & Aslin, R. N. (2013). Rational snacking: Young children’s decisionmaking on the marshmallow task is moderated by beliefs about environmental reliability. Cognition, 126(1), 109–114. Killen, M., Margie, N. G., & Sinno, S. (2006). Morality in the context of intergroup relationships. In M. Killen, & J. Smetana (Eds.), Handbook of moral development (pp. 155–183). Mahwah, NJ: Lawrence Erlbaum Associates, Inc. Kinzler, K. D., Corriveau, K., & Harris, P. L. (2011). Children’s selective trust in nativeaccented speakers. Developmental Science, 14, 106–111. Kinzler, K. D., & Shutts, K. (2008). Memory for “mean” over “nice”: The influence of threat on children’s face memory. Cognition, 107, 775–783.


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Koenig, M. A., Cle´ment, F., & Harris, P. L. (2004). Trust in testimony: Children’s use of true and false statements. Psychological Science, 15(10), 694–698. Koenig, M. A., & Doebel, S. (2012). Children’s understanding of unreliability: Evidence for a negativity bias. In M. Banaji & S. Gelman (Eds.), Navigating the social world: What infants, children and other species can tell us. Oxford University Press. Koenig, M. A., & Echols, C. H. (2003). Infants’ understanding of false labeling events: The referential roles of words and the speakers who use them. Cognition, 87(3), 179–208. Koenig, M. A., & Harris, P. L. (2005). Preschoolers mistrust ignorant and inaccurate speakers. Child Development, 76(6), 1261–1277. Koenig, M. A., & Jaswal, V. K. (2011). Characterizing children’s expectations about expertise and incompetence: Halo or pitchfork effects? Child Development, 82, 1634–1647. Koenig, M. A., & Stephens, E. (2014). Characterizing children’s responsiveness to cues of speaker trustworthiness: Two proposals. In S. Einav & E. Robinson (Eds.), Trust and skepticism (pp. 13–27). New York: Psychology Press. Koenig, M. A., & Woodward, A. L. (2010). Sensitivity of 24-month-olds to the prior inaccuracy of the source: Possible mechanisms. Developmental Psychology, 46(4), 815–826. Kondrad, R. L., & Jaswal, V. K. (2012). Explaining the errors away: Young children forgive understandable semantic mistakes. Cognitive Development, 27(2), 126–135. Krogh-Jespersen, S., & Echols, C. H. (2012). The influence of speaker reliability on first versus second label learning. Child Development, 83(2), 581–590. Kuhlmeier, V., Wynn, K., & Bloom, P. (2003). Attribution of dispositional states by 12-month-olds. Psychological Science, 14, 402–408. Kushnir, T., Vredenburgh, C., & Schneider, L. A. (2013). Who can help me fix this toy? The distinction between causal knowledge and word knowledge guides preschoolers’ selective requests for information. Developmental Psychology, 49, 446–453. Kushnir, T., Wellman, H. M., & Gelman, S. A. (2008). The role of preschoolers’ social understanding in evaluating the informativeness of causal interventions. Cognition, 107, 1084–1092. Lewis, D. K. (1969). Convention: A philosophical study. Cambridge, MA: Harvard University Press. Lindsay, D. S., Johnson, M. K., & Kwon, P. (1991). Developmental changes in memory source monitoring. Journal of Experimental Child Psychology, 52, 297–318. Liu, D., Vanderbilt, K. E., & Heyman, G. D. (2013). Selective trust: Children’s use of intention and outcome of past testimony. Developmental Psychology, 49, 439–445. Macdonald, K., Schug, M., Chase, E., & Barth, H. (2013). My people, right or wrong? Minimal group membership disrupts preschoolers’ selective trust. Cognitive Development, 28, 247–259. Mascaro, O., & Sperber, D. (2009). The moral, epistemic, and mindreading components of children’s vigilance towards deception. Cognition, 112, 367–380. McMyler, B. (2007). Knowing at second hand. Inquiry, 50(5), 511–540. Mercier, H., & Sperber, D. (2009). Intuitive and reflective inferences. In J. St. B. T. Evans, & K. Frankish (Eds.), In two minds: Dual processes and beyond (pp. 149–170). New York, NY: Oxford University Press. Mercier, H., & Sperber, D. (2011). Why do humans reason? Arguments for an argumentative theory. Behavioral and Brain Sciences, 34(02), 57–74. Morris, B. J., & Hasson, U. (2010). Multiple sources of competence underlying the comprehension of inconsistencies: A developmental investigation. Journal of Experimental Psychology. Learning, Memory, and Cognition, 36(2), 277. Noveck, I. A., Ho, S., & Sera, M. (1996). Children’s understanding of epistemic modals. Journal of Child Language, 23(3), 621–643.

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Nurmsoo, E., & Robinson, E. J. (2009a). Identifying unreliable informants: Do children excuse past inaccuracy? Developmental Science, 12(1), 41–47. Nurmsoo, E., & Robinson, E. J. (2009b). Children’s trust in previously inaccurate informants who were well or poorly informed: When past errors can be excused. Child Development, 80, 23–27. O’Neill, D. K. (1996). Two-year-old children’s sensitivity to a parent’s knowledge state when making requests. Child Development, 67, 659–677. Pasquini, E. S., Corriveau, K. H., Koenig, M. A., & Harris, P. L. (2007). Preschoolers monitor the relative accuracy of informants. Developmental Psychology, 43, 1216–1226. Quine, W. V., & Ullian, J. S. (1970). The web of belief. New York, NY: Random House. Rajan, V., Cuevas, K., & Bell, M. A. (2014). The contribution of executive function to source memory development in early childhood. Journal of Cognition and Development, 15, 304–324. Reid, T. (1764/1983). Section XXIV. Of the analogy between perception and the credit we give to human testimony. In R. Beanblossom & K. Lehrer (Eds.), Thomas Reid’s inquiry and essays. Indianapolis, IN: Hackett Publishing. Roberts, K. P. (2002). Children’s ability to distinguish between memories from multiple sources: Implications for quality and accuracy of eyewitness statements. Developmental Review, 22, 403–435. Robinson, E. J., & Whitcombe, E. L. (2003). Children’s suggestibility in relation to their understanding about sources of knowledge. Child Development, 74(1), 48–62. Ruffman, T. (1999). Children’s understanding of logical inconsistency. Child Development, 70(4), 872–886. Sabbagh, M. A., & Shafman, D. (2009). How children block learning from ignorant speakers. Cognition, 112, 415–422. Scofield, J., & Behrend, D. A. (2008). Learning words from reliable and unreliable speakers. Cognitive Development, 23, 278–290. Senju, A., & Csibra, G. (2008). Gaze following in human infants depends on communicative signals. Current Biology, 18(9), 668–671. Shafto, P., Eaves, B., Navarro, D. J., & Perfors, A. (2012). Epistemic trust: Modeling children’s reasoning about others’ knowledge and intent. Developmental Science, 15, 436–447. Shtulman, A., & Carey, S. (2007). Improbable or impossible? How children reason about the possibility of extraordinary events. Child Development, 78(3), 1015–1032. Sobel, D. M., & Macris, D. M. (2013). Children’s understanding of speaker reliability between lexical and syntactic knowledge. Developmental Psychology, 49(3), 523–532. Sperber, D., & Wilson, D. (1986). Precis of relevance: Communication and cognition. Behavioral and Brain Sciences, 10, 697–754. Sperber, D., Cle´ment, F., Heintz, C., Mascaro, O., Mercier, H., Origgi, G., et al. (2010). Epistemic vigilance. Mind & Language, 25(4), 359–393. Stenberg, G. (2009). Selectivity in infant social referencing. Infancy, 14, 457–473. Stephens, E., Corriveau, K., & Koenig, M. (2013). Knowing how you know: Preschoolers show enhanced monitoring of speakers relative to other sources of information. In Poster presented at the biennial meeting of the cognitive development society, Memphis, TN. Stephens, E., & Koenig, M. (under review). The nature of the error is key: Children’s selective learning in semantic and episodic domains. Sutherland, S. L., & Friedman, O. (2013). Just pretending can be really learning: Children use pretend play as a source for acquiring generic knowledge. Developmental Psychology, 49, 1660–1668. Tummeltshammer, K. S., Wu, R. W., & Kirkham, N. Z. (2013). 8-month-olds know whose face is reliable. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th annual conference of the cognitive science society. Austin, TX: Cognitive Science Society.


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Tummeltshammer, K. S., Wu, R., Sobel, D. M., & Kirkham, N. Z. (2014). Infants track the reliability of potential informants. Psychological Science, 25, 1730–1738. Vaish, A., Carpenter, M., & Tomasello, M. (2009). Sympathy through affective perspective taking and its relation to prosocial behavior in toddlers. Developmental Psychology, 45, 534–543. Vaish, A., Grossmann, T., & Woodward, A. (2008). Not all emotions are created equal: The negativity bias in social-emotional development. Psychological Bulletin, 134, 383–403. van Cleve, J. (2006). Reid on the credit of human testimony. In J. Lackey, & E. Sosa (Eds.), The epistemology of testimony (pp. 50–74). Oxford: Oxford University Press. Vanderbilt, K. E., Liu, D., & Heyman, G. D. (2011). The development of distrust. Child Development, 82, 1372–1380. VanderBorght, M., & Jaswal, V. K. (2009). Who knows best? Preschoolers sometimes prefer child informants over adult informants. Infant and Child Development, 18, 61–71. Whitcombe, E. L., & Robinson, E. J. (2000). Children’s decisions about what to believe and their ability to report the source of their belief. Cognitive Development, 15, 329–346. Williams, M. (2000). Dretske on epistemic entitlement. Philosophy and Phenomenological Research, 60, 607–612. Woolley, J. D., & Ghossainy, M. E. (2013). Revisiting the fantasy–reality distinction: Children as naı¨ve skeptics. Child Development, 84(5), 1496–1510.


CHAPTER SIX

Beyond Sally's Missing Marble: Further Development in Children's Understanding of Mind and Emotion in Middle Childhood Kristin Hansen Lagattuta1, Hannah J. Kramer, Katie Kennedy, Karen Hjortsvang, Deborah Goldfarb, Sarah Tashjian Department of Psychology and Center for Mind and Brain, University of California, Davis, California, USA 1 Corresponding author: e-mail address: [email protected]

Contents 1. Introduction 2. Age-Related Improvements in Theory of Mind in Middle Childhood 2.1 Developing an Interpretive Understanding of Mind 2.2 Additional Strategies for Studying Children's Understanding of Interpretation 2.3 Understanding Diversity in Emotional Responses 2.4 Children's Ability to Integrate Experiences and Mental States over Time 2.5 Children's Understanding of Thinking and Emotions More Broadly 2.6 Revisiting the False-Belief Task: How to Make It More Difficult 2.7 Further Tests of Advanced Theory of Mind in Middle Childhood 3. Individual Differences in Theory of Mind in Middle Childhood 3.1 Executive Function 3.2 Parent–Child Interactions 3.3 Maltreatment: An Extreme Negative Family Environment 3.4 Siblings 3.5 Peer Relationships 4. Conclusions References

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Abstract Research on the development of theory of mind (ToM), the understanding of people in relation to mental states and emotions, has been a vibrant area of cognitive development research. Because the dominant focus has been addressing when children acquire a ToM, researchers have concentrated their efforts on studying the emergence of psychological understanding during infancy and early childhood. Here, the benchmark test has been the false-belief task, the awareness that the mind can misrepresent reality. While understanding false belief is a critical milestone achieved by the age of 4 or 5,


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children make further advances in their knowledge about mental states and emotions during middle childhood and beyond. Thus, a comprehensive understanding of children's sociocognitive abilities in older age groups is necessary to understand more fully the course of ToM development. The aim of this review is to outline continued development in ToM during middle childhood. In particular, we focus on children's understanding of interpretation—that different minds can construct different interpretations of the same reality. Additionally, we consider children's growing understanding of how mental states (thoughts, emotions, decisions) derive from personal experiences, cohere across time, and interconnect (e.g., thoughts shape emotions). We close with a discussion of the surprising paucity of studies investigating individual differences in ToM beyond age 6. Our hope is that this chapter will invigorate empirical interest in moving the pendulum toward the opposite research direction—toward exploring strengths, limitations, variability, and persistent errors in developing theories of mind across the life span.

1. INTRODUCTION Fifteen years ago, as a graduate student at the University of Michigan, I collaborated with Henry Wellman to write “Developing understandings of mind” for the volume, Understanding other minds: Perspective from developmental cognitive neuroscience, edited by Baron-Cohen, Tager-Flusberg, and Cohen (2000). My primary contributions included evaluating sources of individual differences in children’s understanding of their own and others’ mental states as well as describing further developments in theory of mind (ToM) post preschool. Notably, the section on individual differences focused entirely on children between 3 and 6 years of age, and “later developments” were brief compared to the rich discussions of preschoolers’ ToM; both sections reflecting the state of the art of the literature at that time. Since then, research on infant ToM has burgeoned as developmental scientists have devised creative strategies to utilize nonverbal procedures to assess potential ToM insights that occur prior to children’s third birthday (see Frye & Moore, 2014; Legerstee, 2005; Sodian, 2011). Empirical studies on preschoolers’ ToM have also remained strong (see Wellman, 2011, 2014). In contrast, investigation of further developments in ToM after age 6, including attention to sources of variability in older age groups, has not experienced parallel growth (see Miller, 2012; Pillow, 2012). This chapter shifts focus toward what develops post preschool, after children have already acquired a solid foundation of knowledge about mental states. Our aim is to catalyze more researchers to expand inquiry of children’s psychological understanding to middle childhood and beyond. We do so by

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integrating current and previous research on children’s more advanced understanding of mind, with emphasis on their developing knowledge about diversity in interpretation, as well as their reasoning about interconnections among different mental states. We draw primarily from behavioral studies with typically developing children. Whereas some of these investigations have been specifically framed as explorations of advanced ToM, others fall under related headings of social cognition, emotion understanding, or metacognition. In the final section, we consider sources of individual differences in ToM in middle childhood, particularly executive function, parent–child interactions, siblings, and peer relationships. Of central interest is the extent to which these known predictors of variability in preschool ToM may continue to exert an impact later in development.

2. AGE-RELATED IMPROVEMENTS IN THEORY OF MIND IN MIDDLE CHILDHOOD The term theory of mind is often used interchangeably with false-belief understanding (awareness that the mind can misrepresent reality), the gold standard for assessing young children’s understanding of mind. Research on false belief has dominated the field since Wimmer and Perner (1983) first reported that children younger than 4 to 5 years of age expect people’s beliefs to reflect reality: Sally will search for her marble where it really is hidden (where Anne moved the marble) versus where Sally last left it, even though she never saw Anne place the marble in that new location (the classic Sally– Anne task). Later variations revealed that children younger than 5 also have difficulty reporting their own, as well as a friend’s, false beliefs about unexpected contents (e.g., that they had first mistakenly believed a raisin box contained raisins after finding out ribbons were actually inside) (Gopnik & Astington, 1988). A meta-analysis of more than 500 false-belief conditions with data collected from several different countries revealed that, despite variations in story characters, content, and questioning, children between the ages of 2.5 and 5 years go from consistently failing false-belief tasks to consistently passing them (Wellman, Cross, & Watson, 2001). These data have been interpreted as evidence that between the ages of 3 and 5 children transition from viewing the mind and the world as one and the same (that the mind accurately copies reality), to a new conceptual awareness that the mind and world are separate, and thus, the mind may misrepresent the true state of the world (Wellman, 1990, 2011).

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Although understanding false belief marks an important milestone in ToM development, it certainly does not equip children with all they need to know about people’s lives and minds. The relative lack of empirical investigation into advances in ToM during middle childhood is actually surprising given the many social and cognitive changes that occur between 6 and 12 years of age. Children spend an increasing number of hours with nonfamilial adults and peers (Larson & Verma, 1999), many coming from different racial and ethnic backgrounds, with different life experiences, customs, values, beliefs, and preferences (Hughes et al., 2006). Social comparison comes to the forefront, as children more routinely compare their knowledge, competence, and traits to that of their peers (Wood, 1989) and care more about how other people evaluate them (Harter, 1988). During middle childhood, children’s brains also undergo increased myelination of the frontal cortex, further synaptogenesis, and show greater EEG coherence among different brain regions, all of which improve the efficiency of information processing (see Lightfoot, Cole, & Cole, 2013). Moreover, there is mounting evidence that brain areas utilized while attending to or reasoning about mental states become less diffuse and more specialized throughout the course of middle childhood into adolescence (Gweon, Dodell-Feder, Bedny, & Saxe, 2012; Kobayashi, Glover, & Temple, 2007; Saxe, Whitfield-Gabrieli, Scholz, & Pelphrey, 2009). This constellation of social, biological, and cognitive changes likely coincides with, as well as propels, further development in psychological understanding.

2.1. Developing an Interpretive Understanding of Mind We begin our discussion on developmental changes in ToM during middle childhood with a focus on children’s understanding of the interpretive nature of the mind—recognition that individuals can have diverse interpretations of the same situation (Chandler & Helm, 1984). In a standard interpretive theory of mind (IToM) task, participants are shown a complete picture (e.g., a ship), and then the image is covered so that only a small, nonidentifiable part is left showing (e.g., a line). Participants predict how knowledgeable (prior experience with the full picture) and naı¨ve people (no prior experience with the unobstructed view) will interpret the ambiguous picture. Across several studies, results indicate that children do not typically succeed on IToM tasks until they are 6 to 7 years of age, with further improvement continuing into adulthood (Carpendale & Chandler, 1996; Lagattuta, Sayfan, & Blattman, 2010; Lagattuta, Sayfan, & Harvey, 2014; Ross, Recchia, & Carpendale,


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2005). This contrasts with the timetable for false-belief tasks, which are typically mastered by age 5 (Wellman et al., 2001). Researchers who have tested the same children on both false-belief and IToM tasks report that children’s pass rates for false-belief measures are significantly higher than IToM measures (Carpendale & Chandler, 1996; Lalonde & Chandler, 2002; Mull & Evans, 2010; Pillow & Mash, 1999). IToM tasks are presumably more difficult than false-belief tasks because they require children to actively construct different beliefs for different individuals and to recognize that one event can provoke multiple interpretations. More recent studies, using new variations of IToM tasks, have revealed intriguing developmental bumps during middle childhood as well as errors that persist into adulthood. For example, in Lagattuta, Sayfan, et al. (2010), we presented 4- to 9-year-olds and adults with three types of IToM trials: relevant-past (current picture is ambiguous, only one character had prior access to full picture), distinct-past (current picture is ambiguous, different characters previously viewed different full pictures), and irrelevant-past (current picture is clear, no past experience needed to identify). Participants predicted and explained how one knowledgeable and two naı¨ve characters would interpret these occluded images. Consistent with previous research, performance on the standard task (relevant-past) improved with age, with most 6- to 7-year-olds passing. Yet, on distinct-past trials 6- to 7-year-olds often attributed random novel interpretations to naı¨ve characters; they failed to recognize that a person’s past experience would bias their interpretation in a specific way. Moreover, on the irrelevant-past trials, 6- to 7-year-olds often overextended the rule that different people with different past experiences will have different interpretations. For example, a person who had not previously seen the full flower picture will think something other than “flowers” even though she sees flowers through the occluder. This overinterpretive mind error appeared in both predictions (suggesting different interpretations) and explanations (overuse of past experience explanations) and followed a U-shaped curve, with errors reaching their peak at ages 6 to 7 when children first consistently pass standard IToM tasks. These data indicate that as children develop increased awareness of mental diversity, they sometimes forget that people can share common ground: They expect diversity when there should be none. Even young adults were not immune to interpretive errors. They frequently judged that two naı¨ve characters would share the same thoughts (e.g., both interpret an ambiguous curve as a “cantaloupe,” or a “frog”), reflecting a false consensus effect (see Dunning & Hayes, 1996).

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Given that interpretive errors continue across a wide age range, we devised additional strategies for testing children’s and adults’ perspectivetaking. In particular, we were interested in replicating the age-specific overinterpretive mind error. We also wanted to test more rigorously older children’s and adults’ ability to simulate a naı¨ve perspective. Lagattuta, Sayfan, et al. (2014) presented 4- to 10-year-olds and adults with full pictures that were occluded to reveal either ambiguous or obvious features. Participants predicted how a naı¨ve person (no prior experience with the full picture) would interpret the occluded picture, and they rated the likelihood (0–10) that a naı¨ve person would have one of several thoughts, with one interpretation being the actual picture. As with Lagattuta, Sayfan, et al. (2010), results on the obvious trials indicated a U-shaped developmental curve: 6- to 10-year-olds provided higher probability ratings than preschoolers and adults that a naı¨ve person would have different interpretations of a clear picture. Although there was significant improvement between 4 and 10 years in attributing novel (false) interpretations to a naı¨ve person on ambiguous picture trials, all age groups had difficulty taking a naı¨ve stance. When compared to a separate group of participants who had no prior access to full pictures, both knowledgeable children and adults overestimated the likelihood that a naı¨ve person would guess the true pictures (a main effect that did not interact with age). Thus, although qualitatively children know by ages 6 to 7 years that a naı¨ve person would interpret an ambiguous picture in a novel way, their quantitative assessments reveal an inability to take a fully naı¨ve perspective. Related studies on hindsight bias, curse of knowledge, and epistemic egocentrism further confirm that once children and adults are contaminated with privileged knowledge, they fail to reason accurately from a naı¨ve perspective (Bernstein, Erdfelder, Meltzoff, Peria, & Loftus, 2011; Birch & Bloom, 2004; Pohl, Bayen, & Martin, 2010; Royzman, Cassidy, & Baron, 2003). Combined, these data indicate a protracted period of egocentric bias that continues into adulthood.

2.2. Additional Strategies for Studying Children's Understanding of Interpretation Children’s and adults’ less than perfect performance on IToM restrictedview tasks raises questions regarding their appreciation of interpretive diversity in more social contexts. Ross et al. (2005) presented 4- to 9-year-olds with stories of interpersonal sibling conflicts in which the child guilty of blame was ambiguous (e.g., arguments over whose turn it was to ride


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in the front seat, whether a block tower should have been cleaned up). Children evaluated each person’s perspective regarding who was at fault and explained their judgments. As with the IToM tasks described earlier, children 7 years of age or older exhibited better recognition than younger children that attributions of blame are open to multiple interpretations (no one is clearly right or wrong). This ability to recognize that there can be several legitimate ways to interpret the same situation has important implications for developing tolerance. Indeed, Wainryb, Shaw, Langley, Cottam, and Lewis (2004) report that 7- and 9-year-olds more frequently endorse tolerant beliefs (people can hold beliefs that are different from their own) and relativist beliefs (multiple beliefs can be correct) compared to 5-year-olds. Developmentally, although children come to have multiplistic beliefs about matters of preference in early to middle childhood (e.g., people can like different foods), they have difficulty recognizing that people can hold different perspectives on matters of fact (scientific facts) or morality until late childhood and adolescence (see also Hofer & Pintrich, 1997; Wainryb, 1991; Wainryb & Ford, 1998; Wright, 2012). This is perhaps not surprising given that adults also have difficulty appreciating that there can be multiple legitimate perspectives in matters of politics and religion (Haidt, 2013) and even science (Kahan, Jenkins-Smith, & Braman, 2011). A further way to test improvement in recognizing interpretive diversity in middle childhood includes investigating children’s knowledge of the factors that affect whether a person can serve as an impartial judge. Mills and Keil (2008) presented 5- to 14-year-olds with vignettes featuring judges presiding over objective (e.g., a running race) or subjective contests (e.g., an art contest). Children rated judges on a 1 to 5 scale reflecting how good of a job they thought they would do selecting the actual winners. Judges varied in their relationship (e.g., friend, parent, teacher) and bias toward the contestants (positive, negative, neutral, none). Although even kindergarteners recognized that a person with a negative bias would make a poor evaluator, it was not until fourth grade that children understood that impartiality was harder to achieve in subjective versus objective contests. This distinction by contest type significantly widened with age. It was not until eighth grade that children recognized that teachers make better judges than parents or friends and that judge neutrality would be more important in subjective situations. In additional studies, Mills and colleagues further documented that between 6 and 13 years of age, children exhibit increasing vigilance toward the potential for biased interpretations, they better recognize sources

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of impartiality, and they better understand that the threat of biased evaluations is higher in subjective versus objective events (Mills, Al-Jabari, & Arachacki, 2012; Mills & Grant, 2009). Related research has also shown that by 9 to 10 years of age, children start to exhibit skepticism about people’s claims; for example, they recognize that others may be biased to portray their traits (e.g., intelligence) more positively than reality (Heyman, Fu, & Lee, 2007; Heyman & Legare, 2005).

2.3. Understanding Diversity in Emotional Responses Researchers have also investigated children’s understanding of mental diversity by assessing their intuitions about how the same event can elicit different emotions in different people. Gnepp, McKee, and Domanic (1987) examined 5- to 8-year-olds’ recognition that whereas some events typically cause all people to feel one way (e.g., child drops and breaks favorite toy), other situations are equivocal in the sense that they can evoke varied, and even opposite-valenced, emotions in different people (e.g., child is approached by a dog). Results indicated greater awareness of emotionally equivocal events in 8-year-olds compared to 5-year-olds. In a later extension, Gnepp and Klayman (1992) found that children 8 years and older also exhibited better understanding than 6-year-olds that the same person could experience mixed emotions in equivocal situations (see also Larsen, To, & Fireman, 2007); yet, even adults only recognized the potential for mixed emotions in equivocal situations about 70% of the time. Also intriguing was that compared to younger and older participants, 8-year-olds more often judged that there was potential for mixed emotions in clear-cut, unequivocal situations. This is reminiscent of the overinterpretive mind error shown by Lagattuta, Sayfan, et al. (2010, 2014), on the irrelevant-past, obvious picture trials: When children first solidify their knowledge about interpretive diversity, they may also “overexpect” the likelihood of multiple emotions in the same situation. Diversity in emotional experience arises not only from equivocal situations but also by the meaning events hold for individuals with respect to their social category (e.g., age, gender), cognitive mental states, and previous life experiences. Sayfan and Lagattuta (2008) presented 3- to 7-year-olds and adults with vignettes featuring a child protagonist accompanied by an adult or an infant in fear-inducing situations involving real (e.g., snake) or imaginary creatures (e.g., monster). Participants predicted and explained how


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each character felt. Although all age groups expected more diverse interindividual fear reactions in response to imaginary versus real threats, 3- to 5-year-olds thought that babies would be more afraid than children and adults across situations. Only 7-year-olds and adults recognized that babies (who know less) and adults (who know more) would experience less fear than children (see Miller, Hardin, & Montgomery, 2003 for similar findings on young children’s misconceptions about infants’ knowledge). Participants in these older age groups also explicitly referred to cognitive mental states as mediating between situations and emotions more often than younger children. In a further extension, Sayfan and Lagattuta (2009) confirmed that between 4 and 7 years of age children come to expect more person-toperson diversity in fear reactions, they more often explain emotional diversity in relation to cognitive mental states, and they exhibit better understanding of how mental strategies can reduce fears. All age groups, however, predicted that mothers would be more fearful than fathers, providing a further example of how social categories shape children’s judgments about people’s psychological states. To explore more systematically children’s beliefs about how different people with different thoughts will feel in response to the same objective situation, Bamford and Lagattuta (2012) presented 5- to 10-year-olds with vignettes featuring two characters who experience negative (e.g., getting hurt), positive (e.g., going on a beach vacation), or ambiguous events (e.g., getting a new teacher). Both characters initially experience the same exact emotion; however, one character then starts to think optimistically, whereas the other thinks pessimistically about the situation. Children predicted and explained each character’s current emotions. Although all age groups expected the two characters’ emotions to be most divergent in ambiguous situations and more similar in negative situations, there was a significant increase between 5 and 10 years of age in judging that differences in mental reframing would lead to larger disparities in emotion experience. Explanations explicitly referring to characters’ thoughts when explaining emotion divergence also increased with age (see also Lagattuta, Wellman, & Flavell, 1997; Harris, Guz, Lipian, & Man-Shu, 1985 for age-related improvements in understanding the impact of rumination and distraction on emotions). Related studies further show that between the ages of 3 and 7 years, children exhibit improving knowledge that a person who is naı¨ve to another’s prior misfortune will not experience the same emotions nor make the same decisions in response to the same current event (Lagattuta, 2007; Lagattuta & Wellman, 2001; Lagattuta et al., 1997).

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2.4. Children's Ability to Integrate Experiences and Mental States over Time As discussed in Lagattuta (2014), if a ToM is really a theory, then one would expect children to acquire more complex causal-explanatory understandings about psychological states as they grow older. One approach we have taken to address this question is to explore age-related improvements in children’s ability to reason about interconnections among people’s unique life experiences and their diverse ways of thinking, feeling, and behaving. Such connections are stressed in scientific theories of mental health (e.g., Ellis, 1991) as well as in more everyday intuitions. In our initial attempts, we presented 3- to 6-year-olds with vignettes featuring characters who experienced negative events in the past and many days later felt sad after reencountering a visual reminder of a past traumatic episode (Lagattuta et al., 1997). Children explained the cause of the current sadness. Although 3- and 4-year-olds could explain sadness as caused by thinking about the past, only 5- to 6-year-olds demonstrated consistent understanding that visual cues had caused the past-oriented thoughts (see also Pons, Harris, & de Rosnay, 2004). Older children also better recognized that even cues that only looked similar to past objects (e.g., a car that looks similar to one that previously broke a child’s bicycle) could reinstate the negative thoughts and emotions. In later studies, we continued to document unsuspected strengths in preschoolers’ knowledge combined with further enrichment in middle childhood. For example, whereas 3- to 4-year-olds can explain unusual negative emotions as caused by past-oriented thinking, it is not until 5 to 6 years of age that they also consistently explain typical negative emotions, typical or unusual positive emotions, and behaviors as elicited by being reminded about the past (Lagattuta & Wellman, 2001). Between 3 and 6 years of age, children also show greater understanding that negative past events can push forward into the future, making people feel worried due to anticipation of reoccurrence (Lagattuta, 2007). Lagattuta and Sayfan (2013) extended this investigation by showing 4- to 10-year-olds and adults multiple past event trials where a “perpetrator” acted positively twice (PP), negatively twice (NN), or both negatively and positively (NP, PN) toward a target character. Participants predicted how the target would think (6-point scale: think something bad definitely will happen to think something good definitely will happen), feel (6-point scale: very worried to very happy), and act (4-point scale: stay very far away to go very close) upon encountering that same perpetrator several days later. All age groups provided significantly more positive thought, emotion,


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and decision forecasts for PP > NP > PN > NN trials, with this differentiation by risk type widening between 4 and 10 years of age and between childhood and adulthood. Divergent forecasting for NP and PN trials is most intriguing because these are mathematically equal (50–50) situations. Here, older children’s and adults’ greater use of a heuristic-based approach to forecast mental states (i.e., more heavily weighting the most recent event, particularly when negative) appeared not only in verbal judgments but also in eye movements. With increasing age, participants exhibited greater biased visual attention to recent past events, with this attention bias most extreme in the PN condition. Thus, as they grow older, children learn strategies for integrating and weighting social “evidence,” providing children with a more complex and nuanced theory about how peoples’ prior life experiences shape their thoughts, emotions, and decisions.

2.5. Children's Understanding of Thinking and Emotions More Broadly Children’s knowledge about the process of thinking itself also undergoes further development during middle childhood. Research by Flavell, Green, and Flavell (1993, 1995) reveals that although children younger than 5 to 6 years know that a person is thinking during effortful cognitive tasks (when they are displaying facial and gestural “thinking pose” cues), they do not view the mind as continuously having thoughts and ideas, or a “stream of consciousness” ( James, 1890). Children 7 years and older demonstrate more awareness of the content of their own thought processes (Flavell & Green, 1999; Flavell et al., 1995), they show stronger knowledge that the same event can provoke different trains of thought in different people (Eisbach, 2004), and they better distinguish between “good” (e.g., planning, hypothesis testing) versus “bad” (e.g., guessing) thinking (Amsterlaw, 2006) compared to younger children. More generally, children’s metacognitive skills improve during middle childhood enabling them to better monitor their certainty about what they know and do not know and what they are likely versus unlikely to remember (Ghetti, Castelli, & Lyons, 2010; Ghetti, Hembacher, & Coughlin, 2013; Roebers, 2002; Rohwer, Kloo, & Perner, 2012). After age 7, children also better appreciate that the mind is not fully controllable: Thoughts can be intrusive and involuntary, and they can be difficult to stop or repress (Flavell, Green, & Flavell, 1998). Advances in children’s understanding of thinking and introspection during middle childhood coincide with their improving emotion regulation skills. Children 7 to 8 years and older demonstrate sophisticated knowledge that mental strategies—including distraction, cognitive reframing, positive

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thinking (Altshuler & Ruble, 1989; Harris et al., 1985; Pons et al., 2004), and prayer (Bamford & Lagattuta, 2010)—can make people feel better in negative situations. Four- to 6-year-olds exhibit some earlier insights when they are queried about autobiographical events (Davis, Levine, Lench, & Quas, 2010), when they are given a thought bubble prompt (Sayfan & Lagattuta, 2009), when they suggest strategies for controlling fears of imaginary creatures (Sayfan & Lagattuta, 2009), and when they are asked about ambiguous situations as opposed to clearly negative events (Bamford & Lagattuta, 2012). Notably, young children’s knowledge of “anticoping” (how negative thoughts can induce negative emotions in a positive event) developmentally precedes their knowledge of “coping” (how positive thoughts can improve emotions in a negative situation) (Bamford & Lagattuta, 2012; Lagattuta & Wellman, 2001). Incidentally, the same pattern occurs in children’s understanding of how emotions impact thinking. Whereas 5- to 6-year-olds know that negative emotions can make it difficult to think and problem solve, it is not until age 7 that children believe positive emotions could enhance thinking (Amsterlaw, Lagattuta, & Meltzoff, 2009).

2.6. Revisiting the False-Belief Task: How to Make It More Difficult Although we have focused this chapter on further development in ToM after children can pass standard false-belief tasks, it is worth highlighting growing evidence that children fail to master some variations of false-belief tasks until age 7 or later. One glitch in children’s early knowledge is that adding emotions makes the false-belief task more challenging (see Harris, de Rosnay, & Ronfard, 2014 for a review). In an initial set of experiments, Harris, Johnson, Hutton, Andrews, and Cooke (1989) introduced young children to a stuffed animal that played tricks by hiding an undesirable item in a container that appeared to contain another animal’s favorite food or drink. Although children 5 years and older recognized that the naı¨ve animal would think a desirable food/drink was inside the container, they still expected the (naı¨ve) animal to feel sad prior to opening the container. Bradmetz and Schneider (1999) demonstrated a similar finding using a classic fairy tale: Children younger than 7 stated that Little Red Riding Hood thought her grandmother was in the bed but still reported that she felt scared (see also Ronford & Harris, 2014). This lag in false-belief understanding when judging emotions also appears in a first-person context. Four- to 5-year-olds can accurately report their prior false belief but misreport their


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prior emotion on an unexpected contents task (Bender, Pons, Harris, & de Rosnay, 2011). In our recent work, we have shown that adding negative emotional stimuli (e.g., scary hidden pictures) also makes IToM restricted-picture tasks more challenging (Kennedy, Lagattuta, Kramer, & Sayfan, 2014). A further way to make false-belief tasks more difficult is to add a moral dimension. For example, Killen, Mulvey, Richardson, Jampol, and Woodward (2011) transformed the traditional false-belief task into an “accidental transgressor” variant. One child throws away a paper bag in the classroom that, unbeknownst to him, contains another child’s special cupcake. Seven- to 8-year-olds were more likely than younger children to attribute a false belief to the accidental transgressor (see also Kalish & Cornelius, 2007) and to judge that the action was okay in light of the accidental transgressor’s positive intentions and false beliefs. Advances in middle childhood in the ability to integrate morality and ToM can also be seen in children’s reasoning about situations where desires conflict with rules (see Lagattuta & Weller, 2014 for a review). Although even 2- to 3-year-olds demonstrate a solid grasp about relations between desire fulfillment and emotions (e.g., Wellman & Banerjee, 1991), children younger than 7 years of age have difficulty understanding that transgressing (getting what you want by breaking rules) “feels bad” and that acts of willpower (intentionally inhibiting desires to abide by rules) “feel good” (Lagattuta, 2005, 2008; Lagattuta, Nucci, & Bosacki, 2010). More recently, we have documented parallel improvements during middle childhood in children’s reasoning about emotions caused by foregoing desires to engage in altruistic action or by fulfiling personal desires and ignoring others in need (Weller & Lagattuta, 2013, 2014). Finally, it is possible to also delay the pass rate on false-belief tasks to 6 to 7 years of age by changing the design so that there are two or more possible wrong locations (as opposed to one) or by testing children on seemingly simpler “true belief” variants. For example, Fabricius and Khalil (2003) found that when queried about where a character will look for a hidden object on a 3-location false-belief task, 4- and 5-year-olds often predicted that the person would search in an unjustified wrong location; they failed to recognize that he or she would be wrong in a specific way. Even 6-year-olds did not perform at ceiling. Fabricius, Boyer, Weimer, and Carroll (2010) further documented that when children start to reliably pass standard false-belief tasks (at 4.5–5.5 years of age), they make more mistakes on “true belief” variants than younger or older children: They erroneously attribute false beliefs

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to knowledgeable people. These data parallel findings from Lagattuta, Sayfan, et al. (2010, 2014) discussed previously. When children first start to reliably pass standard IToM tasks at 6 to 7 years of age (a more advanced test than false belief ), they are most prone to making two types of errors: They fail to recognize that a person’s interpretation will be biased by his or her specific past experience, and they overextend the rule that different people with different past experiences should have different thoughts to situations where people should share common ground. Thus, similar to U-shaped developmental curves found in other areas of cognitive development, such as the acquisition of grammar rules (Kuczaj, 1977), advances in children’s understanding of mind may trigger the appearance of new errors less commonly made by younger or older age groups.

2.7. Further Tests of Advanced Theory of Mind in Middle Childhood Throughout this chapter, we have centered on advances in ToM post falsebelief understanding in relation to children’s understanding of diversity (and common ground) in interpretation as well as their knowledge about interconnections among people’s life experiences, thoughts, emotions, and decisions. This list is by no means exhaustive of measures to assess advances in ToM during middle childhood. Here, we describe four additional tasks researchers utilize to test psychological understanding in older children and adults: (1) Second-Order False Belief, (2) Reading the Mind in the Eyes, (3) Strange Stories, and (4) Faux Pas. While first-order ToM tasks test children’s knowledge about another person’s beliefs or feelings, second-order ToM measures test participants’ understanding about a person’s thoughts regarding how another person thinks or feels, typically involving misconceptions or deception (Miller, 2009, 2013; Perner & Wimmer, 1985). Third-order ToM tasks go a step further—testing knowledge about another person’s belief (Liddle & Nettle, 2006). The recursive level can be further compounded to fourth order, fifth order, and so on. In a review of the research, Miller (2009) concluded that most children pass second-order ToM tasks by 7 to 8 years of age, with emotional variants posing a greater challenge (this is similar to Harris and colleagues’ findings on false belief and emotion reviewed earlier). Although less frequently studied, children do not typically master thirdor fourth-order ToM measures until late childhood or adolescence; even 10- to 11-year-olds perform at chance given the mental gymnastics involved (Liddle & Nettle, 2006).


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Baron-Cohen, Jolliffe, Mortimore, and Robertson (1997) developed the Reading the Mind in the Eyes task as a nonverbal ToM measure useful for testing children and adults from atypical populations, especially those with language disabilities. The task consists of a series of photographs of faces (25 or more slides) that are occluded aside from the eye area. Participants identify what the person is thinking or feeling from this limited information using either a free-response or a forced-choice response format (with varying numbers of options). Mental states featured in these photographs include complex emotions (e.g., ashamed, serious, worried, friendly) and cognitive states (e.g., thinking, remembering). Baron-Cohen and colleagues also developed child versions with fewer items and simpler wording (BaronCohen, Wheelwright, Spong, Scahill, & Lawson, 2001). Studies utilizing the Eyes task with 6- to 12-year-olds are primarily comparative; for example, investigations of deficiencies in mindreading in children or adolescents with autism compared to a control group of typically developing children (e.g., Brent, Rios, Happe´, & Charman, 2004; Dorris, Epsie, Knott, & Salt, 2004). The focus of this research has not been on age-related improvements in performance during middle childhood in normative groups. From examining control group performance, however, it appears that 6- to 12-year-olds get about 60–70% correct and adults fail to reach ceiling as well (about 70% correct). This lack of ceiling effects makes it a promising measure for investigating further how children and adults perceive mental states and emotions across a wide age range. Another commonly used measure to assess ToM in middle childhood as well as atypical adult populations is the Strange Stories task, originally developed by Happe´ (1994). Participants listen to a set of 24 short stories that feature naturalistic social situations involving ambiguities or misconceptions about people’s mental states (e.g., lie, joke, persuade, sarcasm). Participants describe why they think the event took place, and their responses are coded for use of mentalistic explanations. Researchers have found that children and adolescents with developmental disorders (e.g., autism spectrum disorder) generally perform worse than typically developing controls matched on mental age (Happe´, 1994; Jolliffe & Baron-Cohen, 1999; Velloso, Duarte, & Schwartzman, 2013; White, Hill, Happe´, & Frith, 2009, but see Senju, Southgate, White, & Frith, 2009 for no differences). Three- to 12-year-olds with profound hearing impairments also demonstrate weaker understanding of sarcasm than matched controls, and even typically developing children fail to perform at ceiling (Peterson, Wellman, & Slaughter, 2012).

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The Faux Pas test (Baron-Cohen, O’Riordan, Stone, Jones, & Plaisted, 1999) is an advanced ToM task designed for children aged 5 years and older. It involves story vignettes where a person’s statement may be construed as negative or insulting, but are unintentional and made out of ignorance (e.g., a man says “I don’t think I’ve met this little boy” to a little girl with short hair). Children and adults who understand people’s intentions and knowledge state as criterial for assigning blame construe these situations as honest mistakes. Studies using the Faux Pas test have found age-related improvements between 5 and 11 years in being able to detect a faux pas and to identify accurately the “offender’s” ignorance and intention (Banerjee & Watling, 2005; Baron-Cohen et al., 1999). Comparative studies have further shown that children and adults with autism spectrum disorder have significant difficulties with this task, even those with high language proficiency and who have passed first- and second-order false-belief tasks (e.g., Zalla, Sav, Stopin, Ahade, & Leboyer, 2009).

3. INDIVIDUAL DIFFERENCES IN THEORY OF MIND IN MIDDLE CHILDHOOD We turn now to consider sources of variability in children’s more advanced understanding of mind and emotion during middle childhood. This section simultaneously functions as “future directions” because, as we will describe, researchers investigating individual differences in ToM in typically developing populations have overwhelmingly focused on 3- to 6-year-olds. Indeed, we draw from this extensive literature base with preschoolers as a foundation for identifying which cognitive and social factors may continue to predict variability in ToM between the ages of 6 and 12. More specifically, we focus on executive function, parent–child interactions (including maltreatment), siblings, and peer relationships.

3.1. Executive Function Executive function (EF) refers to a set of skills (i.e., working memory, planning, task switching, inhibitory control, and attention) necessary for carrying out higher order cognitive processes (e.g., Best & Miller, 2010). While these abilities continue to develop through adolescence into adulthood, the preschool years (3 to 5 years of age) represent one of the most rapid periods of growth (Best & Miller, 2010; Carlson & Moses, 2001; Prencipe et al., 2011; Zelazo & Carlson, 2012). Numerous studies have documented that


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preschoolers with higher EF perform better on ToM tasks compared to children matched in age with lower EF abilities (e.g., Carlson & Moses, 2001; Carlson, Moses, & Breton, 2002; Sabbagh, Xu, Carlson, Moses, & Lee, 2006). A recent meta-analysis by Devine and Hughes (2014), analyzing 100 studies conducted over the past 20 years with 3- to 6-year-olds, confirmed this significant EF–ToM link. Despite strong evidence for the importance of EF for young children’s ability to predict and explain mental states during the preschool years, few researchers have systematically investigated whether these same relations hold in older children (Miller, 2012). One possibility is that EF only matters early in development when children are first learning mental state concepts; once children have reached a certain threshold of ToM and EF skills, this relation may attenuate or disappear. In contrast, EF may potentially be important for implementing ToM in older children and throughout the lifespan (i.e., using what one knows about mental states to simulate differing perspectives, track mental states, and make psychological inferences, see Lagattuta, Sayfan, et al., 2014). We have tackled these questions in our research by assessing whether individual differences in EF (working memory and inhibitory control) predict 4- to 10-year-olds’ and adults’ knowledge about how past experience affects interpretation (IToM). Recall that in Lagattuta, Sayfan, et al. (2010), participants predicted how knowledgeable and naı¨ve characters would interpret mostly occluded pictures. Controlling for age, children and adults with higher EF more frequently suggested different interpretations for different characters on ambiguous picture trials. Indeed, once EF was entered into the model, age was no longer significant. In contrast, performance on obvious picture trials was only predicted by age: 6- and 7-year-olds most often made the overextension error by mistakenly suggesting unique (false) interpretations for naı¨ve characters. Lagattuta, Sayfan, et al. (2014) replicated the significant EF–IToM relation for ambiguous picture trials using a different IToM task that involved both thought predictions and thought probability ratings for naı¨ve characters. Curiously, although higher EF predicted more frequent suggestions of thoughts other than the actual picture and higher probability ratings for alternative interpretations across age groups, EF had no relation to probability ratings for the actual pictures. Thus, something other than EF is needed to explain more fully children’s and adults’ difficulty in simulating a naı¨ve perspective. Again, only age predicted responses when the picture was obvious. In a further extension, Kennedy, Lagattuta, and Sayfan (in press)

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documented that individual differences in EF also predict 4- to 10-year-olds’ and adults’ ability to explain mental diversity in relation to divergent past experiences. These findings from our recent research strongly suggest that EF skills are necessary for implementing mental state knowledge across a wide age range; well beyond any threshold in ToM or EF abilities reached by 5 to 6 years of age. This lays a foundation for future studies to investigate more fully the extent to which manipulating EF (e.g., by taxing EF, reducing EF demands, or training EF skills) could lead to differential performance on more advanced ToM measures in middle childhood and beyond. Such data would also be informative for devising interventions for children and adults from atypical populations.

3.2. Parent–Child Interactions Another highly researched area on individual differences in ToM concerns how young children’s family environment may contribute to their emerging psychological understanding. In particular, the quality of children’s attachment to caregivers and the frequency that children engage in causal-explanatory talk about mental states and emotions have been implicated as important factors. Several studies have found concurrent as well as longitudinal associations between attachment security and ToM in 2- to 6-year-olds (de Rosnay & Harris, 2002; Fonagy, Redfern, & Charman, 1997; Laible & Thompson, 1998; Meins, Fernyhough, Russell, & Clark-Carter, 1998; Repacholi & Trapolini, 2004; Steele, Steele, Croft, & Fonagy, 1999). Longitudinal and cross-sectional studies have further documented significant relations between parent–child discourse about mental states and preschoolers’ reasoning about beliefs and emotions (see Hughes, White, & Ensor, 2014 for a review). There is also mounting evidence that attachment security and parent–child discourse interconnect: More secure dyads not only have more frequent mentalistic conversations, but parent–child discourse quality mediates the relation between attachment and ToM (Mcquaid, Bigelow, McLaughlin, & MacLean, 2008; Meins et al., 2002; Ontai & Thompson, 2008; Raikes & Thompson, 2006). The question remains whether these same variables continue to predict more advanced ToM in middle childhood and beyond. Researchers examining relations between mental state language and ToM in 6- to 10-year-olds have found no significant association between children’s production of mental


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state terms and their performance on second-order false-belief tasks (Charman & Shmueli-Goetz, 1998; Longobardi, Spataro, & Renna, 2014; Meins, Fernyhough, Johnson, & Lidstone, 2006). Relations continue to exist, however, between older children’s comprehension of the meaning of different mental state terms (metalinguistic knowledge) and their performance on advanced ToM (Grazzani & Ornaghi, 2012; Longobardi et al., 2014); perhaps not surprising because metalinguistic awareness is a direct measure of ToM. These data indicate that parent–child conversations about mental states may be more critical for early acquisition of ToM, but less important once certain ToM levels have been mastered. Future research is needed to make a more definitive conclusion. Research on parent–child attachment in middle childhood has primarily focused on how attachment security predicts peer relationship quality and mental health (Kerns & Brumariu, 2014; Kerns & Richardson, 2005) rather than whether it predicts children’s reasoning about mental states and emotions. Yet, mental models of attachment relationships are presumed to incorporate thoughts, emotions, and beliefs about the self and others, stemming from early experiences with caregivers (Bowlby, 1973), a very ToM-like construct. Whereas attachment quality may predict children’s ability to pass tasks measuring belief and emotion knowledge in early childhood (as discussed previously), in middle childhood and later in life, attachment quality may instead predict nuances in ToM. That is, children and adults may use attachment relationships, whether secure or insecure, as a heuristic for predicting and explaining social behaviors and relationships. In our research, for example, we are investigating whether attachment security predicts how 4- to 12-year-olds and adults differentially weight social evidence (i.e., focus more on negative versus positive actions) to make future-oriented judgments about mental states and emotions. Potentially, older children and adults with insecure attachment may exhibit more pronounced negativity biases than securely attached individuals.

3.3. Maltreatment: An Extreme Negative Family Environment Research on the influence of “experiments of nature,” including childhood maltreatment, can also provide insight into how the family environment can influence ToM development. As noted by Kagan (2013) and Luke and Banerjee (2013a), maltreatment is a categorical term encompassing a variety of behaviors. Children’s conceptualization of their treatment, as influenced by a number of contextual factors (culture, familial structure and support,

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psychopathology), may play a large role in resulting effects. With that caveat in mind, researchers have found that relative to nonabused children, 3- to 8-year-olds with a history of maltreatment score lower on false-belief tasks, they have more difficulty interpreting and understanding situational determinants of emotions, they often misattribute hostile intent to other people in ambiguous situations, and they are hypersensitive to anger cues (Cicchetti & Ng, 2014; Cicchetti, Rogosch, Maughan, Toth, & Bruce, 2003; Pears & Fisher, 2005). Proposed contributors to these ToM difficulties include insecure attachment, lower rates of talking about mental states, and the parental displays of abnormal or aberrant emotions and behaviors (see Cicchetti & Ng, 2014 for a review). More specific to middle childhood, Luke and Banerjee (2013b) conducted a recent meta-analysis exploring maltreated children’s social understanding, with several studies in their dataset including 6- to 12-years-olds. Results were mixed as to whether maltreatment predicted lower ToM performance or had no relation to ToM, with the most consistent significant association occurring between maltreatment and impaired emotion understanding. Koizumi and Takagishi (2014) also analyzed ToM skills in 6- to 17-year-old children using the Reading the Mind in the Eyes task. Children in the maltreatment group performed worse at identifying emotions than nonmaltreated children, but only for positive faces. As these comparisons are cross-sectional, we can only speculate about the exact nature of these developmental pathways. For example, maltreated children may have a delayed ToM, an impaired ToM, or they may develop a different way of thinking about people’s emotions and mental states that is adaptive to their environment. Future longitudinal research is necessary to investigate these developmental changes, including what may be unique about the transition out of early childhood into middle childhood.

3.4. Siblings In addition to learning about psychological states from parents, children also acquire knowledge about desires, intentions, emotions, and beliefs from day-to-day interactions with siblings. Numerous studies have shown that preschoolers with more siblings, particularly older siblings, perform better on ToM tasks than singletons or children with only younger siblings (Farhadian, Gazanizad, & Shakerian, 2011; Jenkins & Astington, 1996; McAlister & Peterson, 2006, 2007, 2013; Perner, Ruffman, & Leekam, 1994; Ruffman, Perner, Naito, Parkin, & Clements, 1998). These studies


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provide evidence for an apprenticeship model in which younger children learn from their older siblings via opportunities to participate in or overhear mental state conversations (e.g., Brown, Donelan-McCall, & Dunn, 1996; Hughes et al., 2014), as well as through opportunities to engage in pretend play (Harris, 2005) and conflict (e.g., Ross et al., 2005) with more socially and cognitively advanced social partners. The findings are not consistent as some researchers have found no sibling–ToM associations (Cole & Mitchell, 2000) or have documented that the presence of older siblings may reduce ToM competencies in children with autism (O’Brien, Slaughter, & Peterson, 2011). This prior research on sibling–ToM relations has focused on children 6 years of age and younger, a time period when ToM is first developing and children spend most of their waking hours with family members. Once children have reached a certain threshold of mental state understanding and they spend more time with peers in middle childhood, connections between siblings and ToM may disappear. Indeed, two recently published studies document null relations between siblings and ToM in 6- to 10-year-olds, as measured through first- and second-order false-belief tasks (Calero, Salles, Semelman, & Sigman, 2013; Miller, 2013). The question is far from resolved, however, and may depend upon sample size as well as the specific ToM measures used. For example, Kennedy et al. (in press) found that even when controlling for age and executive function, 4- to 10-year-olds with a greater number of older or more same-sex siblings demonstrated superior performance on an IToM measure (distinct-past and relevant-past trials from Lagattuta, Sayfan, et al., 2010), both in suggesting interpretations for knowledgeable and naı¨ve characters and in explaining diversity in relation to differences in past experience. Clearly additional work is needed to explore more fully whether and how sibling relationships continue to shape advances in ToM during middle childhood.

3.5. Peer Relationships Another widely studied ToM individual difference variable is peer relationship quality. Slaughter, Dennis, and Pritchard (2002) found that 4- to 6-year-olds classified as popular scored higher on ToM tasks than children classified as rejected. Results from a recent longitudinal study in 5- to 7-year-olds indicate that prosocial behavior mediates the relation between ToM understanding and success with peers (Caputi, Lecce, Pagnin, & Banerjee, 2012). Related studies have also shown significant associations

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between ToM and peer acceptance as rated by teachers or peers: More socially competent children better predict and explain others’ mental states and emotions, show more empathy and cooperation with peers, and engage in more mental state talk with friends (see Lagattuta, Hjortsvang, & Kennedy, 2014 for a review). These relations between ToM and social competence are believed to be bidirectional in the sense that ToM abilities help young children develop friendships, which then later serve as a training ground for improving their social skills and psychological understandings (see Carpendale & Lewis, 2004). Relations between ToM and peer relationship quality continue into middle childhood and early adolescence (Bosacki, 2000; Bosacki & Astington, 1999). These associations hold more strongly for predicting peer rejection rather than peer acceptance or popularity. For example, Devine and Hughes (2013) found that 8- to 13-year-olds who showed weaker ToM knowledge on the Strange Stories task self-reported higher loneliness and peer rejection. Banerjee, Watling, and Caputi (2011) also found that poor ToM understanding (as measured by the Faux Pas test) at ages 7 and 9 predicted higher rates of peer rejection the following year. The converse relation also held: Peer rejection at earlier time-points predicted later deficits in ToM performance. Nevertheless, it is important to point out that in some cases, bullies have also been found to have average or superior ToM in preschool and middle childhood; thus, children may not always use ToM skills for prosocial goals (Gasser & Keller, 2009; Hughes & Leekam, 2004; Sutton, Smith, & Swettenham, 1999). When compared to other individual difference variables studied extensively in the preschool years (executive function, parent–child interactions, siblings), peer relationships appear to be unique in that continued success with peers as children grow older requires parallel advances in ToM; there is no indication that just reaching a threshold level of knowledge—such as understanding false belief—suffices in predicting long-term social competence during middle childhood and beyond.

4. CONCLUSIONS Children’s ability to think about people in relation to mental states and emotions continues to improve well into middle childhood and adulthood. More specifically, between the ages of 6 and 12, children develop an interpretive understanding of mind—they better recognize that past experience, knowledge status, personal characteristics, and bias can influence how different people perceive the same situation, contributing to divergent


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interpretations and emotions. Indeed, 6- to 7-year-olds sometimes exhibit an overinterpretive mind error by overextending their knowledge about diversity in interpretation to situations where people should think and feel alike. During this time period, children also develop deeper insights into how experiences and mental states cohere across time—people’s thoughts, emotions, and decisions in the here-and-now can be caused by thinking about the past and anticipating the future. Children learn how to integrate multiple pieces of social evidence and decide which events to weight more heavily when forecasting what will happen next. Coinciding with this, children also gain stronger awareness that the mind can be a powerful regulatory tool: How one chooses to think about a situation can powerfully shape emotions and decisions. That children continue to have difficulties in false-belief tasks that include emotions, morality, or additional search locations until at least 6 to 7 years of age further highlights the ways in which ToM still has room to grow beyond the preschool years. Indeed, as we have pointed out several times throughout this review, adults also make errors on more advanced ToM tests; strengthening the argument that learning to predict, explain, and introspect on mental states is a lifelong challenge. The last area of research we reviewed was a consideration of individual differences in ToM during middle childhood. We feel this is the area that is most in need of future research. Whereas prior studies focusing on preschoolers have documented numerous cognitive and social predictors of variability in ToM performance (e.g., executive function, parent–child interactions, maltreatment, siblings, peer relations), open questions remain regarding whether these relations are confined to early development when ToM concepts are first emerging. We argued that executive function continues to be important for implementing ToM in middle childhood and adulthood, that attachment quality and maltreatment history may contribute to biases or nuances in how children and adults attend to, interpret, and weight social information, that sibling–ToM relations still exist in middle childhood, and that children’s success in peer relations during middle childhood appears to require increasing sophistication in ToM skills. Going forward, researchers need to move beyond the question of when children acquire a ToM (the focus of ToM research with infants and preschoolers) to consider more fully how different children may formulate different theories for why people think, act, and feel the way they do; theories of mind informed by personal life experiences that are likely open to growth and revision throughout the life span.

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REFERENCES Altshuler, J. L., & Ruble, D. N. (1989). Developmental changes in children’s awareness of strategies for coping with uncontrollable stress. Child Development, 60, 1337–1349. http://dx.doi.org/10.2307/1130925. Amsterlaw, J. (2006). Children’s beliefs about everyday reasoning. Child Development, 77, 443–464. http://dx.doi.org/10.1111/j.1467-8624.2006.00881.x. Amsterlaw, J., Lagattuta, K. H., & Meltzoff, A. N. (2009). Young children’s reasoning about the effects of emotional and physiological states on academic performance. Child Development, 80, 115–133. http://dx.doi.org/10.1111/j.1467-8624.2008.01249.x. Bamford, C., & Lagattuta, K. H. (2010). A new look and children’s understanding of mind and emotion: The case of prayer. Developmental Psychology, 46, 78–92. http://dx.doi.org/ 10.1037/a0016694. Bamford, C., & Lagattuta, K. H. (2012). Looking on the bright side: Children’s knowledge about the benefits of positive versus negative thinking. Child Development, 83, 667–682. http://dx.doi.org/10.1111/j.1467-8624.2011.01706.x. Banerjee, R., & Watling, D. (2005). Children’s understanding of faux pas: Associations with peer relations. Hellenic Journal of Psychology, 2, 27–45. Banerjee, R., Watling, D., & Caputi, M. (2011). Peer relations and the understanding of faux pas: Longitudinal evidence for bidirectional associations. Child Development, 86, 1887–1905. http://dx.doi.org/10.1111/j.1467-8624.2011.0. Baron-Cohen, S., Jolliffe, T., Mortimore, C., & Robertson, M. (1997). Another advanced test of theory of mind: Evidence from very high functioning adults with autism or Asperger syndrome. Journal of Child Psychology and Psychiatry, 38, 813–822. http://dx. doi.org/10.1111/j.1469-7610.1997.tb01599.x. Baron-Cohen, S., O’Riordan, M., Stone, V., Jones, R., & Plaisted, K. (1999). Recognition of faux pas by normally developing children with asperger syndrome or high-functioning autism. Journal of Autism and Developmental Disorders, 29, 407–418. http://dx.doi.org/ 10.1023/A:1023035012436. Baron-Cohen, S., Wheelwright, S., Spong, A., Scahill, V., & Lawson, J. (2001). Are intuitive physics and intuitive psychology independent? A test with children with Asperger syndrome. Journal of Developmental and Learning Disorders, 5, 47–78. Bender, P. K., Pons, F., Harris, P. L., & de Rosnay, M. (2011). Do young children misunderstand their own emotions. The European Journal of Developmental Psychology, 8, 331–348. http://dx.doi.org/10.1080/17405629.2010.495615. Bernstein, D. M., Erdfelder, E., Meltzoff, A. N., Peria, W., & Loftus, G. R. (2011). Hindsight bias from 3 to 95 years of age. Journal of Experimental Psychology Learning, Memory, and Cognition, 37, 378–391. http://dx.doi.org/10.1037/a0021971. Best, J. R., & Miller, P. H. (2010). A developmental perspective on executive function. Child Development, 81, 1641–1660. http://dx.doi.org/10.1111/j.1467-8624.2010.01499. Birch, S. A. J., & Bloom, P. (2004). Understanding children’s and adults’ limitations in mental state reasoning. Trends in Cognitive Sciences, 8, 255–260. http://dx.doi.org/10.1016/ j.tics.2004.04.011. Bosacki, S. L. (2000). Theory of mind and self-concept in preadolescents: Links with gender and language. Journal of Educational Psychology, 92, 709–717. http://dx.doi.org/ 10.1037/0022-0663.92.4.709. Bosacki, S., & Astington, J. W. (1999). Theory of mind in preadolescent: Relations between social understanding and social competence. Social Development, 8, 238–255. http://dx. doi.org/10.1111/1467-9507.00093. Bowlby, J. (1973). Attachment and loss: Separation. New York, NY: Basic Books. Bradmetz, J., & Schneider, R. (1999). Is Little Red Riding Hood afraid of her grandmother? Cognitive vs. emotional response to false belief. British Journal of Developmental Psychology, 17, 501–514. http://dx.doi.org/10.1348/-26151099165438.


209

Brent, E., Rios, P., Happe´, F., & Charman, T. (2004). Performance of children with autism spectrum disorder on advanced theory of mind tasks. Autism, 8(3), 283–299. http://dx. doi.org/10.1177/1362361304045217. Brown, J. R., Donelan-McCall, N., & Dunn, J. (1996). Why talk about mental states? The significance of children’s conversations with friends, siblings and mothers. Child Development, 67, 836–849. Calero, C. I., Salles, A., Semelman, M., & Sigman, M. (2013). Age and gender dependent development of theory of mind in 6-to 8-years old children. Frontiers in Human Neuroscience, 7, 1–7. http://dx.doi.org/10.3389/fnhum.2013.00281. Caputi, M., Lecce, S., Pagnin, A., & Banerjee, R. (2012). Longitudinal effects of theory of mind on later peer relations: The role of prosocial behaviour. Developmental Psychology, 48, 257–270. http://dx.doi.org/10.1037/a0025402. Carlson, S. M., & Moses, L. J. (2001). Individual differences in inhibitory control and children’s theory of mind. Child Development, 72, 1032–1053. http://dx.doi.org/ 10.111/1467-8624.00333. Carlson, S. M., Moses, L. J., & Breton, C. (2002). How specific is the relation between executive function and theory of mind? Contributions of inhibitory control and working memory. Infant and Child Development, 11, 73–92. http://dx.doi.org/10.1002/ icd.298. Carpendale, J. I., & Chandler, M. J. (1996). On the distinction between false belief understanding and subscribing to an interpretive theory of mind. Child Development, 67, 1686–1706. http://dx.doi.org/10.1111/j.1467-8624.1996.tb01821.x. Carpendale, J. I. M., & Lewis, C. (2004). Constructing and understanding of mind: The development of children’s social understanding within social interaction. Behavioral and Brain Sciences, 27, 79–151. http://dx.doi.org/10.1017/S0140525X04000032. Chandler, M. J., & Helm, D. (1984). Developmental changes in the contribution of shared experience to social role-taking competence. International Journal of Behavioral Development, 7, 145–156. http://dx.doi.org/10.1016/S0163-6383(84)80207-6. Charman, T., & Shmueli-Goetz, Y. (1998). The relationship between theory of mind, language, and narrative discourses: An experimental study. Current Psychology of Cognition, 17, 245–271. Cicchetti, D., & Ng, R. (2014). Emotional development in maltreated children. In K. H. Lagattuta (Ed.), Children and emotion: New insights into developmental affective sciences (pp. 106–118). Switzerland: Karger. http://dx.doi.org/10.1159/000354349. Cicchetti, D., Rogosch, F. A., Maughan, A., Toth, S. L., & Bruce, J. (2003). False belief understanding in maltreated children. Development and Psychopathology, 15, 1067–1091. http://dx.doi.org/10.1017/S0954579403000440. Cole, K., & Mitchell, P. (2000). Siblings in the development of executive control and a theory of mind. British Journal of Developmental Psychology, 18, 279–295. http://dx.doi.org/ 10.1348/026151000165698. Davis, E. L., Levine, L. J., Lench, H. C., & Quas, J. A. (2010). Metacognitive emotion regulation: Children’s awareness that changing thoughts and goals can alleviate negative emotions. Emotion, 10, 498–510. http://dx.doi.org/10.1037/a0018428. de Rosnay, M., & Harris, P. L. (2002). Individual differences in children’s understanding of emotion: The roles of attachment and language. Attachment & Human Development, 4, 39–54. http://dx.doi.org/10.1080/14616730210123139. Devine, R. T., & Hughes, C. (2013). Silent films and strange stories: Theory of mind, gender, and social experiences in middle childhood. Child Development, 84, 989–1003. http://dx. doi.org/10.1111/cdev.12017. Devine, R. T., & Hughes, C. (2014). Relations between false belief understanding and executive function in early childhood: A meta-analysis. Child Development, 85, 1777–1794. http://dx.doi.org/10.1111/cdev.12237.

210


Dorris, L., Epsie, C. A. E., Knott, F., & Salt, J. (2004). Mind-reading difficulties in the siblings of people with Aperger’s syndrome: Evidence for a genetic influence in the abnormal development of specific cognitive domain. Journal of Child Psychology and Psychiatry, 45, 412–418. http://dx.doi.org/10.1111/j.1469-7610.2004.00232.x. Dunning, D., & Hayes, A. F. (1996). Evidence for egocentric comparison in social judgment. Journal of Personality and Social Psychology, 71, 213–229. http://dx.doi.org/10.1037/00223514.71.2.213. Eisbach, A. O. (2004). Children’s developing awareness of diversity in people’s trains of thought. Child Development, 75, 1694–1707. http://dx.doi.org/10.1111/j.14678624.2004.00810.x. Ellis, A. (1991). The revised ABC’s of rational-emotive therapy (RET). Journal of RationalEmotive & Cognitive-Behavior Therapy, 9, 139–172. http://dx.doi.org/10.1007/BF01061227. Fabricius, W. V., Boyer, T. W., Weimer, A. A., & Carroll, K. (2010). True or false? Do 5-year-olds understand belief? Developmental Psychology, 46, 1402–1416. http://dx.doi. org/10.1037/10017648. Fabricius, W. V., & Khalil, S. L. (2003). False beliefs or false positives? Limits on chidren’s understanding of mental representation. Journal of Cognition and Development, 4, 239–262. http://dx.doi.org/10.1207/S15327647JCD0403_01. Farhadian, M., Gazanizad, N., & Shakerian, A. (2011). Theory of mind and siblings among preschool children. Asian Social Science, 7, 224–231. http://dx.doi.org/10.1111/j.14678624.2005.00832.x. Flavell, J. H., & Green, F. L. (1999). Development of intuitions about the controllability of different mental states. Cognitive Development, 14, 133–146. http://dx.doi.org/10.1016/ S0885-2014(99)80021-5. Flavell, J. H., Green, F. L., & Flavell, E. R. (1993). Children’s understanding of the stream of consciousness. Child Development, 64, 387–398. http://dx.doi.org/10.2307/1131257. Flavell, J. H., Green, F. L., & Flavell, E. R. (1995). Young children’s knowledge about thinking. Monographs of the Society for Research in Child Development, 60, v–96. http://dx.doi. org/10.2307/1166124. Flavell, J. H., Green, F. L., & Flavell, E. R. (1998). The mind has a mind of its own: Developing knowledge about mental uncontrollability. Cognitive Development, 13, 127–138. http://dx.doi.org/10.1016/S0885-2014(98)90024-7. Fonagy, P., Redfern, S., & Charman, T. (1997). The relationship between belief-desire reasoning and a projective measure of attachment security (SAT). British Journal of Developmental Psychology, 15, 51–61. http://dx.doi.org/10.1111/j.2044-835X.1997.tb00724.x. Frye, D., & Moore, C. (2014). Children’s theories of mind: Mental states and social understanding. New York, NY: Psychology Press. Gasser, L., & Keller, M. (2009). Are the competent the morally good? Perspective taking and moral motivation of children involved in bullying. Social Development, 18, 798–816. http://dx.doi.org/10.1111/j.1467-9507.2008.00516.x. Ghetti, S., Castelli, P., & Lyons, K. E. (2010). Knowing about not remembering: Developmental dissociations in lack-of-memory monitoring. Developmental Science, 13, 611–621. http://dx.doi.org/10.1111/j.1467-7687.2009.00908.x. Ghetti, S., Hembacher, E., & Coughlin, C. A. (2013). Feeling uncertain and acting on it during the preschool years: A metacognitive approach. Child Development Perspectives, 7, 160–165. http://dx.doi.org/10.1111/cdep.12035. Gnepp, J., & Klayman, J. (1992). Recognition of uncertainty in emotional inferences: Reasoning about emotionally equivocal situations. Developmental Psychology, 28, 145–158. http://dx.doi.org/10.1037/0012-1649.28.1.145. Gnepp, J., McKee, E., & Domanic, J. A. (1987). Children’s use of situational information to infer emotion: Understanding emotionally equivocal situations. Developmental Psychology, 23, 114–123. http://dx.doi.org/10.1037/0012-1649.23.1.114.


211

Gopnik, A., & Astington, J. W. (1988). Children’s understanding of representational change and its relation to the understanding of false belief and the appearance-reality distinction. Child Development, 59, 26–37. http://dx.doi.org/10.2307/11303086. Grazzani, I., & Ornaghi, V. (2012). How do use and comprehension of mental-state language relate to theory of mind in middle childhood? Cognitive Development, 27, 99–111. http:// dx.doi.org/10.1016/j.cogdev.2012.03.002. Gweon, H., Dodell-Feder, D., Bedny, M., & Saxe, R. (2012). Theory of mind performance in children correlates with functional specialization of a brain region for thinking about thoughts. Child Development, 83, 1853–1868. http://dx.doi.org/10.1111/j.14678624.2012.01829.x. Haidt, J. (2013). Moral psychology for the twenty-first century. Journal of Moral Education, 42, 281–297. http://dx.doi.org/10.1080/03057240.2013.817327. Happe´, F. G. (1994). An advanced test of theory of mind: Understanding of story character’ thoughts, and feelings by able autistic, mentally handicapped, and normal children and adults. Journal of Autism and Developmental Disorders, 24, 129–154. http://dx.doi.org/ 10.1007/BF02172093. Harris, P. L. (2005). Conversation, pretense, and theory of mind. In J. W. Astington & J. A. Baird (Eds.), Why language matters for theory of mind (pp. 70–83). New York, NY: Oxford University Press. Harris, P. L., de Rosnay, M., & Ronfard, S. (2014). The mysterious emotional life of little red riding hood. In K. H. Lagattuta (Ed.), Children and emotion: New insights into developmental affective sciences (pp. 106–118). Switzerland: Karger. http://dx.doi.org/10.1159/000354364. Harris, P. L., Guz, G. R., Lipian, M. S., & Man-Shu, Z. (1985). Insights into the time course emotion among Western and Chinese children. Child Development, 56, 972–988. http:// dx.doi.org/10.2307/1130109. Harris, P. L., Johnson, C. N., Hutton, D., Andrews, G., & Cooke, T. (1989). Young children’s theory-of-mind and emotion. Cognition and Emotion, 3, 379–400. http://dx.doi. org/10.1080/17405629.2010495615. Harter, S. (1988). Developmental processes in the construction of the self. In T. D. Yawkey & J. E. Johnson (Eds.), Integrative processes and socialization: Early to middle childhood (pp. 45–78). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Heyman, G. D., Fu, G., & Lee, K. (2007). Evaluating claims people make about themselves: The development skepticism. Child Development, 78, 367–375. http://dx.doi.org/ 10.1111/j.1467-8624.2007.01003.x. Heyman, G. D., & Legare, C. H. (2005). Children’s evaluation of sources of information about traits. Developmental Psychology, 41, 636–647. http://dx.doi.org/10.1037/00121649.41.4.636. Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67, 88–140. http://dx.doi.org/10.2307/1170620. Hughes, C., & Leekam, S. (2004). What are the links between theory of mind and social relations? Review, reflections and new directions for studies of typical and atypical development. Social Development, 13, 590–619. http://dx.doi.org/10.1111/j.1467-9507.2004.00285.x. Hughes, D., Rodriguez, J., Smith, E. P., Johnson, D. J., Stevenson, H. C., & Spicer, P. (2006). Parents’ ethnic-racial socialization practices: A review of research and directions for future study. Developmental Psychology, 42, 747–770. http://dx.doi.org/ 10.1037/001201649.42.5.747. Hughes, C., White, N., & Ensor, R. (2014). How does talk about thoughts, desires, and feelings foster children’s socio-cognitive development? Mediators, moderators and implications for intervention. In K. H. Lagattuta (Ed.), Children and emotion: New insights into developmental affective sciences (pp. 106–118). Switzerland: Karger. http://dx.doi.org/ 10.1159/000354360.

212


James, W. (1890). The principles of psychology. New York, NY: Henry Holt and Companyhttp://dx.doi.org/10.1037/11059-000. Jenkins, J. M., & Astington, J. W. (1996). Cognitive factors and family structure associated with theory of mind development in young children. Developmental Psychology, 32, 70–78. http://dx.doi.org/10.1037/0012-1649.32.1.70. Jolliffe, T., & Baron-Cohen, S. (1999). The strange stories test: A replication with highfunctioning adults with autism or Asperger syndrome. Journal of Autism and Developmental Disorders, 29, 395–406. http://dx.doi.org/10.1023/A:1023082928366. Kagan, J. (2013). Contextualizing experience. Developmental Review, 33, 273–278. http://dx. doi.org/10.1016/j.dr.2013.07.003. Kahan, D. M., Jenkins-Smith, H., & Braman, D. (2011). Cultural cognition of scientific consensus. Journal of Risk Research, 14, 147–174. http://dx.doi.org/10.1080/ 13669877.2010.511246. Kalish, C. W., & Cornelius, R. (2007). What is to be done? Children’s ascriptions of conventional obligations. Child Development, 78, 859–878. http://dx.doi.org/10.1111/ j.1467-8624.2007.01037.x. Kennedy, K., Lagattuta, K. H., Kramer, H. J., & Sayfan, L. (2014). Children’s and adults’ reasoning about when past experience biases interpretations. In Presented at the Jean Piaget Society Meeting, San Francisco, CA. Kennedy, K., Lagattuta, K. H., & Sayfan, L. (in press). Sibling composition, executive function, and children’s thinking about mental diversity in middle childhood. Journal of Experimental Child Psychology. http://dx.doi.org/10.1016/j.jecp.2014.11.007. Kerns, K. A., & Brumariu, L. A. (2014). Is insecure parent–child attachment a risk factor for the development of anxiety in childhood or adolescence? Child Development Perspectives, 8, 12–17. http://dx.doi.org/10.1111/cdep.12054. Kerns, K. A., & Richardson, R. A. (2005). Attachment in middle childhood. New York, NY: Guilford Press. Killen, M., Mulvey, K. L., Richardson, C., Jampol, N., & Woodward, A. (2011). The accidental transgressor: Morally-relevant theory of mind. Cognition, 119, 197–215. http://dx. doi.org/10.1016/j.cognition.2011.01.006. Kobayashi, C., Glover, G. H., & Temple, E. (2007). Children’s and adults’ neural bases of verbal and nonverbal ‘theory of mind’. Neuropsychologia, 45, 1522–1532. http://dx.doi. org/10.1016/j.neuropsychologia.2006.11.017. Koizumi, M., & Takagishi, H. (2014). The relationship between child maltreatment and emotion recognition. PloS One, 9, 1–4. http://dx.doi.org/10.1371/journal. pone.0086093. Kuczaj, S. A. (1977). The acquisition of regular and irregular past tense forms. Journal of Verbal Learning and Verbal Behavior, 16, 589–600. http://dx.doi.org/10.1016/S0022-5371(77) 80021-2. Lagattuta, K. H. (2005). When you shouldn’t do what you want to do: Young children’s understanding of desires, rules and emotions. Child Development, 76, 713–733. http:// dx.doi.org/10.1111/j.1467-8624.2005.00873.x. Lagattuta, K. H. (2007). Thinking about the future because of the past: Young children’s knowledge about the causes of worry and preventative decisions. Child Development, 78, 1492–1509. http://dx.doi.org/10.1111/j.1467-8624.2007.01079.x. Lagattuta, K. H. (2008). Young children’s knowledge about the influence of thoughts on emotions in rule situations. Developmental Science, 11, 809–818. http://dx.doi.org/ 10.1111/j.1467-7687.2008.00727.x. Lagattuta, K. H. (2014). Linking past, present and future: Children’s ability to connect mental states and emotions across time. Child Development Perspectives, 8, 90–95. http://dx.doi. org/10.1111/cdep.12065.


213

Lagattuta, K. H., Hjortsvang, K., & Kennedy, K. (2014). Theory of mind and academic competence during early childhood: Emotion understanding, relationships, and learning from others. In O. Saracho & B. Spodek (Eds.), Contemporary perspectives on research in theories of mind in early childhood (pp. 245–268). Charlotte, NC: Information Age Publishers. Lagattuta, K. H., Nucci, L., & Bosacki, S. L. (2010). Bridging theory of mind and the personal domain: Children’s reasoning about resistance to parental control. Child Development, 81, 616–635. http://dx.doi.org/10.1111/j.1467-8624.2009.01419.x. Lagattuta, K. H., & Sayfan, L. (2013). Not all past events are equal: Biased attention and emerging heuristics in children’s past-to-future forecasting. Child Development, 84, 2094–2111. http://dx.doi.org/10.1111/cdev.12082. Lagattuta, K. H., Sayfan, L., & Blattman, A. J. (2010). Forgetting common ground: Six- to seven-year-olds have an overinterpretive theory of mind. Developmental Psychology, 46, 1417–1432. http://dx.doi.org/10.1037/aa0021062. Lagattuta, K. H., Sayfan, L., & Harvey, C. (2014). Beliefs about thought probability: Evidence for persistent errors in mindreading and links to executive control. Child Development, 85, 659–674. http://dx.doi.org/10.1111/cdev.12154. Lagattuta, K. H., & Weller, D. (2014). Interrelations between theory of mind and morality: A developmental perspective. In M. Killen & J. G. Smetana (Eds.), Handbook of moral development (2nd ed., pp. 385–407). New York, NY: Psychology Press. Lagattuta, K. H., & Wellman, H. M. (2001). Thinking about the past: Early knowledge about links between prior experience, thinking and emotion. Child Development, 72, 82–102. http://dx.doi.org/10.1111/1467-8624.00267. Lagattuta, K. H., Wellman, H. M., & Flavell, J. H. (1997). Preschoolers’ understanding of the connection between thinking and feeling: Cognitive cuing and emotional change. Child Development, 68, 1081–1104. http://dx.doi.org/10.2307/1132293. Laible, D. J., & Thompson, R. A. (1998). Attachment and emotional understanding in pre-school children. Developmental Psychology, 34, 1038–1045. http://dx.doi.org/ 10.1037/0012-1649.34.5.1038. Lalonde, C. E., & Chandler, M. J. (2002). Children’s understanding of interpretation. New Ideas in Psychology, 20, 163–198. http://dx.doi.org/10.1016/S0732-188X(02) 00007-7. Larsen, J. T., To, Y. M., & Fireman, G. (2007). Children’s understanding and experience of mixed emotions. Psychological Science, 18, 186–191. http://dx.doi.org/10.1111/j.14679280.2007.01879.x. Larson, R. W., & Verma, S. (1999). How children and adolescents spend time across the world: Work, play and developmental opportunities. Psychological Bulletin, 125, 701–736. http://dx.doi.org/10.1037/0033-2909.125.6.701. Legerstee, M. (2005). Infants’ sense of people: Precursors to theory of mind. New York, NY: Cambridge University Press. Liddle, B., & Nettle, D. (2006). Higher-order theory of mind and social competence in school-age children. Journal of Cultural and Evolutionary Psychology, 4, 231–246. http:// dx.doi.org/10.1556/JCEP.4.2006.3. Lightfoot, C., Cole, M., & Cole, S. R. (2013). The development of children (7th ed.). New York, NY: Worth Publishers. Longobardi, E., Spataro, P., & Renna, M. (2014). Relationship between false belief, mental state language, metalinguistic awareness and social abilities in school-age children. Social and Behavioral Sciences, 114, 365–371. http://dx.doi.org/10.1016/j. sbspro.2013.12.713. Luke, N., & Banerjee, R. (2013a). Situating maltreatment in the social context: Challenges for research. Developmental Review, 33(3), 279–284. http://dx.doi.org/10.1016/ j.dr.2013.07.001.

214


Luke, N., & Banerjee, R. (2013b). Differentiated associations between childhood maltreatment experiences and social understanding: A meta-analysis and systematic review. Developmental Review, 33, 1–28. http://dx.doi.org/10.1016/j.dr.2012.10.001. McAlister, A., & Peterson, C. (2006). Mental playmates: Siblings, executive functioning and theory of mind. British Journal of Developmental Psychology, 24, 733–751. http://dx.doi. org/10.1348/026151005X70094. McAlister, A., & Peterson, C. (2007). A longitudinal study of child siblings and theory of mind development. Cognitive Development, 22, 258–270. http://dx.doi.org/10.1016/ j.cogdev.2006.10.009. McAlister, A. R., & Peterson, C. C. (2013). Siblings, theory of mind, and executive functioning in children aged 3–6 years: New longitudinal evidence. Child Development, 84, 1442–1458. http://dx.doi.org/10.1111/cdev.12043. Mcquaid, N., Bigelow, A. E., McLaughlin, J., & MacLean, K. (2008). Maternal mental state language and preschool children’s attachment security: Relation to children’s mental state language and expressions of emotional understanding. Social Development, 17, 61–83. http://dx.doi.org/10.1111/j.1467-9507.2007.00415.x. Meins, E., Fernyhough, C., Johnson, F., & Lidstone, J. (2006). Mind-mindedness in children: Individual differences in internal-state talk in middle childhood. British Journal of Developmental Psychology, 24, 181–196. http://dx.doi.org/10.1348/ 026151005X80174. Meins, E., Fernyhough, C., Russell, J., & Clark-Carter, D. (1998). Security of attachment as a predictor of symbolic and mentalising abilities: A longitudinal study. Social Development, 7, 1–24. http://dx.doi.org/10.1111/1467-9507.00047. Meins, E., Fernyhough, C., Wainwright, R., Das Gupta, M., Fradley, E., & Tuckey, M. (2002). Maternal mind–mindedness and attachment security as predictors of theory of mind understanding. Child Development, 73, 1715–1726. http://dx.doi.org/ 10.1111/1467-8624.00501. Miller, S. A. (2009). Children’s understanding of second-order mental states. Psychological Bulletin, 135, 749–773. http://dx.doi.org/10.1037/a001. Miller, S. A. (2012). Theory of mind: Beyond the preschool years. New York, NY: Psychology Press. Miller, S. A. (2013). Children’s understanding of second-order false belief: Comparisons of content and method of assessment. Infant and Child Development, 22, 649–658. http://dx. doi.org/10.1002/icd.1810. Miller, S. A., Hardin, C. A., & Montgomery, D. E. (2003). Young children’s understanding of the conditions for knowledge acquisition. Journal of Cognition and Development, 4, 325–356. http://dx.doi.org/10.1207/S15327647JCD0403_05. Mills, C. M., Al-Jabari, R. M., & Arachacki, M. A. (2012). Why do people disagree? Explaining and endorsing the possibility of partiality in judgments. Journal of Cognition and Development, 13, 111–136. http://dx.doi.org/10.1080/15248372.2010.547236. Mills, C. M., & Grant, M. G. (2009). Biased decision-making: Developing an understanding of how positive and negative relationships may skew judgments. Developmental Science, 12, 784–797. http://dx.doi.org/10.1111/j.1467-7687.2009.00836.x. Mills, C. M., & Keil, F. C. (2008). Children’s developing notions of (im)partiality. Cognition, 107, 528–551. http://dx.doi.org/10.1016/j.cognition.2007.11.003. Mull, S. M., & Evans, E. M. (2010). Did she mean to do it? Acquiring a folk theory of intentionality. Journal of Experimental Child Psychology, 107, 207–228. http://dx.doi.org/ 10.1016/j.jecp.2010.04.001. O’Brien, K., Slaughter, V., & Peterson, C. C. (2011). Siblings influences on theory of mind development for children with ASD. Journal of Child Psychology and Psychiatry, 52, 713–719. http://dx.doi.org/10.1111/j.1469-7610.2011.02389.x.


215

Ontai, L. L., & Thompson, R. A. (2008). Attachment, parent–child discourse and theory-ofmind development. Social Development, 17, 47–60. http://dx.doi.org/10.1111/j.14679507.2007.0414.x. Pears, K. C., & Fisher, P. A. (2005). Emotion understanding and theory of mind among maltreated children in foster care: Evidence of deficits. Development and Psychopathology, 17, 47–65. http://dx.doi.org/10.1017/S0954579405050030. Perner, J., Ruffman, T., & Leekam, S. R. (1994). Theory of mind is contagious: You catch it from your sibs. Child Development, 65, 1228–1238. http://dx.doi.org/10.2307/1131316. Perner, J., & Wimmer, H. (1985). ’John thinks that Mary thinks that . . .’: Attribution of second-order beliefs by 5- to 10-year-old children. Journal of Experimental Child Psychology, 39, 437–471. http://dx.doi.org/10.1016/0022-0965(85)90051-7. Peterson, C. C., Wellman, H. M., & Slaughter, V. (2012). The mind behind the message: Advancing theory-of-mind scales for typically developing children, and those with deafness, autism, or Asperger syndrome. Child Development, 83, 469–485. http://dx.doi.org/ 10.1111/j.1467-8624.2011.01728.x. Pillow, B. H. (2012). Children’s discovery of the active mind: Phenomenological awareness, social experience, and knowledge about cognition. New York, NY: Springer Science + Business Media. Pillow, B. H., & Mash, C. (1999). Young children’s understanding of interpretations, expectation and direct perception as sources of false belief. British Journal of Developmental Psychology, 17, 263–267. http://dx.doi.org/10.1348/026151099165267. Pohl, R. F., Bayen, U. J., & Martin, C. (2010). A multiprocess account of hindsight bias in children. Developmental Psychology, 46, 1268–1282. http://dx.doi.org/10.1037/a0020209. Pons, F., Harris, P. L., & de Rosnay, M. (2004). Emotion comprehension between 3 and 11 years: Developmental periods and hierarchical organization. The European Journal of Developmental Psychology, 1, 127–152. http://dx.doi.org/10.1080/17405620344000022. Prencipe, A., Kesek, A., Cohen, J., Lamm, C., Lewis, M. D., & Zelazo, P. D. (2011). Development of hot and cool and executive function during the transition to adolescence. Journal of Experimental Child Psychology, 108, 621–637. http://dx.doi.org/10.1016/ j.jecp.2010.09.008. Raikes, H. A., & Thompson, R. A. (2006). Family emotional climate, attachment security and young children’s emotional knowledge in a high risk sample. British Journal of Developmental Psychology, 24, 89–104. http://dx.doi.org/10.1348/026151005X70427. Repacholi, B., & Trapolini, T. (2004). Attachment and preschool children’s understanding of maternal versus non-maternal psychological states. British Journal of Developmental Psychology, 22, 395–416. http://dx.doi.org/10.1348/0261510041552693. Roebers, C. M. (2002). Confidence judgments in children’s and adults’ event recall and suggestibility. Developmental Psychology, 38, 1052–1067. http://dx.doi.org/10.1037//00121649.38.6.1052. Rohwer, M., Kloo, D., & Perner, J. (2012). Escape from metaignorance: How children develop an understanding of their own lack of knowledge. Child Development, 83, 1869–1883. http://dx.doi.org/10.1111/j.1467-8624.2012.01830.x. Ronford, S., & Harris, P. L. (2014). When will little red riding hood become scared? Children’s attribution of mental states to a story character. Developmental Psychology, 50, 283–292. http://dx.doi.org/10.1037/a0032970. Ross, H. S., Recchia, H. E., & Carpendale, J. I. M. (2005). Making sense of divergent interpretations of conflict and developing an interpretive understanding of mind. Journal of Cognition and Development, 6, 571–592. http://dx.doi.org/10.1207/s15327647jcd0604_7. Royzman, E. B., Cassidy, K. W., & Baron, J. (2003). “I know, you know”: Epistemic egocentrism in children and adults. Review of General Psychology, 7, 38–65. http://dx.doi.org/ 10.1037/1089-2680.71.1.38.

216


Ruffman, T., Perner, J., Naito, M., Parkin, L., & Clements, W. (1998). Older (but not younger) siblings facilitate false belief understanding. Developmental Psychology, 34, 161–174. http://dx.doi.org/10.1037/0012-1649.34.1.161. Sabbagh, M. A., Xu, F., Carlson, S. M., Moses, L. J., & Lee, K. (2006). The development of executive functioning in theory of mind: A comparison of Chinese and U.S. preschoolers. Psychological Science, 17, 74–81. http://dx.doi.org/10.1111/j.1467-9280.2005.01667.x. Saxe, R. R., Whitfield-Gabrieli, S., Scholz, J., & Pelphrey, K. A. (2009). Brain regions for perceiving and reasoning about other people in school-aged children. Child Development, 80, 1197–1209. http://dx.doi.org/10.1111/j.1467-8624.2009.01325.x. Sayfan, L., & Lagattuta, K. H. (2008). Grownups are not afraid of scary stuff, but kids are: Young children’s and adults’ reasoning about children’s, infants’, and adults’ fears. Child Development, 79, 821–835. http://dx.doi.org/10.1111/j.1467-8624.2008.01161.x. Sayfan, L., & Lagattuta, K. H. (2009). Scaring the monster away: What children know about managing fears of real and imaginary creatures. Child Development, 80, 1756–1774. http://dx.doi.org/10.1111/j.1467-8624.2009.01366.x. Senju, A., Southgate, V., White, S., & Frith, U. (2009). Mindblind eyes: An absence of spontaneous theory of mind in Asperger syndrome. Science, 325, 883–885. http://dx.doi.org/ 10.1126/science.1176170. Slaughter, V., Dennis, M., & Pritchard, M. (2002). Theory of mind and peer acceptance in preschoolers. British Journal of Developmental Psychology, 20, 545–564. http://dx.doi.org/ 10.1348/026151002760390945. Sodian, B. (2011). Theory of mind in infancy. Child Development, 5, 39–43. http://dx.doi. org/10.1111/j.1750-8606.2010.0. Steele, H., Steele, M., Croft, C., & Fonagy, P. (1999). Infant-mother attachment at one year predicts children’s understanding of mixed emotions at six years. Social Development, 8, 161–178. http://dx.doi.org/10.1111/1467-9507.00089. Sutton, J., Smith, P. K., & Swettenham, J. (1999). Social cognition and bullying: Social inadequacy or skilled manipulation? British Journal of Developmental Psychology, 17, 435–450. http://dx.doi.org/10.1348/026151099165384. Velloso, R. D. L., Duarte, C. P., & Schwartzman, J. S. (2013). Evaluation of the theory of mind in autism spectrum disorders with the strange stories test. Arquivos de NeuroPsiquiatria, 71, 871–876. http://dx.doi.org/10.1590/0004-282X20130171. Wainryb, C. (1991). Understanding differences in moral judgments. The role of informational assumptions. Child Development, 62, 840–851. http://dx.doi.org/10.2307/1131181. Wainryb, C., & Ford, S. (1998). Young children’s evaluations of acts based on beliefs different from their own. Merrill-Palmer Quarterly, 44, 484–503. Wainryb, C., Shaw, L. A., Langley, M., Cottam, K., & Lewis, R. (2004). Children’s thinking about diversity of belief in the early school years: Judgments of relativism, tolerance and disagreeing persons. Child Development, 75, 687–703. http://dx.doi.org/10.1111/ j.1467-8624.2004.00701.x. Weller, D., & Lagattuta, K. H. (2013). Helping the in-group feels better: Children’s judgments and emotion attributions in response to prosocial dilemmas. Child Development, 84, 253–268. http://dx.doi.org/10.1111/j.1467-8624.2012.01837.x. Weller, D., & Lagattuta, K. H. (2014). Children’s judgments about prosocial decisions and emotions: Gender of the helper and recipient matters. Child Development, 85, 2011–2028. http://dx.doi.org/10.1111/cdev.12238. Wellman, H. M. (1990). The child’s theory of mind. Cambridge, MA: MIT Press, A Bradford Book. Wellman, H. M. (2011). Developing a theory of mind. In U. Goswami (Ed.), The Wiley-Blackwell handbook of childhood cognitive development (2nd ed., pp. 258–284). Oxford, UK: Blackwell.


217

Wellman, H. M. (2014). Making minds: How theory of mind develops. New York, NY: Oxford University Press. Wellman, H. M., & Banerjee, M. (1991). Mind and emotion: Children’s understanding of the emotional consequences of beliefs and desires. British Journal of Developmental Psychology, 9, 191–214. http://dx.doi.org/10.1111/j.2044-835X.1991.tb00871. Wellman, H. M., Cross, D., & Watson, J. (2001). Meta-analysis of theory-of-mind development: The truth about false belief. Child Development, 72, 655–684. http://dx.doi. org/10.1111/1467-8624.00304. Wellman, H. M., & Lagattuta, K. H. (2000). Developing understandings of mind. In S. Baron-Cohen, H. Tager-Flusberg, & D. J. Cohen (Eds.), Understanding other minds: Perspectives from developmental cognitive neuroscience (2nd ed., pp. 21–49). New York, NY: Oxford University Press. White, S., Hill, E., Happe´, F., & Frith, U. (2009). Revisiting the strange stories: Revealing mentalizing impairments in autism. Child Development, 80, 1097–1117. http://dx.doi. org/10.1111/j.1467-8. Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition, 13, 103–128. http://dx.doi.org/10.1016/0010-0277(83)90004-5. Wood, J. V. (1989). Theory and research concerning social comparisons of personal attributes. Psychological Bulletin, 106, 231–248. http://dx.doi.org/10.1037/00332909.106.2.231. Wright, J. C. (2012). Children’s and adolescents’ tolerance for divergent beliefs: Exploring the cognitive and affective dimensions of moral conviction in our youth. British Journal of Developmental Psychology, 30, 493–510. http://dx.doi.org/10.1111/j.2044835X.2011.02058.x. Zalla, T., Sav, A., Stopin, A., Ahade, S., & Leboyer, M. (2009). Faux pas detection and intentional action in Asperger syndrome. A replication on a French sample. Journal of Autism and Developmental Disorders, 39, 373–382. http://dx.doi.org/10.1007/s10803-008-0634-y. Zelazo, P. D., & Carlson, S. M. (2012). Hot and cool executive function in childhood and adolescence: Development and plasticity. Child Development Perspectives, 6, 345–360. http://dx.doi.org/10.1111/j.1750-8606.2012.00246.x.


CHAPTER SEVEN

Television and Children's Executive Function Angeline S. Lillard*,1, Hui Li*,†, Katie Boguszewski*

*Department of Psychology, University of Virginia, Charlottesville, Virginia, USA † School of Psychology, Central China Normal University, Wuhan, Hubei, PR China 1 Corresponding author: e-mail address: [email protected]

Contents 1. 2. 3. 4. 5. 6. 7. 8.

Introduction Executive Function Children and Television Media Long-Term Media Influences on Executive Function Short-Term Studies of Television and Executive Function Processing of Television Our Studies Modeling How Fantastical Television Might Influence Executive Function 8.1 Attention 8.2 Encoding/Processing 8.3 Arousal 9. Conclusion Acknowledgments References

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Abstract Children spend a lot of time watching television on its many platforms: directly, online, and via videos and DVDs. Many researchers are concerned that some types of television content appear to negatively influence children's executive function. Because (1) executive function predicts key developmental outcomes, (2) executive function appears to be influenced by some television content, and (3) American children watch large quantities of television (including the content of concern), the issues discussed here comprise a crucial public health issue. Further research is needed to reveal exactly what television content is implicated, what underlies television's effect on executive function, how long the effect lasts, and who is affected.


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1. INTRODUCTION Young children watch a good deal of television. There are some indications that television might influence the development of a very important construct called “executive function.” Executive function is an umbrella term for processes that underlie our ability to plan and execute actions directed toward a goal (Carlson, Zelazo, & Faja, 2012). For example, one executive function process is working memory, or our ability to keep information in mind and operate on that information. Another is inhibitory control, or the ability to stop ourselves from engaging in an action, or even thinking about something that we do not (on an “executive” level) want to engage in or think of. Another executive function process is changing mind sets or operating by new rules when the situation we are in changes. If watching television early in life impairs these abilities, it is cause for public health action. This chapter reviews the concept of executive function and children’s media use before discussing studies of both the long-term and short-term influences of television on executive function. It ends with a model of how television might exert such effects and calls for further research into understanding this relationship.

2. EXECUTIVE FUNCTION Executive functions are the suite of processes that underlie goaldirected self-regulatory behaviors, including attention, planning, and inhibitory control (Miyake & Friedman, 2012; Miyake, Friedman, Emerson, Witzki, & Howerter, 2000). These abilities have been shown to be highly correlated and to constitute a unified construct, but they are also separable and distinct. The developmental trajectory of these abilities relies heavily on the development of the prefrontal cortex (PFC), which exhibits an extended maturation progression when compared to other areas of the brain (Mueller, Baker, & Yeung, 2013). Very early aspects of executive function are observable within the first year of life; however, a great amount of development occurs during the preschool years (Best & Miller, 2010; Diamond, 2013; Garon, Bryson, & Smith, 2008). Between 3 and 5 years, observable competencies are gained in all distinct executive function abilities. For example, longer delays can be handled on working memory tasks, and larger degrees of conflict can be managed on inhibition tasks. Continued maturation of these skills is seen throughout later childhood and adolescence.

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Executive function is beneficial both immediately and prospectively, as it is predictive of positive long-term outcomes across several life domains. Executive function skills have been shown to undergird positive social (Eisenberg et al., 2004) and cognitive function (Blair & Razza, 2007), and executive function is strongly associated with success in school and life (Blair & Razza, 2007; Bull, Espy, & Wiebe, 2008; Diamond & Lee, 2011; Duncan et al., 2007; Espy et al., 2004; Mischel, Shoda, & Rodriguez, 1989; Ponitz, McClelland, Matthews, & Morrison, 2009). A large-scale study showed that childhood self-control predicts myriad health, wealth, and criminal behaviors at age 32 (Moffitt et al., 2011). Due to both the short- and long-term positive outcomes associated with executive function abilities, any common activity affecting this construct is of interest. One activity that appears to influence executive function is modern media, including television.

3. CHILDREN AND TELEVISION MEDIA Television is, of course, a common pastime for young children. A recent survey found that at both 2–4 and 5–8 years of age, children spend an average of about 2 h/day watching television, DVDs, and videos (Rideout, 2011). Some television programs have positive effects (Linebarger & Vaala, 2010). For example, children have been shown to learn some Spanish words from watching Dora the Explorer, and low-income children who watched Sesame Street were more school-ready than were those who did not watch the show (for review, see Anderson & Kerkorian, in press). Despite some positive findings, developmental psychologists have long been concerned about children watching television. One theoretical reason for this concern is the passivity of the medium. Passivity is a concern because, as Piaget (Flavell, 1963), Montessori (Lillard, 2005), and others have pointed out, children develop through acting on the environment. A child who passively absorbs stimuli is thought not to absorb them as well. Beyond this theoretical concern, many studies show associations between television and negative child outcomes. For example, children who watch more television show increased obesity, aggression, stereotyped cognitions and other misconceptions, and worse academic performance than children who watch less television (Andersen, Crespo, Bartlett, Cheskin, & Pratt, 1998; Anderson et al., 2003; Anderson, Huston, Schmitt, Linebarger, & Wright, 2001; Schmidt & Vandewater, 2008; Sharif, Wills, & Sargent, 2010). As noted earlier, this chapter addresses another negative outcome with which more television has been associated: poor executive function,

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including problems with attention. We first discuss evidence of these associations, before turning to short-term experimental studies that suggest the relationship might be causal.

4. LONG-TERM MEDIA INFLUENCES ON EXECUTIVE FUNCTION There has been much discussion of associations between television and long-term or trait-level attention problems. Most, although not all, of the published studies report that television viewing is associated with lower attention skills concurrently and/or over the long term (Anderson & Pempek, 2005; Ferguson, 2011; Foster & Watkins, 2010; Johnson, Cohen, Kasen, & Brook, 2007; Jolin & Weller, 2011; Landhuis, Poulton, Welch, & Hancox, 2007; Mistry, Minkovitz, Strobino, & Borzekowski, 2007; Obel et al., 2004; Pagani, Fitzpatrick, Barnett, & Dubow, 2010; Russ, Larson, Franke, & Halfon, 2009; Stevens & Mulsow, 2006; Swing, Gentile, Anderson, & Walsh, 2010; Thakkar, Garrison, & Christakis, 2006; Zimmerman & Christakis, 2007). Furthermore, these negative effects of television do not only result from children intentionally viewing television programs. One recent study showed that exposure to adult-directed background television at age 1 and overall household television use at age 4 predicted low executive function at age 4 (Barr, Lauricella, Zack, & Calvert, 2010). Another study showed that earlier exposure to both background and foreground television was related to poorer executive function ability, even when the content was child directed (Nathanson, Alade, Sharp, Rasmussen, & Christy, 2014). Additionally, total television and video game exposure in middle school was found to be related to attention problems 13 months later, controlling for earlier attention (Swing et al., 2010). Johnson et al. (2007) obtained similar findings with adolescents: the amount of television watched at age 14 predicted later attention problems. Various theories have been proposed to account for these findings. One is that they relate to time use. According to this theory, it is not that the television impairs attention, but rather, that time spent watching television is time away from other activities, such as reading, that train executive capacity. Another is that the rapid scene changes and high levels of sensory stimulation associated with television—especially entertainment and violent content television—interfere with attentional capacities. According to this theory, television during time periods when attentional capacities are developing might be particularly detrimental.


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A perennial problem with studies showing that something at Time 1 predicts something at Time 2 is determining causality. Perhaps children with manifest or even latent attention problems choose to watch more television, and the exposure to television has nothing to do with the attention problems. Ideally one could randomly assign children to watch television or not, then examine them for attention and executive function problems several years later. Of course, there are many impediments to such a study, from the fact that few parents would willingly have their child assigned to either group, and parents cannot perfectly control children’s television exposure regardless. Mice parents and baby mice are much more controllable, and recently a study was conducted using a mouse model. This study was carried out by Christakis and his colleagues, who posit that it is not any early television exposure, but rather that it is exposure to particular television content within a sensitive developmental period (Lillard & Erisir, 2011) that impairs the developing attention system (Christakis, 2009; Thakkar et al., 2006). In their study, for 6 h each day, from postnatal day 10 and continuing for 42 days, mice had Cartoon Channel audio (at normal loudness) piped into their cages, while a photorhythmic modulator programmed LED lights in each corner of the cage to change color and intensity in concert with audio changes (Christakis, Ramirez, & Ramirez, 2012). Ten days later, when tested on a battery of behavioral and cognitive tests (e.g., open field, mazes), the experimental mice performed worse, were hyperactive, and failed to show species-typical caution, when compared to control mice. The rapid changes in visual and auditory stimuli during a sensitive period of rapid synaptic growth and pruning were thought to explain these subsequent behavioral effects. However, one might argue that these effects are specific to mice; human brains are much more complex, and humans have a good deal of other complex input even when they watch a great deal of television. Although long-term associations between attention and television have been established for humans, there has been very little investigation of immediate impacts or possible mechanisms by which television might impact executive function. If certain television content makes children less able to concentrate and follow rules immediately afterward, then repeated viewing of such content might lead to longer term impairment. One could also argue the opposite—that repeated viewing might build an attention “muscle” (Baumeister, Vohs, & Tice, 2007). However, this seems unlikely, at least at usual levels of viewing. This is because of a lack of positive lagged associations between television and attention. If repeated television viewing

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builds an executive function muscle, then there should be reports showing that the more television watched in early childhood, the greater one’s executive function later. Instead, existing reports show either a negative impact or no impact over the long-term.

5. SHORT-TERM STUDIES OF TELEVISION AND EXECUTIVE FUNCTION Aside from those conducted in our laboratories (which are described later in this chapter), we have located five studies of the immediate influence of television on executive function; one was conducted with adults and four with preschoolers. With adults, one study showed that after 30 min viewing either a highly arousing clip from the movie Doom or a banal tennis match, Doom viewers performed worse on a test of attention in which they were required to hold rules in mind and mark symbols according to those rules in a timed test (Maass, Klpper, Michel, & Lohaus, 2011). Four earlier studies involved preschoolers. Two of these found an influence of television on executive function and two did not. Geist and Gibson (2000) showed preschoolers 30 min of PBS or network television shows, specifically Mister Rogers’ Neighborhood or Mighty Morphin’ Power Rangers, then coded their behavior for 30 min in a playroom. The control group went straight to the playroom, which had seven activity centers, such as a water table and a table of math games. Relative to controls, children who had watched Mighty Morphin’ Power Rangers switched activity centers more often and spent less time at each, whereas children who watched Mr. Rogers’ Neighborhood behaved no differently than children in the control group. Similar findings were obtained over 30 years ago in a short-term longitudinal study in which children watched aggressive (cartoon versions of Batman and Superman) or prosocial (Mister Rogers’ Neighborhood) shows over 4 weeks in preschool. Classroom behaviors were coded at baseline and during the viewing period. Over 4 weeks, children who watched aggressive television became less patient (waiting for teacher attention) and obedient, whereas those who watched prosocial television became more patient for teacher attention and engaged longer in tasks (Friedrich & Stein, 1973). Although selected for specific features like aggression, in both of these studies, the shows that had negative effects on executive function abilities also differed in other ways. One other way they differed is what is termed “pacing” in the television literature. Pacing has been defined in myriad ways (see Table 1).


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Table 1 Some Prior Operationalizations of Television Pacing

Anderson, Levin, and Lorch (1977)

(a) Frequency of camera or editing actions, (b) frequency of changes to an essentially new visual scene, (c) percentage of active motion, (d) frequency of auditory changes (e.g., change from voice to music), (e) percentage of lively music, (f ) percentage of aroused, active talking, and (g) segment length

Cooper, Uller, Pettifer, and Stolc (2009)

Frequency of camera angle changes

Huston et al. (1981)

(a) Variability (rate of changes to scenes not previously shown in the program) and (b) tempo (rate of changes in scenes previously shown in the program plus the rate of character change)

Lang, Geiger, Strickwerda, and Sumner (1993)

Related cuts

Lang, Bolls, Potter, and Kawahara (1999)

The number of times a structural feature known to elicit orienting in attentive television viewers occurs

McCollum and Bryant (2003) (a) Frequency of camera cuts, (b) frequency of related scene changes, (c) frequency of unrelated scene changes, (d) frequency of auditory changes, (e) percentage of active motion, (f ) percentage of active talking, and (g) percentage of active music Watt and Krull (1974)

Frequency of verbal utterances and set changes

Watt and Welch (1983)

Visual dynamic complexity: the unpredictability, or difference, in light levels on the screen over time

In one study (McCollum & Bryant, 2003) that coded some of the involved shows (among many others—85 popular children’s shows in all), pacing was defined as frequency of scene changes (related and unrelated), camera cuts, auditory changes, talking, music, and motion. Mighty Morphin’ Power Rangers was among the fastest paced shows with a score of 41.90 and Mister Rogers’ Neighborhood was the slowest, with a score of 14.95. Batman (it is not clear whether this was the cartoon or real version—the study mentioned earlier used the cartoon) scored 25.85. In addition, then, to differing in terms of aggressive content, the shows used in these two studies happened to also vary in pacing, with faster-paced shows associated with increases in behaviors associated with poor executive

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functioning. In the adult study, even without formal study, it seems that the tennis match was more slowly paced than Doom. This raises the possibility that fast television pacing causes poor executive function. Two other studies controlled for content but systematically varied pacing. Cooper et al. showed 4-, 5-, and 6-year-olds fast- or slow-paced (with pacing defined only as camera cuts) 3.5-min clips of an adult reading a story (Cooper et al., 2009), then administered the Attentional Network Task, which tests for the executive function skills of alerting, orienting, and resolving conflict. Afterward, 4-year-olds who saw the slow clip oriented better on the Attentional Network Task, but for 6-year-olds the findings were the opposite: those who saw the fast clip oriented better. Across the whole sample, there were fewer errors made by those who saw the fast clip, perhaps due to increased arousal. There were no differences on alerting, conflict, or overall reaction time based on pace. However, as will become clear later, it is possible that despite the differences in pacing, the content (a person reading) presented little encoding challenge, at least in the short presentation period (3.5 min) used in this study. A second study that controlled pacing, also conducted over 30 years ago, created two 40-min episodes of Sesame Street by splicing together fast- or slow-paced bits from four episodes (Anderson et al., 1977). (At that time, the program was in magazine format and composed of several self-contained mini-stories termed “bits.”) In this study, pacing referred not only to camera angle changes but also to factors such as voice changes (see Table 1). Pacing was found to have no effect on preschoolers’ subsequent impulsivity and task persistence (tested immediately after viewing), which with other findings suggested that television pacing is not problematic for subsequent executive function (Anderson et al., 2001). However, it is possible that even the fastpaced episode was not particularly challenging to encode, in that even fastpaced bits of Sesame Street 30 years ago were not fast by today’s standards. The show’s rate of camera cuts doubled from 1980 to 2000 (Koolstra, van Zanten, Lucassen, & Ishaak, 2004), yet even around 2000, it was one of the slowest paced children’s television programs on the air (McCollum & Bryant, 2003).

6. PROCESSING OF TELEVISION Other studies have looked at the effect of pacing on ongoing attention to and processing of television, which might have implications for its influence on executive function immediately after viewing. Wright and colleagues systematically varied television pacing (defined as scene and


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character changes), and found more gaze shifts during fast-paced programs, implying that bottom-up attention (Chun, Golomb, & Turk-Browne, 2011) was grabbed by salient features of the shows (Wright et al., 1984). In addition, fast pacing in television shows was found to negatively impact memory for show sequences, suggesting processing overload. Faster pacing also impairs adults’ processing of television (Lang et al., 1999; Lang, Zhou, Schwartz, Bolls, & Potter, 2000). Increased reliance on bottom-up attention (rather than top-down attention) and difficulty processing television could both lead to lower levels of executive function subsequent to viewing. Lang has shown that processing television is a function of resources allocated to processing the message, minus resources consumed by processing it (Lang, Kurita, Gao, & Rubenking, 2013). If one uses more resources than were allocated, then one runs out of resources and cannot process the message. Allocation of resources is increased with increased orienting responses, caused in part by cuts and other structural features of the program being watched. Use of resources is determined by the amount of new information introduced, such as new objects, changes in existing objects, and other similar situations. Perhaps for both of the studies that showed no effect of pacing on children’s executive function, faster pacing increased the resources allocated to processing television, and there was relatively little new information introduced. As a result, the stimuli were not challenging enough to cause a processing overload that would have then impaired executive function. This proposed relationship will be further explained below. Thus far, we have focused on pacing as a cause of information processing and executive function problems during exposure to fast-paced television. There is also some support for the view that the content of television programs causes these difficulties (Huston & Wright, 1983). Content presenting fantastical or physically impossible events1 could be specifically problematic for subsequent executive function performance. When Coyote chases after Road Runner until he is suspended in a cloud, where he remains for an impossibly long time before dropping down (“Zoom and Bored” episode), physics have been violated; the event is fantastical. Doom, Batman, and Superman also show physically unrealistic events. In contrast, events in television shows like Mr. Rogers and Sesame Street are typically realistic, at 1

Fantasy can also refer to cartoons or to humanized animals. Although children do not learn as well from cartoons as from more realistic pictures, they do learn to some degree from cartoons (Ganea, Pickard, & DeLoache, 2008). Regarding humanized animals, children appear to readily accept them, even interpreting pulsating blobs as having human-like goals or intentions (Hamlin, Wynn, & Bloom, 2007). Fantasy in our discussion refers to physically unrealistic events.

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least in physics terms. In an earlier era, presentation of such violations was largely confined to magic shows, but moving pictures easily present fantastical events. Why might observing fantastic events lead to lower executive function? Humans are theorized to possess a “naı¨ve physics,” an innate representation of the laws governing physical events (Spelke, 1994). Even if those representations are not innate, they do appear very early in life, such that by age 4 one has strong expectations of how physical events should occur (Shtulman & Carey, 2007). In one view, physically ordinary events are “scaffolded” by expectations formed over ontogeny and phylogeny; human beings are “prepared” to represent them (Williams, Huang, & Bargh, 2009). In Piagetian terms, ordinary events can be assimilated to existing cognitive structures, whereas one’s cognitive structures need to be altered to accommodate to novel events. Accommodation clearly takes more processing resources than assimilation, since it must require neuropil alteration (for example, new dendritic spines). Based on our studies with fantastical television shows, we hypothesize that events that violate these innate or at least well-rehearsed representations are more difficult to process, and thus require more cognitive resources than events that adhere to the physical laws of reality. It might also be the case that we allocate more resources to such events, because they are “attentiongrabbing.” This possibility is compatible with our hypothesis. Repeatedly needing to allocate more cognitive resources to novel events in fantastical shows is hypothesized to deplete cognitive resources over the course of 10–20 min of viewing time. Fantastical events can be regarded as “new information” in Lang’s model (described above), because they violate expectations of how things should happen; such events might therefore require more processing resources. The processing demands of fantastical events have not to our knowledge been a focus of television research. A prior analysis of children’s television shows (Huston & Wright, 1983; Huston et al., 1981) mentions “incongruity” and “visual tricks,” but did not focus on these features. Results from our studies of television, discussed next, suggest that the fantastical events contained in television programs might be very important.

7. OUR STUDIES In our first experiment to test whether fast and fantastical television shows might influence later executive function, Jen Peterson and


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I randomly assigned sixty 4-year-olds to watching SpongeBob, watching Caillou (a slow-paced cartoon about a young boy, devoid of fantastical events), or free drawing in a laboratory testing room for 9 min (Lillard & Peterson, 2011). Pacing was roughly determined by counting scene changes per minute; by this measure, SpongeBob was three times faster than Caillou. Each child was given four posttests of executive function. One was the Backwards Digit Span (McGrew & Woodcock, 2001), in which children are read increasingly long lists of numbers, and must repeat them backward. Another was a child-friendly version of the classic Tower of Hanoi, in which people need to move objects according to specific rules in order to match a pattern. The third task was Head-Toes-Knees-Shoulders (HTKS) (Ponitz et al., 2008, 2009), a Simon-Says like game in which children must do the opposite of what is asked. The fourth test of executive function was the classic Delay of Gratification task (Mischel et al., 1989), in which children need to wait to receive a larger food reward, or can ring a bell to get a smaller reward sooner. In addition, we thought that there might be something good about watching SpongeBob, namely that it might increase creativity. Our thinking was simply that when watching SpongeBob children see reality changed in myriad ways, and this might lead them to think more creatively afterward. To measure this, we administered the Alternate Uses task, in which people are asked to think of all the uses they can come up with for each set of everyday objects (Dansky, 1980). Interestingly, there is some controversy regarding whether creativity is best when one has high or low executive function. Some that one must inhibit the typical uses in order to think of new ones (Diamond, 2013), whereas others find that people are more creative when they are poor at inhibition—thus, theoretically, at inhibiting unusual uses (Thompson-Schill, Ramscar, & Chrysikou, 2009). It seems likely that both processes operate in creativity and that what might be essential is cognitive control: exerting or removing inhibitory processes as needed. While children were watching the shows and taking these tests, their parents completed a media survey of how much the children watched television and what programs; and “Strengths and Difficulties,” a scale addressing attention problems (among other things) that is related to the widely used but longer Achenbach Child Behavior Checklist (Goodman, 1997, 2001). To examine possible experimenter effects, half of the children in each condition were tested by a posttest experimenter who was blind to the study hypotheses. This will not be discussed further, because it had no impact on results (in fact, effects were larger with the blind tester).

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There were no group differences in parent ratings of attention problems nor in the amount of television or specifically SpongeBob typically watched by the children. Cronbach’s alphas showed that performance on the Delay of Gratification task did not correspond to performance on the other executive function tests, which is a finding consistent with other studies (Diamond & Lee, 2011; Huizinga, Dolan, & van der Molen, 2006). Because a single test of executive function is not as reliable as a composite score (Wiebe et al., 2011; Willoughby, Wirth, & Blair, 2011, 2012), we summed Z-scores of responses to HTKS, Backwards Digit, and Tower of Hanoi (to put them on the same scale, equally weighted), and compared these sum scores across the groups. Thus, three executive function tasks were analyzed together (as a sum of the Z-scores), and responses to the Delay of Gratification task was analyzed separately. Both using a standard ANCOVA (with age covaried) and regression (with age, attention problems, and television-per-week entered at a first step), and both for the Delay of Gratification measure and the executive function composite, the children who watched SpongeBob performed worse than those who drew or watched Caillou. We had expected that SpongeBob might increase creativity, but it did not. Although there are many differences between our experimental conditions, we hypothesized that the combination of fast pacing and fantasy in SpongeBob caused the effect. This is because the fantasy events are difficult to process (we hypothesize), due to the child having no existing scripts or schemas to which to assimilate them, and because these difficult-to-process events are also arriving in rapid succession. In our second study to test whether fast pacing and fantasy might be at issue, we examined children’s executive function following: (1) a new episode of SpongeBob, (2) a different fast and fantastical cartoon (Fan-Boy and Chum Chum), and (3) a different slow, realistic cartoon (Arthur). We also changed the control condition to playing instead of drawing, and checked to see if 6-year-olds’ executive function was also influenced by these experiences. Furthermore, we used full 11-min episodes of the shows (often two 11-min episodes are paired for a 30-min television slot, with commercials.) In all, 160 children were shown an episode or played, followed by a similar battery of executive function tests; their parents completed the same surveys. Again, there were no a priori differences in attention or media exposure between the conditions. A two-way ANOVA with age group (4 and 6) and condition (SpongeBob, FanBoy, Arthur, Playtime) showed a significant effect of condition, F(3, 159) ¼ 3.34, p ¼ 0.02; post hoc t-tests revealed that children who had watched the fast and fantastical shows performed worse


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on the executive function composite than children who had played. The performance of children in the Arthur condition was intermediate—worse than players, but better than children who watched SpongeBob. This is similar to the Caillou children in the initial study. There was no significant Age Condition interaction, suggesting that the effect does not wane significantly from age 4 to 6. The second follow-up study used a 2 2 factorial design to examine the separate contributions of fantasy and fast pacing to executive function. Pacing was determined by a computer program called Scene Detector, a movie editing tool that uses percentage of pixels changed to determine when a scene has changed. The four shows were Little Bill (slow, realistic), Little Einsteins (slow, fantastical), Phineas and Ferb (fast, realistic; the only fantastical feature in the episode, a talking platypus, was edited out), and a different episode of SpongeBob (fast, fantastical) than we had used in the previous two studies. Eighty 4-year-olds were given pre- as well as posttests of executive function, and parents completed the short form of the Child Behavior Questionnaire or CBQ-SF (Putnam & Rothbart, 2006). There were no preexisting group differences on the CBQ-SF subscales most relevant to executive function or on our pretests executive function. An ANCOVA with age and pretest executive function score as covariates showed a significant effect for fantasy, F(1, 75) ¼ 5.04, p ¼ 0.03, but not for pacing. Follow-up tests showed that children did as poorly on the executive function tests after Little Einsteins (slow, fantastical) as after SpongeBob (fast, fantastical), but did equally well after Phineas and Ferb (fast, realistic) as after Little Bill (slow, realistic). From this study, it appears that fantastical events, but not pacing, are responsible for children’s poor executive function skills following certain television shows. We were also interested in whether educational television might have similar effects. To examine this, Eve Richey conducted a third follow-up with 60 4-year-olds. She tested whether a fast, fantastical PBS show designed to teach children vocabulary, Martha Speaks, would be as problematic as SpongeBob. Pacing (judged by Scene Detector) and fantasy content (the number of unique physically impossible events) were similar in the two shows. In the episode of Martha Speaks, for example, a child’s school desk dropped through the floor, emerged from the school, and flew through the air. Control group children read a book version of Martha Speaks, with the reading taped and signals in the tape indicating when to turn the page; the reading and the videos each lasted about 22 min. Unlike the television show, Martha Speaks books do not portray physically impossible events.

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We again found that fantastical television, even when intended to teach vocabulary, significantly impaired executive function: F(2, 59) ¼ 5.51, p ¼ 0.007; follow-up tests showed that children in both video conditions performed worse on the executive function tests than children in the book condition. The vocabulary words were not learned with either Martha Speaks presentation. To summarize our research so far, we have found four television shows that negatively impact executive function relative to control (play, art, and reading) conditions and/or other shows (see Table 2). These effects were seen in children of ages 4 and 6. Fuxing Wang (unpublished raw data) also conducted a study in our laboratory at the University of Virginia with undergraduates, having them watch either SpongeBob or Bob’s Burgers, a funny cartoon without fantastical events. Afterward, they completed a battery of computerized tests of executive function, such as the Wisconsin Card Sort. In undergraduates, there was no evidence that the fantastical events impaired executive function. This suggests the effect disappears sometime between the ages of 6 and 20 (although these authors feel depleted after watching fantastical shows!)

Table 2 Executive Function Results and Some Characteristics of Shows Used in Studies 1–4 Show Diminished Producer's “Commonsense Executive Fast Intended Media” Target Function? Show Paced Fantastical Audience Age Age

Yes

SpongeBob

Yes

Little Einsteins

Yes

FanBoy & ChumChum

x

x

6–11

6

x

4–6

4

x

x

6–9

7

Yes

Martha Speaks x

x

4–7

4

No

Little Bill

4–6

4

No

Caillou

3–6

3

No

Arthur

4–8

5

No

Phineas and Ferb

6–11

5

x


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As can be seen in Table 2, fantasy (in the sense of physically impossible events) appears to be more important than pacing for subsequent executive function. Another variable that could be responsible for the effect is the fact that some shows are aimed at an older target age than was tested (thus, message complexity/comprehensibility might have caused the effect). Although the intended age range and the age recommendation of a respected parent media website (Commonsense Media) were similar for some shows that did and did not cause the effect, it is possible that something about comprehensibility of the story line is responsible; we have not firmly established that fantasy is the reason for our finding. Besides intended audience age, future research might look at themes of a show, identification with show characters, and children’s level of arousal while viewing as possible causes. In other research, we have examined whether Chinese children would show the same effects as American ones. Chinese preschoolers are known to have higher levels of executive function than their American counterparts (Lan, Legare, Ponitz, & Morrison, 2011; Sabbagh, Xu, Carlson, Moses, & Lee, 2006), which might render them impervious to the negative influence of television on executive function found with the samples of American children. In China, there is little research on children’s television, and there are no official recommendations regarding children’s television viewing. In collaboration with other Chinese colleagues, we investigated both the association between cartoon viewing and executive function, and the immediate influence of two different cartoons (educational vs. entertainment) on children’s executive function. We first employed a parent survey to examine current and predictive relationships between television viewing and executive function from ages 2 to 5. The parent survey was given three times, 6 months apart. Executive function was measured with 15 items intended to tap inhibition, shifting, emotional control, working memory, planning, and organizing; these were adapted from sample items on a published survey (Isquith, Gioia, & Espy, 2004). Example items included, for example, “When asked two things to do, remembers only the first or last” and “Has trouble in concentrating on games, puzzles, or play activities.” Parents rated each item as not true, somewhat true, and certainly true of their child; these scores were converted to 3, 2, and 1, respectively, and were added to create an index of EF (ranging from 15 to 45). This 15-item scale was pretested on 855 preschoolers, and the results suggested good statistical properties (e.g., Cronbach’s alpha ¼ 0.82).

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Children had watched a total of 867 television shows, many of which were American shows with Chinese language dubbed in; average viewing was between 7 and 8 h per week across the testing points. The great majority of shows were for entertainment, as educational television is rare in China. Multilevel modeling was used to examine the development of executive function across the three time points. The most striking result was a decrease of 0.012 points on average in children’s executive function for each additional hour per week of viewing television, after age and gender were accounted for. Although this in some ways seems small, our scale was of limited range (15–45), and even small differences can be quite meaningful at the population level. Our second Chinese study attempted to establish whether a similar relationship between television viewing and executive function could be found after short-term exposure in Chinese children. Ninety preschoolers (ages 4–6) were randomly assigned to either an entertaining cartoon group which viewed Tom & Jerry, an educational cartoon group which watched Mickey Mouse Clubhouse, or a no television group that played freely in their classrooms, using a 3 3 between-subjects design. Scene changes occurred at a similar rate in the two videos, as measured by Scene Detector. Mickey was agreed by a panel of judges to be educational, and was shown by objective coding to be less fantastical, although it still showed 17 fantasy events lasting a total of 107 s. Tom & Jerry was judged by the panel to be an entertainment show, and coding revealed that the stimulus showed 46 fantasy events, lasting 218 s. For a subset of the children, a Tobii T120 eye tracker recorded eye movements during viewing to determine whether increases in attentional processing could be responsible for any effects on executive function. Next, children in all three groups completed three executive function tasks. These were Backward Digit Span, as in the prior studies; Day–Night (Gerstadt, Hong, & Diamond, 1994), in which children must say “Night” to a picture of a sun and “Day” to a picture of a moon; and the Flexible Item Selection task ( Jacques & Zelazo, 2001) in which children must change the criteria by which they categorize a set of items. These are thought to mainly assess working memory, inhibitory control, and set shifting, respectively. A composite executive function score was created from the sum of standardized scores on the three tasks. Parent-report measures of the amount of television children typically watched each week, the content of those television programs, and the child’s attention level were used as control variables.


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The eye tracking results showed significant differences in how children in the two television exposure groups viewed the videos. Children viewing the entertainment program had significantly shorter average fixation durations during viewing than did children who watched the educational program, t(16) ¼ 3.68, p < 0.005, Cohen’s d ¼ 1.72. The average number of fixations per minute in the entertainment group was significantly greater than in the educational group, t(16) ¼ 4.93, p < 0.001, Cohen’s d ¼ 2.29. Shorter average fixation durations meant children sustained their attention on one stimulus for shorter periods of time; the greater number of fixations suggests that children shifted their attention more frequently. There were also significant age effects on executive function, F(2, 87) ¼ 14.48, p < 0.001, ηp 2 ¼ 0:25. Because of this, an ANCOVA with age as the covariate was used to analyze whether there was a main effect of condition on executive function. The results indicated that there was a significant condition effect, F(2, 86) ¼ 6.99, p < 0.005, ηp 2 ¼ 0:14. Post hoc Tukey’s tests indicated that children in the entertainment television group had lower posttest executive function scores than children in the educational television group, t(58) ¼ 2.56, p < 0.05, Cohen’s d ¼ 0.66, and the control group, t(58) ¼ 2.95, p < 0.01, Cohen’s d ¼ 0.76. The latter two groups did not differ. Study 2 suggested increased orienting responses occur during an entertainment show. More direct readings of children’s neural activity while watching the shows could be useful to determine the cause of the detriment. A third study was conducted in China to better investigate the cause of the negative influence of television on executive function found in the two studies just described. We tested activation of the PFC during children’s viewing of the same shows used in the prior study, using fNIRS to reveal successive changes in the concentrations of oxygenated (O2Hb) and deoxygenated (HHb) blood during children’s viewing. Data collection with fNIRS is particularly well suited for child participants, because it has far fewer body movement restrictions and is noiseless (Moffitt et al., 2011). Twelve laser optodes (connected to 24 laser sources through bifurcated cables with cables of 690 and 830 nm paired and combined into one laser optode) were used and evenly assigned to subjects who watched the video simultaneously. This allowed us to more directly examine the internal activity of children’s PFCs while viewing the television shows. HomER (Hemodynamic Evoked Response) software was used to analyze the changes in oxy-Hb, which were assumed to be a more sensitive reflection of cognitive activation than deoxy-Hb changes (Hoshi, Kobayashi, & Tamura, 2001; Strangman, Culver, Thompson, & Boas, 2002), because previous research

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Figure 1 Time course for concentration of oxy-Hb in the prefrontal cortex during the first 6 min of viewing.

has indicated that cerebral blood flow increases in response to neuronal activation (Fox & Raichle, 1986). Figure 1 shows the level of oxy-Hb in the PFC for each group during the first 6 min of viewing the shows. Summing across the entire 6 min of viewing, the fNIRS results showed no significant group differences in levels of oxy-Hb in the orbitofrontal cortex during viewing. However, visual inspection of the figure reveals four clear epochs in which one group exceeded the other to some degree in prefrontal processing. During the first 74 s of viewing, the level of prefrontal processing was higher for the entertainment group, t(19) ¼ 2.05, p ¼ 0.05, Cohen’s d ¼ 0.94. For the next 35 s, although it appears to be higher for the educational group, the difference was not significant. For the next 120 s, from 112 to 225 s, there was also significantly higher activity in the PFC for the entertainment group, t(19) ¼ 2.32, p < 0.05, Cohen’s d ¼ 1.06. Finally, during the remainder of the recorded viewing time (226–410 s), the educational group generally showed higher activity; statistical analysis showed this was a trend, t(19) ¼ 1.87, p ¼ 0.07, Cohen’s d ¼ 0.86. Thus, it appears that, overall, activity was higher for the entertainment group for approximately the first 4 min of viewing, and then the activity dropped off.


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We see two ways to interpret these results. One possibility is that the increased orienting required by the entertainment cartoon increased processing to a point (the first 4 min of viewing). After that, the system became overloaded and prefrontal processing shut down. In support of this, although not directly examining neural activity, Lang and her colleagues showed that adults’ allocation of processing resources to television messages becomes taxed when camera angle changes become excessive, reaching cognitive overload (Lang et al., 2013). The second possibility hinges on the element of fantastical events rather than orienting responses. It is possible that when first shown fantasy events, children attempt to process them (using cognitive resources) and then, because fantasy events are incomprehensible, they stop trying to process them. This would also render the PFC less active for the remainder of viewing, and also for subsequent executive function tasks. Although we know of no literature on how children process fantastical events, we do know that children have difficulty filtering out irrelevant events, which can then overload processing (Ridderinkhof, van der Molen, Band, & Bashore, 1997). Perhaps the fantasy events are similar to irrelevant events: they do not fit the standard schematic narratives of how things happen in the world. In sum, according to this second possibility, cognitive resources are initially allocated to process fantasy events (and notably, the educational cartoon did have some fantastical events in the first minute), but the processing system becomes overloaded by them (particularly with the entertainment cartoon, which shows fantastical events throughout) and ceases to attempt the processing. It would be useful in future research to compare clips with defined, occasional fantastical events with realistic clips that require repeated orienting responses, to see whether processing reliably decreases during or after fantastical events. Such research could tease apart the two possibilities just mentioned.

8. MODELING HOW FANTASTICAL TELEVISION MIGHT INFLUENCE EXECUTIVE FUNCTION Here, we present a new theory—hinted at in the preceding pages— regarding why certain television shows deplete executive function. Our thinking is grounded in information processing theory and research on adult television processing (Lang, 2000; Lee & Lang, 2013), attention (Petersen & Posner, 2012), and executive function (Diamond, 2013). The basic premises

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Top-down Attention

W. Memory (Sensory receptors/store)

Encoding

Processing

Bottom-up

Figure 2 Information flow during television viewing.

are that: (1) information processing resources (such as neurotransmitters and/or perhaps glucose) are limited, (2) some shows use more processing resources than others (Lang, 2000), and (3) those resources are needed to perform our executive function tasks. Taking these premises into account, then, to the extent that the resources are depleted by a show observed just prior, performance on the executive function tasks suffers. Arousal also interacts with this system, as it has an upside-down U-shaped relationship to information processing (Yerkes & Dodson, 1908): too little or too much impairs it. Below, we will spell this theory out more fully (Figure 2). Watching television entails attending to and encoding messages in auditory and visual streams, processing those messages in working memory, and storing and retrieving them dynamically in order to continuously interpret newly arriving messages (Lang, 2000). Attentional processes direct sensory receptors (eyes and ears) to attend to particular locations or sounds in a top-down fashion, and bottom-up responses (orienting responses to visual stimuli, and alerting responses to auditory ones) also control the allocation of attention resources. Attended stimuli enter the brain through the sensory receptors and are held briefly in the sensory store, from which some of the information is encoded. Encoding involves selecting information from the sensory store, which is then passed to working memory for processing.2 These same processes are also important to maintaining attention and performing executive function tasks. Our hypothesis is that watching fantasy events quickly exhausts attention and/or processing resources, making them unavailable for the subsequent executive function tasks. This results in the immediate, short-term impairments we record in most of our studies. However, repeated viewing leads to so many of these short-term impairments that it disrupts the normal development of the information processing system.

2

Although conventionally discussed as if different levels were locations, this is for some levels metaphorical, and a more true description might involve neuron or connection state.


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First, consider how these processes are entailed in performing the executive function tasks. For every executive function task, a child must pay attention to instructions and keep attending to those instructions (in working memory) while carrying out the tasks. For example, for HTKS, a child must attend first to instructions (“When I say touch your head, I want you to touch your toes”) and then, holding those instructions in mind, must attend to the commands (“Touch your head”) and monitor their own behavior to handle the conflict inherent in doing the opposite of the instructions. For the memory span task, a child attends to instructions to repeat a string of words, and then must attend to what those words are, holding them in memory to repeat (and for backward tests, while reversing them). In contrast, for a delay task, a child must attend to, encode, and process instructions to wait, but while waiting might not continuously monitor those instructions; children who perform best often reimagine the circumstances or the desired objects (Mischel et al., 1989). The Tower of Hanoi puzzle task also involves attending to instructions, keeping them in mind, and envisioning how disks (or in our child-friendly version of the task, monkeys) relate to one another while conforming to the rules and adjusting the relationships between puzzle pieces to meet a goal. Our hypothesis is that either fantasy events, and/or repeated orienting responses, on certain television programs quickly deplete these resources, rendering them less available for subsequent executive function tasks. Because we are focused mainly on the influence of fantastical events, next I explain how observing fantasy events on television might deplete these same resources. Although our evidence suggests that fantasy events are the main source of the problem, perhaps when such events are shown in rapid succession (as when shows are fast-paced, and thus more likely to require repeated orienting responses), it is particularly problematic.

8.1. Attention Both initially (in ontogeny) and perennially (across life) attention is controlled by bottom-up circuits originating in the visual/auditory cortices and extending to the temporal cortex (object identification) and parietal cortex (object locations) (Mechelli, Price, Friston, & Ishai, 2004; Sarter, Givens, & Bruno, 2001). By 4 years of age (Ruff & Rothbart, 1996), attention is also controlled by top-down resources that originate in prefrontal areas (Lang, 1990; McMains & Kastner, 2011; Mechelli et al., 2004). Fast-paced shows present many stimuli that capture attention in a bottom-up fashion, via both auditory and visual changes. Surprising

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events—which include fantastical ones, because unreal events are typically unexpected—capture attention as well. Orienting responses increase resources available for television processing to a point, after which the cognitive system is overloaded and incoming messages are not processed (Lang et al., 2013). This is consistent with research showing that children comprehend television better when there are more attention-grabbing sound effects (Calvert & Gersh, 1987), but at some point (and we cannot say at this time exactly what this point is) processing likely becomes overloaded and comprehension declines. In research to test this theory, use of attentional resources during television viewing could be monitored in at least three ways: eye movements, heart rate (HR), and skin conductance (SC). Increased eye movements while looking at the screen suggest increased bottom-up orienting responses; our Chinese study supports that preschooler’s attentional resources were particularly used while watching a fantastical entertainment show (as compared to a less fantastical educational show). Although visual attention can also be voluntarily assigned to a stimulus using top-down processes, young children’s television especially (Goodrich, Pempek, & Calvert, 2009; Huston et al., 1981) captures attention in a bottom-up fashion via changes in sound and light that reflect pacing. Young children’s attention to television also increases with cuts and movement (Schmitt, Anderson, & Collins, 1999) that are accompanied by sound and light changes. A child looking away from the screen likely indicates inattention, which could stem from boredom or lack of comprehension. When television messages are scrambled or foreign dialog is inserted, making the message incomprehensible, preschoolers look away from the television (Anderson, Lorch, Field, & Sanders, 1981). Based on prior research by Lang et al. (2013), we would expect that up to a certain level, bottom-up attention (orienting responses) should increase processing resources available. When a television show becomes too challenging (i.e., elicits an excessive rate of orienting responses), however, resources become insufficient and executive function is compromised. Reduced attention to the screen (looking away) would also lead to failure to encode show content.

8.2. Encoding/Processing The process of encoding entails getting the message from the sensory store into working memory. Encoding of television is compromised when pacing


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or message complexity exceeds information processing capacity (Lang et al., 1999, 2013). Although cuts evoke orienting responses, when a cut is followed by unrelated information, the new information is poorly encoded (Lang et al., 1993). Once information is in working memory, it can be processed and stored, making it available for retrieval; it can then be used both for interpreting later parts of the show and for recall after the show is over. Research with adults has shown that there is an inverted U function for memory and cuts, such that up to a point more cuts (faster pacing) improves memory, but beyond that number, memory is diminished (Lang et al., 1999). The reason for this appears to be that the cuts increase orienting responses, which increases resources allocated to processing; but once processing becomes overly challenging (because the information is too difficult), then the information is not encoded (Lang et al., 2013). In our experiments so far, we have not examined whether encoding and/or processing are disrupted by the television shows. Further research might examine this in at least two ways. First, one might examine encoding by testing for recognition memory of still shots of key show events (along with distractors) after viewing (a method used by Lee & Lang, 2013). Message difficulty would be expected to interact with recognition (see, for example, Thorson & Lang, 1992). Specifically, if encoding is at issue, recognition memory would be fairly constant for events occurring early and late in the easier shows, but memory for events in difficult shows would decline from the first to last minute of viewing due to overload. To test whether television stimuli create problems at the level of processing, children could be asked to arrange scrambled sets of still shots from the show to reflect their ordering in the show (an approach used by Wright et al., 1984). Children might conceivably do well on the first task, recognizing still shots, suggesting information was encoded, yet do poorly on the second task, suggesting they lack sufficient processing resources to commit the narrative sequence to memory for later recall.

8.3. Arousal In addition to examining effects at the levels of attention or encoding/ processing, future research should examine arousal, which has overall effects on processing of television (Lang, 1990; Lang, Dhillon, & Dong, 1995; Lang et al., 2000). Arousal stems from the reticular system signaling one to pay attention (Ravaja, 2004). High arousal is associated with challenge and excitement. To a point, increases in arousal improve message processing;

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at higher levels arousal leads to processing decrements (Lang et al., 1995; Pourtois, Schettino, & Vuilleumier, 2012). Levels of arousal could be indicated in future research on television’s effects on children’s executive function with measures of SC and HR.

9. CONCLUSION In sum, we hypothesize that certain television shows impair subsequent executive function because viewing the shows and performing the executive function tasks both draw on the same information processing resources. More attentional resources are allocated to the television shows with increased bottom-up pacing features (camera cuts). Top-down attentional resources and processing of the information in the television shows also use those resources. If processing the show is very challenging, resources are depleted and unavailable for the executive function tasks just after stimuli exposure. Content is more difficult to process when more new and unexpected information is presented. Impossible events are difficult because the human brain is not used to processing such events; novelty requires additional resources relative to familiar stimuli. At a point, however, those resources become unavailable, possibly because they are depleted, or possibly because the system makes a “choice” not to allocate resources to an impossible task. Arousal can improve these processes to a point, but if a child becomes overly aroused by a show, processing will suffer. Repeatedly experiencing difficult shows early in development could impair the development of processing networks, resulting in long-term executive function problems as suggested by Barr, Christakis, and others. But even short-term impacts are important because children do not function well when their executive function processes are depleted. Knowing what kinds of television cause this depletion will be helpful to those overseeing children’s television viewing, and by extension, to children who can benefit from higher executive function in many situations. We believe this line of research can provide valuable information to those who produce television content and also will have public policy implications. In addition, we hope that further research can experimentally determine whether the short-term negative impacts we observe translate into the long-term difficulties seen in some of our and many other laboratories’ research on the important issues of television and executive control.


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ACKNOWLEDGMENTS This work was supported by NSF grant 1024293 and a Brady Education Foundation grant to A. L., and a China Scholarship Council grant and the Excellent Doctorial Dissertation Cultivation grant #2013YBZD06 from Central China Normal University to H. L., and a National Natural Science Foundation of China grant #31300864 to Fuxing Wang. We are grateful to Fuxing Wang and the other members of the Early Development Laboratory for their support, as well as to the parents and children who participated in our studies.

REFERENCES Anderson, D. R., & Kerkorian, H. L. (in press). Media and cognitive development. In L. S. Liben & U. Mueller (Eds.), Handbook of child psychology and developmental science: Cognitive processes Vol. 2 (7th ed.). New York: Wiley-Blackwell. Andersen, R. E., Crespo, C. J., Bartlett, S. J., Cheskin, L. J., & Pratt, M. (1998). Relationship of physical activity and television watching with body weight and level of fatness among children: Results from the third national health and nutrition examination survey. Journal of the American Medical Association, 279, 938–942. Anderson, C. A., Berkowitz, L., Donnerstein, E., Huesmann, L. R., Johnson, J. D., Linz, D., et al. (2003). The influence of media violence on youth. Psychological Science in the Public Interest, 4, 81–110. Anderson, D. R., Huston, A. C., Schmitt, K. L., Linebarger, D. L., & Wright, J. C. (2001). Early childhood television viewing and adolescent behavior: The recontact study. Monographs of the Society for Research in Child Development, 66, 1–147. Anderson, D. R., Levin, S., & Lorch, E. (1977). The effects of TV program pacing on the behavior of preschool children. Educational Technology Research and Development, 25, 159–166. Anderson, D. R., Lorch, E. P., Field, D. E., & Sanders, J. (1981). The effects of TV program comprehensibility on preschool children’s visual attention to television. Child Development, 52, 151–157. Anderson, D. R., & Pempek, T. A. (2005). Television and very young children. American Behavioral Scientist, 48, 505–522. Barr, R., Lauricella, A., Zack, E., & Calvert, S. L. (2010). Infant and early childhood exposure to adult-directed and child-directed television programming: Relations with cognitive skills at age four. Merrill-Palmer Quarterly, 56, 21–48. Baumeister, R., Vohs, K., & Tice, D. (2007). The strength model of self-control. Current Directions in Psychological Science, 16, 351–355. Best, J. R., & Miller, P. H. (2010). A developmental perspective on executive function. Child Development, 81, 1641–1660. Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78, 647–663. http://dx.doi.org/10.1111/j.1467-8624.2007.01019.x. Bull, R., Espy, K. A., & Wiebe, S. A. (2008). Short-term memory, working memory, and executive functioning in preschoolers: Longitudinal predictors of mathematical achievement at age 7 years. Developmental Neuropsychology, 33, 205–228. Calvert, S. L., & Gersh, T. L. (1987). The selective use of sound effects and visual inserts for children’s television story comprehension. Journal of Applied Developmental Psychology, 8, 363–375. Carlson, S. M., Zelazo, P. D., & Faja, S. (2012). Executive function. In P. D. Zelazo (Ed.), Oxford handbook of developmental psychology: Vol. 1. (pp. 706–743). New York: Oxford. Christakis, D. A. (2009). The effects of infant media usage: What do we know and what should we learn. Acta Paediatrica, 98, 8–16.

244


Christakis, D. A., Ramirez, J., & Ramirez, J. (2012). Overstimulation of newborn mice leads to behavioral differences and deficits in cognitive performance. Scientific Reports, 2, 1–6. Chun, M. M., Golomb, J. D., & Turk-Browne, N. B. (2011). A taxonomy of external and internal attention. Annual Review of Psychology, 62, 73–101. Cooper, N., Uller, C., Pettifer, J., & Stolc, F. (2009). Conditioning attentional skills: Examining the effects of the pace of television editing on children’s attention. Acta Paediatrica, 98, 1651–1655. Dansky, J. (1980). Make-believe: A mediator of the relationship between play and associative fluency. Child Development, 51, 576–579. http://dx.doi.org/10.2307/1129296. Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 1–34. http://dx. doi.org/10.1146/annurev-psych-113011-143750, Advance online publication. Diamond, A., & Lee, K. (2011). How can we help children succeed in the 21st century? What the scientific evidence shows aids executive function development in children 4–12 years of age. Science, 333, 959–964. http://dx.doi.org/10.1126/science.1204529. Duncan, G., Dowsett, C., Claessens, A., Magnuson, K., Huston, A., Klebanov, P., et al. (2007). School readiness and later achievement. Developmental Psychology, 43, 1428–1446. Eisenberg, N., Spinrad, T. L., Fabes, R. A., Reiser, M., Cumberland, A., Shepard, S. A., et al. (2004). The relations of effortful control and impulsivity to children’s resiliency and adjustment. Child Development, 75, 25–46. Espy, K., McDiarmid, M., Cwik, M., Stalets, M., Hamby, A., & Senn, T. (2004). The contribution of executive functions to emergent mathematic skills in preschool children. Developmental Neuropsychology, 26, 465–486. Ferguson, C. J. (2011). The influence of television and video game use on attention and school problems: A multivariate analysis with other risk factors controlled. Journal of Psychiatric Research, 45, 808–813. Flavell, J. (1963). The developmental psychology of Jean Piaget. New York: D. Van Nostrand Co. Foster, E. M., & Watkins, S. (2010). The value of reanalysis: TV viewing and attention problems. Child Development, 81, 368–375. Fox, P. T., & Raichle, M. E. (1986). Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects. Proceedings of the National Academy of Sciences of the United States of America, 83, 1140–1144. Friedrich, L., & Stein, A. (1973). Aggressive and prosocial television programs and the natural behavior of preschool children. Monographs of the Society for Research in Child Development, 38, 1–64. Ganea, P. A., Pickard, M. B., & DeLoache, J. S. (2008). Transfer between picture books and the real world by very young children. Journal of Cognition and Development, 9, 46–66. Garon, N., Bryson, S., & Smith, I. (2008). Executive function in preschoolers: A review using an integrative framework. Psychological Bulletin, 134, 31–60. http://dx.doi.org/ 10.1037/0033-2909.134.1.31. Geist, E. A., & Gibson, M. (2000). The effect of network and public television programs on four and five year olds ability to attend to educational tasks. Journal of Instructional Psychology, 27, 250–261. Gerstadt, C. L., Hong, Y. J., & Diamond, A. (1994). The relationship between cognition and action: Performance of children 3½-7 years old on a stroop-like day-night test. Cognition, 53, 129–153. Goodman, R. (1997). Strengths and difficulties questionnaire. The Journal of Child Psychology and Psychiatry, 38, 581–586. Goodman, R. (2001). Psychometric properties of the strengths and difficulties questionnaire. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 1337–1345. Goodrich, S. A., Pempek, T. A., & Calvert, S. L. (2009). Formal production features of infant and toddler DVDs. Archives of Pediatrics & Adolescent Medicine, 163, 1151.


245

Hamlin, J. K., Wynn, K., & Bloom, P. (2007). Social evaluation by preverbal infants. Nature, 450, 557–559. Hoshi, Y., Kobayashi, N., & Tamura, M. (2001). Interpretation of near-infrared spectroscopy signals: A study with a newly developed perfused rat brain model. Journal of Applied Physiology, 90, 1657–1662. Huizinga, M., Dolan, C. V., & van der Molen, M. W. (2006). Age-related change in executive function: Developmental trends and a latent variable analysis. Neuropsychologia, 44, 2017–2036. Huston, A. C., & Wright, J. C. (1983). Children’s processing of television: The informative functions of formal features. In J. Bryant & D. R. Anderson (Eds.), Children’s understanding of television: Research on attention and comprehension (pp. 35–68). New York: Academic Press. Huston, A. C., Wright, J. C., Wartella, E., Rice, M. L., Watkins, B. A., Campbell, T., et al. (1981). Communicating more than content: Formal features of children’s television programs. Journal of Communication, 31, 32–48. Isquith, P. K., Gioia, G. A., & Espy, K. A. (2004). Executive function in preschool children: Examination through everyday behavior. Developmental Neuropsychology, 26, 403–422. Jacques, S., & Zelazo, P. D. (2001). The flexible item selection task (fist): A measure of executive function in preschoolers. Developmental Neuropsychology, 20, 573–591. Johnson, J. G., Cohen, P., Kasen, S., & Brook, J. S. (2007). Extensive television viewing and the development of attention and learning difficulties during adolescence. Archives of Pediatrics & Adolescent Medicine, 161, 480–486. Jolin, E. M., & Weller, R. A. (2011). Television viewing and its impact on childhood behaviors. Current Psychiatry Reports, 13, 122–128. Koolstra, C. M., van Zanten, J., Lucassen, N., & Ishaak, N. (2004). The formal pace of Sesame street over 26 years. Perceptual and Motor Skills, 99, 354–360. Lan, X., Legare, C. H., Ponitz, C., & Morrison, F. (2011). Investigating the links between the subcomponents of executive functioning and academic achievement: A cross-cultural analysis of Chinese and American preschool children. Journal of Experimental Child Psychology, 108, 677–692. Landhuis, C., Poulton, R., Welch, D., & Hancox, R. (2007). Does childhood television viewing lead to attention problems in adolescence? Results from a prospective longitudinal study. Pediatrics, 120, 532–537. Lang, A. (1990). Involuntary attention and physiological arousal evoked by structural features and emotional content in TV commercials. Communication Research, 17, 275–299. Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50, 46–70. Lang, A., Bolls, P., Potter, R. F., & Kawahara, K. (1999). The effects of production pacing and arousing content on the information processing of television messages. Journal of Broadcasting & Electronic Media, 43, 451–475. Lang, A., Dhillon, K., & Dong, Q. (1995). The effects of emotional arousal and valence on television viewers’ cognitive capacity and memory. Journal of Broadcasting & Electronic Media, 39, 313–327. Lang, A., Geiger, S., Strickwerda, M., & Sumner, J. (1993). The effects of related and unrelated cuts on television viewers’ attention, processing capacity, and memory. Communication Research, 20, 4. Lang, A., Kurita, S., Gao, Y., & Rubenking, B. (2013). Measuring television message complexity as available processing resources: Dimensions of information and cognitive load. Media Psychology, 16, 129–153. Lang, A., Zhou, S., Schwartz, N., Bolls, P. D., & Potter, R. F. (2000). The effects of edits on arousal, attention, and memory for television messages: When an edit is an edit can an edit be too much? Journal of Broadcasting & Electronic Media, 44, 94–109.

246


Lee, S. J., & Lang, A. (2013). Redefining media content and structure in terms of available resources: Toward a dynamic human-centric theory of communication. Communication Research, Advance online publication. Lillard, A. S. (2005). Montessori: The science behind the genius. New York: Oxford University Press. Lillard, A. S., & Erisir, A. (2011). Old dogs learning new tricks: Neuroplasticity before and after critical periods. Developmental Review, 31, 207–239. http://dx.doi.org/10.1016/j. dr.2011.07.008. Lillard, A. S., & Peterson, J. (2011). The immediate impact of different types of television on young children’s executive function. Pediatrics, 128, 644–649. http://dx.doi.org/ 10.1542/peds.2010-1919. Linebarger, D. L., & Vaala, S. E. (2010). Screen media and language development in infants and toddlers: An ecological perspective. Developmental Review, 30, 176–202. Maass, A., Klpper, K. M., Michel, F., & Lohaus, A. (2011). Does media use have a short-term impact on cognitive performance? A study of television viewing and video gaming. Journal of Media Psychology: Theories, Methods, and Applications, 23, 65. McCollum, J., & Bryant, J. (2003). Pacing in children’s television programming. Mass Communication and Society, 6, 115–136. McGrew, K. S., & Woodcock, R. W. (2001). Woodcock-Johnson III technical manual. Itasca, IL: Riverside Publishing. McMains, S., & Kastner, S. (2011). Interactions of top-down and bottom-up mechanisms in human visual cortex. The Journal of Neuroscience, 31, 587–597. Mechelli, A., Price, C. J., Friston, K. J., & Ishai, A. (2004). Where bottom-up meets top-down: Neuronal interactions during perception and imagery. Cerebral Cortex, 14, 1256–1265. Mischel, W., Shoda, Y., & Rodriguez, M. L. (1989). Delay of gratification in children. Science, 244, 933–938. http://dx.doi.org/10.1126/science.2658056. Mistry, K. B., Minkovitz, C. S., Strobino, D. M., & Borzekowski, D. L. (2007). Children’s television exposure and behavioral and social outcomes at 5.5 years: Does timing of exposure matter? Pediatrics, 120, 762–769. Miyake, A., & Friedman, N. P. (2012). The nature and organization of individual differences in executive functions: Four general conclusions. Current Directions in Psychological Science, 21, 8–14. Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., & Howerter, A. (2000). The unity and diversity of executive functions and their contributions to complex ’frontal lobe’ tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100. http://dx.doi. org/10.1006/cogp.1999.0734. Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H., et al. (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences of the United States of America, 108(7), 2693–2698. http://dx.doi.org/10.1073/pnas.1010076108. Mueller, U., Baker, L., & Yeung, E. (2013). A developmental systems approach to executive function. In R. M. Lerner & J. B. Benson (Eds.), Embodiment and epigenesis: Theoretical and methodological issues in understanding the role of biology within the relational developmental system (pp. 39–66). New York: Elsevier Academic Press. Nathanson, A. I., Alade, F., Sharp, M. L., Rasmussen, E. E., & Christy, K. (2014). The relation between television exposure and executive function among preschoolers. Developmental Psychology, 50, 1497–1506. http://dx.doi.org/10.1037/a0035714. Obel, C., Henriksen, T. B., Dalsgaard, S., Linnet, K. M., Skajaa, E., Thomsen, P. H., et al. (2004). Does children’s watching of television cause attention problems? Retesting the hypothesis in a Danish cohort. Pediatrics, 114, 1372–1373.


247

Pagani, L. S., Fitzpatrick, C., Barnett, T. A., & Dubow, E. (2010). Prospective associations between early childhood television exposure and academic, psychosocial, and physical well-being by middle childhood. Archives of Pediatrics & Adolescent Medicine, 164, 425. Petersen, S. E., & Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annual Review of Neuroscience, 35, 73–89. Ponitz, C. C., McClelland, M. M., Jewkes, A. M., Connor, C. M., Farris, C. L., & Morrison, F. J. (2008). Touch your toes! Developing a direct measure of behavioral regulation in early childhood. Early Childhood Research Quarterly, 23, 141–158. http://dx. doi.org/10.1016/j.ecresq.2007.01.004. Ponitz, C. C., McClelland, M. M., Matthews, J. S., & Morrison, F. J. (2009). A structured observation of behavioral self-regulation and its contribution to kindergarten outcomes. Developmental Psychology, 45, 605–619. http://dx.doi.org/10.1037/a0015365. Pourtois, G., Schettino, A., & Vuilleumier, P. (2012). Brain mechanisms for emotional influences on perception and attention: What is magic and what is not. Biological Psychology, 92, 492–512. Putnam, S. P., & Rothbart, M. K. (2006). Development of short and very short forms of the children’s behavior questionnaire. Journal of Personality Assessment, 87, 102–112. Ravaja, N. (2004). Contributions of psychophysiology to media research: Review and recommendations. Media Psychology, 6, 193–235. Ridderinkhof, K. R., van der Molen, M. W., Band, G. P. H., & Bashore, T. R. (1997). Sources of interference from irrelevant information: A developmental study. Journal of Experimental Child Psychology, 65(3), 315–341. http://dx.doi.org/10.1006/jecp.1997.2367. Rideout, V. J. (2011). Zero to eight: Children’s media use in America. San Francisco: Common Sense Media. Ruff, H. A., & Rothbart, M. K. (1996). Attention in early development: Themes and variations. New York: Oxford University Press. Russ, S. A., Larson, K., Franke, T. M., & Halfon, N. (2009). Associations between media use and health in us children. Academic Pediatrics, 9, 300–306. Sabbagh, M. A., Xu, F., Carlson, S. M., Moses, L. J., & Lee, K. (2006). The development of executive functioning and theory of mind: A comparison of Chinese and U.S. preschoolers. Psychological Science, 17, 74–81. Sarter, M., Givens, B., & Bruno, J. P. (2001). The cognitive neuroscience of sustained attention: Where top-down meets bottom-up. Brain Research Reviews, 35, 146–160. Schmidt, M. E., & Vandewater, E. A. (2008). Media and attention, cognition, and school achievement. The Future of Children, 18, 63–85. Schmitt, K. L., Anderson, D. R., & Collins, P. A. (1999). Form and content: Looking at visual features of television. Developmental Psychology, 35, 1156. Sharif, I., Wills, T. A., & Sargent, J. D. (2010). Effect of visual media use on school performance: A prospective study. The Journal of Adolescent Health, 46, 52–61. Shtulman, A., & Carey, S. (2007). Improbable or impossible? How children reason about the possibility of extraordinary events. Child Development, 78, 1015–1032. Spelke, E. (1994). Initial knowledge: Six suggestions. Cognition, 50, 431–445. Stevens, T., & Mulsow, M. (2006). There is no meaningful relationship between television exposure and symptoms of attention-deficit/hyperactivity disorder. Pediatrics, 117, 665–672. Strangman, G., Culver, J. P., Thompson, J. H., & Boas, D. A. (2002). A quantitative comparison of simultaneous BOLD fmRI and NIRS recordings during functional brain activation. NeuroImage, 17, 719–731. Swing, E. L., Gentile, D. A., Anderson, C. A., & Walsh, D. A. (2010). Television and video game exposure and the development of attention problems. Pediatrics, 126, 214–221. Thakkar, R., Garrison, M., & Christakis, D. A. (2006). A systematic review for the effects of television viewing by infants and preschoolers. Pediatrics, 118, 2025–2031.

248


Thompson-Schill, S. L., Ramscar, M., & Chrysikou, E. G. (2009). Cognition without control when a little frontal lobe goes a long way. Current Directions in Psychological Science, 18, 259–263. Thorson, E., & Lang, A. (1992). The effects of television videographics and lecture familiarity on adult cardiac orienting responses and memory. Communication Research, 19, 346–369. Watt, J. H., & Krull, R. (1974). An information theory measure for television programming. Communication Research, 1, 44–68. Watt, J. H., & Welch, A. J. (1983). Effects of static and dynamic complexity on children’s attention and recall of televised instruction. In J. Bryant & D. R. Anderson (Eds.), Children’s understanding of television: Research on attention and comprehension (pp. 69–102). New York: Academic Press. Wiebe, S. A., Sheffield, T., Nelson, J. M., Clark, C. A., Chevalier, N., & Espy, K. A. (2011). The structure of executive function in 3-year-olds. Journal of Experimental Child Psychology, 108, 436–452. Williams, L., Huang, J., & Bargh, J. (2009). The Scaffolded mind: Higher mental processes are grounded in early experience of the physical world. European Journal of Social Psychology, 39, 1257–1267. Willoughby, M. T., Wirth, R., & Blair, C. B. (2011). Contributions of modern measurement theory to measuring executive function in early childhood: An empirical demonstration. Journal of Experimental Child Psychology, 108, 414–435. Willoughby, M. T., Wirth, R., & Blair, C. B. (2012). Executive function in early childhood: Longitudinal measurement invariance and developmental change. Psychological Assessment, 24, 418–431. Wright, J. C., Huston, A. C., Ross, R. P., Calvert, S. L., Rolandelli, D., Weeks, L. A., et al. (1984). Pace and continuity of television programs: Effects on children’s attention and comprehension. Developmental Psychology, 20, 653–666. Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology, 18, 459–482. Zimmerman, F. J., & Christakis, D. A. (2007). Associations between content types of early media exposure and subsequent attentional problems. Pediatrics, 120, 986–992.

CHAPTER EIGHT

Moral Judgments and Emotions in Contexts of Peer Exclusion and Victimization Melanie Killen*,1, Tina Malti†

*Department of Human Development and Quantitative Methodology, University of Maryland, College Park, Maryland, USA † Department of Psychology, University of Toronto, Mississauga, Ontario, Canada 1 Corresponding author: e-mail address: [email protected]

Contents 1. Overview: The Centrality of Morality 2. Intergroup Exclusion and Interpersonal Victimization 3. Moral Judgments and Moral Emotions 4. Social Reasoning Developmental Model 5. Developmental Theories of Social and Group Identity 6. Moral Emotions Clinical-Developmental Theory 7. Interventions for Reducing Prejudice and Victimization 8. Integrating Group-Level and Individual-Level Models 9. Implications and Conclusions Acknowledgments References

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Abstract Morality is at the core of social development. How individuals treat one another, develop a sense of obligation toward others regarding equality and equity, and understand the emotions experienced by victims and victimizers, are essential ingredients for healthy development, and for creating a just and civil society. In this chapter, we review research on two forms of social exclusion, intergroup exclusion and interpersonal victimization, from a moral development perspective, identifying distinctions as well as areas of overlap and intersections. Intergroup exclusion (defined as exclusion based on group membership, such as gender, race, ethnicity, and nationality) is most often analyzed at the group level in contrast to interpersonal victimization (defined as the repeated infliction of physical and psychological harm on another) which is most often analyzed at the individual level. In this chapter, we assert that research needs to examine both group-level and individual-level factors for intergroup and interpersonal exclusion and that moral development provides an important framework for investigating these phenomena.


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Melanie Killen and Tina Malti

1. OVERVIEW: THE CENTRALITY OF MORALITY Morality, defined as the fair and equal treatment of other persons, is implicated in both contexts of intergroup exclusion and interpersonal victimization. Intergroup exclusion, defined as exclusion based on group membership, such as gender, race, ethnicity, religion, nationality, or disability (and other categories), is often, but not always, viewed as a form of prejudice. Most of the research on intergroup exclusion examines the role of group norms, group identity, and various forms of implicit and explicit bias to understand the emergence, maintenance, and perpetuation of prejudicial and discriminatory attitudes. Yet, prejudicial and discriminatory treatment of others also reflects attitudes and behavior that are unfair, and involving unequal treatment of others. Only recently has intergroup exclusion and prejudice been investigated from the moral development viewpoint (see Killen & Rutland, 2011); prejudice involves the violation of moral judgments about prescriptive norms for how to treat others, and how children evaluate prejudice from a moral viewpoint has provided a new window into its origins. Interpersonal victimization, defined as the infliction of harm on others and the disregard of others’ physical and psychological welfare, has been examined in the context of aggression, bullying, and/or violence. Research on interpersonal victimization involves studying the psychological, situational, and biological characteristics that contribute to cycles of aggression and violence. As well, victimization involves the violation of moral norms, although it is rarely studied from a moral development perspective (see Eisner & Malti, 2015). We assert that both forms of exclusion and victimization reflect moral transgressions even though much of the research in these two fields remains focused on only one part of the story: group-level dynamics for intergroup exclusion and individual-level dynamics for interpersonal victimization. The lines of research that best reflect this intersection are those that have used multimethod approaches for analyzing peer rejection, such as social cognition and reasoning about exclusion, group identity, and intergroup attitudes (intergroup social exclusion), along with emotional experiences, friendship relationships among children, individual difference assessments, and potential at-risk factors for psychopathology and maladaptive outcomes (interpersonal victimization). We propose that this approach will help formulate the types of developmental interventions that will work to address social exclusion and victimization.

Peer Exclusion and Victimization

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In this chapter, then, we assert that research from an integrated perspective, one that examines both group-level and individual-level factors for intergroup and interpersonal exclusion, has revealed important findings regarding how moral judgment and moral emotions are integral aspects of these phenomena in childhood and adolescence. We review intergroup social exclusion theory and research, followed by theory and research on interpersonal victimization. Then, we will discuss further overlaps, interactions, and comparisons between these two fields. We describe applications and intervention strategies, followed by our conclusions and future research directions.

2. INTERGROUP EXCLUSION AND INTERPERSONAL VICTIMIZATION Social exclusion is a broad term and we concentrate on two forms, intergroup and interpersonal. Both forms of exclusion have the potential to result in victimization. We view the lack of intersection of research on intergroup social exclusion and interpersonal victimization as a missed opportunity. This is because one form of rejection can often lead to another, and increasing our understanding of these connections is crucial for creating effective prevention and intervention strategies (Malti, Noam, Beelmann, & Sommer, in press (a)). Given that one form of peer rejection can lead to another, it is time to reexamine the underlying assumptions in these two areas of research and to identify the common as well as divergent developmental phenomena associated with intergroup social exclusion and peer victimization. Just as social psychologists studying prejudice have argued that personality trait approaches are not enough to explain prejudice and discrimination in adulthood, developmental and clinical psychologists studying prejudice and discrimination in childhood have made the same argument (Aboud & Levy, 2000; Killen, Rutland, & Ruck, 2011). There are times when children are excluded and victimized for reasons that have nothing to do with their personality traits exclusion stemming solely from biases about group membership, defined as “the outgroup,” such as, categories related to race, ethnicity, religion, disability, or gender (among other group identities). Yet, understanding the individual differences that contribute to peer victimization is important and includes personality factors, such as temperamental differences, which lead children to refrain from social interactions,

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and unable to cope with the complexities of social engagement. Children identified as “bullies” are often rejected by their peers and have trouble reading social cues, attributing self-conscious emotions (e.g., guilt), and demonstrating empathy, as well as complex forms of theory of mind. Bullies seek out as targets children who are shy, fearful, and wary to victimize; potential victims often have social deficits that lead to these forms of vulnerability. Thus, these factors are reflected in the personality characteristics of children at risk for being bullies and victims. Nesdale (2007) has shown that children who are rejected by others are at risk for acting in a prejudicial and biased manner toward others identified as “outgroup members.” Chronic exclusion based on group membership has the potential to lead to maladaptive behavioral outcomes, such as prejudicial orientations toward others. Research on intergroup exclusion has shown that children and adolescents often use moral reasoning to explain what makes intergroup exclusion wrong as well as attribute emotional states to those who are excluded or are excluders (Killen, Mulvey, & Hitti, 2013). Much of the research on intergroup exclusion examines how intergroup dynamics, in the form of ingroup preference and outgroup dislike, perpetuates forms of prejudice in childhood. Further, how children interpret societal-level group norms about prejudice is investigated to understand group dynamics, stereotyping, implicit and explicit bias, and discriminatory acts in childhood and adolescence (Nesdale, Maass, Griffiths, & Durkin, 2003; Verkuyten, 2002). While the bulk of research is focused on group-level factors, research has revealed how moral reasoning and social judgments about groups contributes to an understanding about intergroup exclusion, that is, how it reflects prejudicial behavior and unfair treatment of others by children toward their peers as well as expectations about group identity, group norms, and group functioning (Killen et al., 2013; Rutland, Killen, & Abrams, 2010). Extensive research on interpersonal victimization that focuses on the individual factors that contribute to victimization such as personality traits, aggressiveness, extreme shyness, fearfulness, and a general lack of social skills provides one part, but not the whole story about factors that contribute to developmental psychopathology. Victimization involves the infliction of psychological and/or physical harm on others. Children’s judgments and moral emotions about victimization and bullying reflect age-related changes concerning the attributions of emotions of bullies and victims as well as the judgments about when aggressive actions reflect intentional states (Malti, Gasser, & Buchmann, 2009; Malti & Krettenauer, 2013). In fact, research on moral judgments and emotions in the context of interpersonal


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victimization has reflected several new lines of research (Arsenio, 2014; Keller, Lourenço, Malti, & Saalbach, 2003; Malti & Ongley, 2014).

3. MORAL JUDGMENTS AND MORAL EMOTIONS In developmental psychology, there is a strong tradition for the study of children’s and adolescents’ moral judgments (Killen & Smetana, 2015; Turiel, 2002) and moral emotions (Eisenberg, Spinrad, & Knafo, 2015; Malti & Latzko, 2012). Both are inevitably embedded into, and influenced by, situational factors, including group-level norms, normative group processes, status within the peer group, and social hierarchies. While many of these situational features distinguish contexts of social exclusion from situations involving interpersonal victimization, the boundaries are often fluid, particularly in proximal, real-time processes of peer exclusion and victimization, where peer victimization can easily lead to exclusion as a consequence and vice versa. An emerging literature on the intersection of intergroup exclusion and victimization from an integrative moral developmental and clinical-developmental viewpoint provides a new window into the origins of both phenomena. For example, research on moral development in contexts of intergroup exclusion and inclusion has examined judgments and emotions attributed to excluders or excluded individuals within minority and majority populations. Conceptually, the assumption is that peer groups are likely to influence these judgments and emotions following exclusion in complex ways, especially when children find themselves in the role of the excluder or excluded child. Investigating contexts of intergroup exclusion also elucidate the role of children’s emotions and reasoning in their actual exclusive and inclusive behavior (Hitti, Mulvey, Rutland, Abrams, & Killen, 2013). As such, this line of work provides insight into how children negotiate moral principles of fairness and equality with peer group processes, norms, and functioning. Ultimately, this knowledge can help us understand when intergroup exclusion is viewed as legitimate, how it may manifest in peer interactions, and when peer exclusion is judged as morally wrong and elicits feelings of guilt, remorse, and concern for excluded children. Yet, despite an increasing number of integrative developmental studies on moral judgments and emotions in contexts of peer exclusion, it is still an evolving field. This line of research has examined judgments and/or emotions attributed to victimizers and victimized children across a variety of situational contexts, such as infliction of physical or psychological harm,

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the omission of prosocial duties, or unfair treatment (Arsenio, 2014; Malti & Ongley, 2014). As has been extensively documented, social exclusion and peer victimization are pervasive problems in childhood, leading to negative long-term outcomes. The consequences of social exclusion range from mild anxiety and depressed motivation to achieve to social withdrawal and disengagement. Chronic victimization can lead to a number of more detrimental outcomes, such as persistent psychopathology, low well-being, and low productivity. While the majority of children report experiences of being excluded by their peers at some point during childhood, chronic victimization is more rare, reported by a minority of children, and also more severe. We turn to three sets of models, social reasoning developmental (SRD) model, developmental theories of social and group identity, and moral emotions clinical-developmental theory to report on integrated research on social exclusion and morality.

4. SOCIAL REASONING DEVELOPMENTAL MODEL Social exclusion has been studied from a social reasoning developmental (SRD) model that integrates social domain research (Smetana, 2006; Turiel, 2002) with intergroup attitudes, stemming from social identity theory (SIT; Tajfel & Turner, 1979). The SRD model provides a framework for investigating social and moral judgments and reasoning regarding social exclusion and the origins of prejudice (Killen & Rutland, 2011; Rutland et al., 2010), as well as SIT, and specifically developmental theories about how children form group identity, intergroup attitudes, and beliefs about others. Research based on this model has shown how children use reasoning based on conventions, customs, and traditions to justify the exclusion of others, and how children use reasoning based on fairness, equal treatment, or concern for others to reject forms of social exclusion such as racial and ethnic exclusion. As an example, when asked about exclusion based on stereotypic expectations (e.g., excluding a girl from a baseball club), children at 7, 10, and 13 years of age were likely to reject this form of exclusion and use moral reasons, such as unfairness. When the situation was described as one in which group functioning was threatened, such as including someone who was not talented regarding the goals of the club, however, children condoned exclusion and used group functioning reasons. For example, a 13-year-old participant stated that, “You should pick the boy for the baseball club because he


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will know a lot more about baseball than the girls and be better at it.” In contrast, another 13-year old asserted that, “You should pick the girl because she might be really good at baseball and you should give her a chance; then you’ll have more people to choose from.” Surprisingly, there were few differences based on gender of the participant (i.e., whether the participant was a boy or a girl); instead, participants were more likely to view the exclusion of boys from ballet as more legitimate from exclusion of girls from baseball, supporting findings regarding the asymmetry of gender prejudice. As reviewed by Ruble, Martin, and Berenbaum (2006), stereotypes about cross-gender behavior for boys are more rigid than those for girls. This asymmetry pattern for gender exclusion was also demonstrated in a recent study on the perceived costs for challenging exclusion based on gender stereotypes (Mulvey & Killen, 2014). One implication of this finding is that children who view gender exclusion as legitimate due to conventional or traditional reasons need to understand that there are times when stereotypes contribute to expectations about group functioning. If girls are assumed to be poor at baseball then children and adolescents are more likely to allow exclusion based on conventional reasons. Moreover, children’s use of conventional reasoning (e.g., “It’s okay because the group will be uncomfortable with someone who is different”) is often inconsistently applied across various forms of group identity. For example, conventional reasoning to justify exclusion is more common for gender than for racial exclusion in the case of clubs at school, in which using race as a reason to not allow someone to join a club is viewed negatively (e.g., “It would be unfair to not include him in the group just because of his race;” Killen & Stangor, 2001). In the case of friendships, however, children and adolescents view personal choice as the basis by which one should decide whom to spend time with during and after school. As has been well documented, cross-race friendships decline with age, and this may be due to the fact that, with age, adolescents’ views about both autonomy and group identity increase in salience. Thus, engaging in intimate cross-race relationships, such as dating, is both viewed as a personal choice as well as a violation of conventional expectations. Research reveals that group identity, group conventions, and fairness considerations are involved with group-based and peer-based exclusion by middle childhood. Determining when these forms of exclusion involve unequal treatment often needs to be identified for children and adolescents, especially when many societal messages reinforce the conventions and customs associated with these forms of exclusion.

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Moreover, with age, children recognize that group preferences are different from individual preferences and that the favorability of a group toward an ingroup member who violates the expectations of the group may result in exclusion by the group members. Thus, even when a child views social exclusion as unfair, they may expect that the group will exclude a deviating member to preserve the identity of the group; with age, children recognize that there often exists a cost to challenging the group (Mulvey, Hitti, Rutland, Abrams, & Killen, 2014). As described by developmental social identity theories (Abrams & Rutland, 2008), intergroup social exclusion creates specific group-level norms that serve to exclude others and enhance the ingroup identity. These groups can be organized along any type of criteria, distinguishing an ingroup from an outgroup to enhance self-esteem. At the same time, children also rely on societal expectations about groups to create ingroups and outgroups, such as gender, race, ethnicity, nationality, and other categories. These forms of group identification increase with age as children are exposed to a wider range of group biases and stereotypes that permeate most cultures. Determining high and low status for the societally derived group identities is often determined by the larger societal level. Peer groups, however, also form their own sources of stigma, such as those that exist in adolescence that are created by one group to exclude another group (such as gangs). As has been documented, social hierarchies exist regarding high- and low-status individuals in both forms of peer exclusion, intergroup and victimization. We turn to developmental theories of social and group identity, which has been informative about how social hierarchies are embedded in children’s social interactions and judgments.

5. DEVELOPMENTAL THEORIES OF SOCIAL AND GROUP IDENTITY According to SIT, individuals are motivated to make favorable evaluations based on ingroup membership, and are thus more susceptible to expressing outgroup biases (Tajfel & Turner, 1979). SIT was not originally formulated as a developmental model, and a group of SIT trained researchers formulated developmental social identity theories to chart age-related changes in childhood through adolescence (Abrams & Rutland, 2008; Nesdale, 2008; Verkuyten, 2007). Nesdale (2004) identified social identity development theory which focuses on the role that context and motivation play in eliciting a particular social identity that leads individuals to favor their


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ingroup and dislike the outgroup (or both). His model suggests that prejudice depends on how much children identify with their social group, whether the group holds a norm that reflects a prejudicial attitude, and whether the ingroup believes that the outgroup is a threat to their identity. Nesdale (2004) has shown that an awareness of group identity emerges prior to group preference and forms of group prejudice. As children get older, they bolster their sense of social identity by excluding outgroup others from their social ingroup (Nesdale, 2004; Verkuyten & Steenhuis, 2005). An important point demonstrated by Nesdale (2004) is that children do not automatically dislike peers from outgroups. Outgroup dislike is a result of contextual conditions being present that create outgroup threat and bias. These conditions include when: (a) children identify with their social group, (b) prejudice is a norm held by the members of the child’s group, and (c) the ingroup members believe that their group is threatened in some way by the members of the outgroup (Nesdale, 2007). Further, Nesdale’s research has shown that children pay attention to different levels of norms, distinguishing peer-based from school-based norms about bullying and aggression (Nesdale & Lawson, 2011). Abrams and Rutland’s (2008) developmental subjective group dynamics model focuses on children’s social-cognitive competencies that play a role in their age-related understanding of groups and group dynamics. Research from this model has shown that, with age, children focus on group norms to define their group identity more than group membership. This means that group identity is not just whether someone is of the same gender, ethnicity, or race, but whether they share the same values and norms. One way to test this form of competence is to determine how children evaluate social inclusion and exclusion. Groups share membership, but they also share norms and values. When a member of the ingroup deviates from the norms of the group, do children view this as a form of disloyalty? If so, are they willing to exclude someone who deviates from the group? Abrams and Rutland (2008) tested this expectation by asking children whether they differentially evaluated a normative member (someone who espouses a group’s norm) and a deviant member (someone who rejects the group’s norm). Then, they asked children whom they thought the group would prefer to have in their group, a deviant ingroup member (someone who challenged the group norm but shared membership) or an outgroup member who supported the ingroup norm. The example they used in one of their first studies was about norms related to nationality, whether children would expect a group to prefer having an English child in a soccer club

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who rooted for the German team (deviant ingroup) or a German child who rooted for the English team (outgroup member supporting the ingroup norm). The findings revealed that, with age, children expected that groups would give priority to norms over membership (Abrams & Rutland, 2008; Abrams, Rutland, Pelletier, & Ferrell, 2009). One question that arose regarding this set of studies had to do with the type of norm held by a group. Do children treat all norms the same? Social domain theory has demonstrated that children treat moral norms different from conventional (societal) ones (Smetana, 2006; Turiel, 2002). In a series of collaborative studies, Killen and her colleagues (Killen et al., 2013) found that children had different ideas about whether it is legitimate to deviate from a group when the norm was about equality than when the norm was about modes of dress or conventions. Research by Abrams and Rutland (2008) has revealed the social-cognitive developmental changes regarding how children understand group dynamics, particularly the factors that contribute to understanding when groups are favorable or unfavorable toward ingroup members who deviate from group norms, and the contexts that enable children to expect groups to like outgroup members. Abrams and Rutland (2008) refer to social-cognitive changes as children’s acquisition of “group nous,” which is an understanding of the group dynamics associated with social interactions. Group nous refers to children’s knowledge about groups, and specifically when it is that children realize that their own view of what their group thinks is desirable may be different from their own (individual) view about it. Verkuyten and his colleagues (Verkuyten & Thijs, 2001) extended social identity to ethnic relationships and ethnic victimization by conducting investigations to understand whether multicultural education in The Netherlands has been effective for reducing prejudice. They found that the more the majority Dutch adolescents positively evaluated multiculturalism, the likelier they were to view the outgroup positively. Conversely, strong endorsement from the minority groups was related to positive ingroup feelings. One of the inferences from his research is that the impact of multicultural education differs for majority and minority groups. In fact, the way that multiculturalism is taught it is targeted more for minority groups, in celebrating their identity, than for the majority groups, who tend to support assimilation, which is contrary, in some respect, to integration (assimilation focuses on subsuming one’s minority identity to take on the identity of the majority group). More recently, Thijs and Verkuyten (2012) found that the Turkish and Moroccan-Dutch preadolescents who


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had better relationships with their native Dutch teachers had more positive attitudes toward the Dutch outgroup, especially in segregated classrooms. The closeness of the relationship (positive aspects) was more important than the conflicts (negative aspects) that existed for how they viewed their majority ethnic peers. These findings show, again, that context and social relationships make a difference regarding children’s ingroup preference and ingroup bias. The SRD model draws on these developmental theories of SIT by investigating the context of group norms and how children conceptualize these norms. Moreover, developmental theories of SIT have provided a set of issues to investigate concerning intergroup attitudes using social domain categories. For example, subjective group dynamics research has shown that by 6–8 years of age, children develop a dynamic relationship between their judgments about peers within groups and about groups as a whole (i.e., intergroup attitudes; Abrams & Rutland, 2008). Changes in children’s social cognition means they can often both exclude a peer because they are from a different social group (i.e., intergroup bias) and exclude a peer from within their group who deviates from the group’s social-conventional norms (i.e., intragroup bias), such as by showing increased liking or support of an outgroup member. An SRD perspective involves examining the social domain of the group norm (e.g., is it about fairness or conventions?), the status of group membership (e.g., are the groups of equal or unequal status?), and the reasoning by the individual evaluating group dynamics (e.g., is favorability of the group based on moral, conventional, or psychological considerations?). As one example, when groups have norms that violate moral principles of equality, children are favorable to outgroup members who support equality (Killen et al., 2013). Children use moral reasoning about fairness to explain why they dislike the disloyal ingroup member. Yet, with age, children also recognize the cost of challenging the group and that this will often result in exclusion from the group. This becomes particularly salient in late childhood when group identity is enhanced. Children will often express reluctance to reject a group norm even when it is based on inequality. Understanding group norms and group identity is essential for judging groups that have antisocial norms and for recognizing when these norms should be challenged or changed. Moreover, the SRD model makes a fundamental difference between excluding someone based on ingroup preference and on the basis of individual traits (e.g., rejecting someone due to individual abilities). The former behavior is connected to group identity, which is part of social development

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(belonging to groups); the latter behavior is connected to personality traits, in some cases, or personality characteristics that deviate markedly from societal expectations and conventions (i.e., excluding someone who is extremely shy or overly aggressive). Children who are treated differentially due to their group membership (e.g., race, gender, religion) face different consequences from those children who are treated differentially due to their social deficits, which, in extreme cases, may be reflective of developmental psychopathology and maladaptive functioning (Rubin, Bukowski, & Parker, 2006). As mentioned earlier, intergroup social exclusion often serves as a source of psychological stress for many children which, when experienced extensively, leads to anxiety, depression, and social withdrawal (Rubin et al., 2006). Developmental literature on peer rejection in childhood (e.g., bullying and victimization) has often suggested that victims of exclusion invite rejection by their peers because of specific individual traits, such as shyness or aggressiveness (Rubin et al., 2006). While assessing individual characteristics is important, stereotypic information related to the victim’s social group membership that excluders may attribute to an individual has to be understood as well, given that this source of exclusion does not stem from the excluded individual but from the excluder (Killen et al., 2013).

6. MORAL EMOTIONS CLINICAL-DEVELOPMENTAL THEORY As mentioned, interpersonal victimization is different from intergroup exclusion. Interpersonal victimization has been studied from the perspective of clinical-developmental theory. Most recently, victimization has been studied from moral emotions clinical-developmental theory that integrates affect-event and affect-cognition models. One goal of this theory has been to explain why children behave aggressively and victimize others, while others refrain from aggression and bullying behavior in peer groups (Malti, 2014; Malti & Ongley, 2014). A basic premise of this theory is that social and moral emotions, such as guilt, empathy, or respect, serve important motivational functions to resolve interpersonal conflict and to understand children’s aggression, bullying behavior, and victimization. Because emotions in social and moral situations highlight the negative consequences of acts of victimization and bullying for self and others, they provide insight into children’s motivation to engage in, or refrain from, aggression. An interesting and unanswered question, to be described in more detail later, is whether these


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emotions are related to children’s motivation to engage in intergroup exclusion such as prejudice and bias. A study by Sierksma, Thijs, Verkuyten and Komter (2014) is one of the first to examine this relation. However, we first need to examine what is known about individual motivation based on moral emotions. Developmental researchers have pointed to the relevance of emotions such as guilt and sympathy for understanding the genesis of interpersonal aggression and victimization. Self-conscious moral emotions (e.g., guilt), and other-oriented moral emotions (e.g., sympathy and respect), are conceptually linked to aggression, violence, and antisocial conduct. These emotions can help children and adolescents link emotional consequences that others face to specific events (e.g., anticipating feeling guilty about hitting another child because he/she will feel sad), as well as to the severity of these events (e.g., hitting another child may have more serious physical and psychological consequences for the child than not helping a child finish his/her homework) (Arsenio, 2014; Arsenio & Lemerise, 2004; Malti, 2014). Developmental research has identified event-related differences in anticipated emotions to self and others. For example, the anticipation of guilt feelings and related emotions differs across domains of social knowledge (Menesini & Camodeca, 2008; Smetana, 2006). This research is essential in understanding the normative development of moral emotions from early childhood to adolescence because it points to situational influences on development, as well as links to experiences of aggression, bullying, and victimization. The anticipation of moral emotions such as guilt and sympathy also involves coordination of affective experiences with judgments, decisionmaking, and an understanding of others’ intentions (Malti & Ongley, 2014; see Lagattuta, 2014). With age, children develop social-cognitive skills, which help them coordinate their affective responses with their judgments of, and reasoning about, moral events. For example, the anticipation of complex moral emotions, such as guilt, indicates that children can coordinate their judgments of the wrongfulness of the act (e.g., it is not right to hit others) with other-oriented concern (e.g., it hurts), which may produce empathy-induced guilt as a consequential affective state. According to this integrative clinical-developmental model of moral emotions, both specific types of events as well as links between cognition and affect account for differences in the anticipation of moral and social emotions. This, in turn, has important implications for children’s engagement in aggression and victimization. In line with this theorizing, an absence of the self-evaluative emotion of guilt following one’s own wrongdoing has

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been associated with increased levels of aggression and bullying in community-based and clinical samples ranging from early childhood to early adulthood (Eisner & Malti, 2015; Malti & Krettenauer, 2013). Similarly, low levels of other-oriented concern and sympathy have been shown to be positively related to aggression and bullying (van Noorden, Haselager, Cillessen, & Bukowski, 2014). Thus far, links between bullying and victimization, and affective experiences associated with these events, have been mostly studied in contexts of straightforward moral transgressions, such as the infliction of harm on another person and stealing desired resources. For example, much of developmental research on links between aggression and guilt has been conducted in the happy victimizer paradigm. In this paradigm, children are presented with hypothetical moral transgressions, such as stealing another child’s chocolate. After presentation of the transgression, children are typically asked to anticipate the emotions in the role of the victimizer. One major finding of research using this paradigm is that younger children (i.e., 3- to 4-year-olds) tend to attribute happy emotions to the self in the role of the victimizer because they focus on the short-term gains associated with the transgression (i.e., eating chocolate). In contrast, the majority of older children (i.e., 7- to 8-year-olds and older) tend to attribute sad emotions to the self in the role of the victimizer (e.g., guilt, sadness, or shame) because they understand the negative long-term consequences of the transgression for the self as victimizer (e.g., guilt), the other, victimized child (e.g., sadness), and the relationship between victimizer and victim (e.g., conflict). Despite developmental change in anticipated moral emotions, metaanalytic evidence indicates that the absence of negative emotion attributions following one’s own transgressions is associated with aggression and bullying, independent of age (Malti & Krettenauer, 2013). Another approach to the study of judgments and emotions in contexts of victimization has been to use narratives of the child’s own moral and social experiences (e.g., Gutzwiller-Helfenfinger, Gasser, & Malti, 2010; Wainryb, Brehl, & Matwin, 2005). Because narratives represent contextualized social interactions, it is likely that moral emotions and moral reasoning are different for narratives of real-life situations and for hypothetical scenarios (Malti & Ongley, 2014). Moral emotions clinical-developmental theory has offered a conceptual framework from which to systematically study affective moral development in relation to bullying and victimization. The model integrates across past traditions that have focused on the development of moral emotions, as well


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as research that has studied interpersonal experiences of bullying and victimization in the context of peer group interactions. This latter literature typically utilizes sociometric status as an indicator of being liked or disliked and/or of being popular or unpopular (i.e., peer acceptance and social status). In the sociometric literature, children who are identified as involved in bullying behavior and children who are being victimized tend to differ in terms of social status and dominance (e.g., Olthof, Goossens, Vermande, Aleva, & van der Meulen, 2011). Specifically, if social status is defined as power, victimizers (i.e., bullies) tend to score higher than children who are being victimized. Bullies are often highly visible in the peer group and can be seen as popular. Yet this high status comes with high costs because these children also tend to be disliked (Cillessen & Rose, 2005). Importantly, this indicates that high status that is solely based on power and dominance has its limitations when it comes to interpersonal functioning, for instance establishing and maintaining friendship and mutual respect among peers (see Berndt, 2004). Thus, emotions and judgments about bullying and victimization are embedded in peer group dynamics, and peer acceptance and social status influence how children feel and think about bullying and victimization. This has considerable implications for social development and mental health outcomes. For example, children with severe levels of aggression may become disliked and, as a consequence, rejected by their peers. They may also face a lack of support from friends, and/or may be excluded from the peer group. Thus, status and hierarchies in peer groups affect children’s anticipation of emotions and judgments about victimization and exclusion in various ways. Our chapter outlines integrative approaches to account for the role of social status and hierarchies on judgments and emotions about victimization and exclusion. The anticipation of social and moral emotions can also highlight the affective consequences of social exclusion and inclusion. Research examining the emotions attributed to excluders or excluded individuals in addition to emotion attributions within minority and majority populations reveals more information about the dynamics of exclusion. Because contexts of social exclusion are multifaceted, typically involving both moral concerns and considerations about peer group functioning, peer group norms, and group identity, children and adolescents are expected to anticipate a wider range of emotions in these contexts (e.g., sadness, guilt, and shame, as well as pride, happiness, and mixed emotions). As peer group norms become particularly important during adolescence (Abrams et al., 2009), the

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anticipation of moral emotions may progress in a less linear fashion from early childhood to late adolescence than in straightforward moral contexts. For example, it is likely that adolescents attribute pride to excluders because it serves to maintain peer group functioning and enhance ingroup identity, which is much less likely going to play a role in contexts of straightforward moral transgressions (e.g., harming others psychologically or physically). Taken together, these first studies on the intersection are promising and reveal when children may condemn exclusion based on individual characteristics that can be associated with victimization. In summary, moral emotions theory posits that emotions in moral contexts provide new insights on intergroup attitudes and reveal important information on the motivations that are associated with decision-making, attitudes, and (mal)adaptive behaviors. For example, feelings of guilt and sadness help children view bullying, victimization, and intergroup bias as unfair and anticipate negative emotions to the self and others with these events. On the microlevel, linking proximal real-time processes of victimization and exclusion with emotional responses can facilitate further understanding of affect-event links and how they affect children’s and adolescents’ intergroup attitudes and experiences of victimization and exclusion.

7. INTERVENTIONS FOR REDUCING PREJUDICE AND VICTIMIZATION Given the negative immediate and long-term effects of peer exclusion and victimization on children’s well-being, health, and social development, interventions for reducing experiences of peer exclusion and victimization are essential. Yet, interventions designed to ameliorate intergroup social exclusion and interpersonal victimization are quite different, focusing on prejudice reduction for intergroup social exclusion on the one hand, and social skills training for decreasing interpersonal victimization on the other hand. Social skills training for decreasing interpersonal victimization is most often focused on the individual traits of a victim or bully that need to be changed to prevent the cycle of abuse. In contrast, reducing prejudice that results from intergroup exclusion requires changing attitudes of the group, often the group with high status, reflecting the majority. When one form of exclusion reflects both intergroup attitudes and lack of social competence, however, the form of intervention may need to be multimethod, that is, focused on both group-level and individual-level strategies.


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One of the most significant factors in reducing prejudice is intergroup contact, a group-level form of intervention. Intergroup contact alone, however, does not necessarily reduce prejudice or improve intergroup relationships. The optimal conditions that must be met for contact with members of outgroups to reduce prejudice include equal status, common goals, authority sanctions (supporting mutual respect), and cross-group friendships (such as cross-race friendships; Tajfel & Turner, 1979). Meta-analyses by Tropp and Prenovost (2008) with children, adolescents, and adults reveal that crossgroup friendships is the most significant predictor for prejudice reduction among majority or high-status groups (Pettigrew & Tropp, 2006; Tropp & Prenovost, 2008). The interpretation is that being friends with someone from an “outgroup” helps children to challenge stereotypes that they encounter in the culture (e.g., “My friend is not like that”). Moreover, the affiliation and friendship create positive bonds that lead to a new common ingroup identity (e.g., “We both like music”). Research has shown that intergroup contact in the form of cross-group friendships increases the use of moral reasoning to reject racial exclusion (Crystal, Killen, & Ruck, 2008) and reduces the use of stereotypes to justify exclusion (Killen, Kelly, Richardson, Crystal, & Ruck, 2010). Moreover, longitudinal studies with Turkish and German children have shown that cross-group friendships are related to an increase in positive ethnic attitudes toward the outgroup by the majority (German) group (Feddes, Noack, & Rutland, 2009; Jugert, Noack, & Rutland, 2011). Recent debates have arisen regarding the effectiveness of intergroup contact for minority or low-status individuals (Dixon, Durrheim, & Tredoux, 2005). While intergroup contact enables high-status group members to affiliate with low-status members, it does not necessarily empower or engage low-status members to improve their social status. From a developmental science perspective, however, it has been argued that cross-group friendships in childhood may be even more powerful than in adulthood, because these experiences have the potential to inhibit the acquisition of stereotypes for both majority and minority participants. Direct and indirect forms of contact have been shown to be effective in reducing prejudice and bias. While direct contact (friendships) is most effective, indirect contact in the form of reading stories about interracial or intergroup peers (Cameron & Rutland, 2006) serve as explicit parental messages to support the goals of respect and inclusiveness, and the teaching of the historical context for how and why a group comes to be associated with low status (through maintaining hierarchical status and economic viability)

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reduces discriminatory attitudes (Aboud & Doyle, 1996; Hughes, Bigler, & Levy, 2007). Moreover, studies in which children have been organized into new groups identified by an overarching identity (common ingroup identity) have been shown to be effective (Gaertner & Dovidio, 2005). In contrast, interventions for reducing chronic victimization and bullying are typically either targeted with a focus on at-risk and/or high-risk populations and emphasize the promotion of social skills, and/or they implement a whole-school approach to prevent and reduce bullying and victimization in school contexts (Strohmeier & Noam, 2012). Intervention research indicates that effective programs often utilize both prevention and intervention strategies. For example, bullying and victimization prevention and intervention programs often target bullies, victimizers, and bystanders at the general level, which includes children designated as “average” in terms of friendships but who are vulnerable. This is done because of the recognition that bullying is a peer group phenomenon and that silent bystanders perpetuate bullying behavior (Salmivalli, 2010; see Olweus, 1993). Effective bullying intervention therefore requires not only immediate interventions by peers or teachers and/or social skills training with individual children, but also prevention and intervention strategies at the classroom and school level, such as changes in school climate and the promotion of a safe school environment.

8. INTEGRATING GROUP-LEVEL AND INDIVIDUAL-LEVEL MODELS In complex social situations, the boundaries between peer exclusion and victimization often overlap. For example, even chronic victimization that involves one individual child as a target often involves various group processes and norms at the level of the classroom, grade, and/or school (e.g., bias, prejudice). This speaks to the need for a three-tiered framework that addresses the necessity to change norms on the large scale (i.e., a wholeschool approach), as well as targeted strategies to reduce victimization and incidences of bullying among children. Research supports the notion that chronic victimization and serious bullying need more intense, targeted treatment, often involving multiple referrals and multidisciplinary services. In order to effectively reduce social exclusion and interpersonal victimization in school contexts, a combined intervention approach seems warranted. Such an intervention approach should address norms to help reduce


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stereotypes and bias and to promote principles of fairness, inclusion, and respect on a large scale, and include “best practices” or evidence-based intervention techniques to reduce bullying and victimization and improve mental health. A few recent studies have examined children’s knowledge about social exclusion based on behavior or personality characteristics (Ojala & Nesdale, 2004). What these studies have in common is that they examine exclusion based on individual characteristics, such as personality or behavioral characteristics that are associated with victimization and bullying. For example, Malti and colleagues (2012) examined 12- and 15-year-old Swiss and non-Swiss adolescents’ judgments and emotion attributions about social exclusion and how these vary when exclusion is based on different characteristics of the excluded individual, including nationality, gender, and personality (i.e., shyness; Malti, Killen, & Gasser, 2012). Adolescents judged exclusion based on nationality as less acceptable than exclusion based on personality. Non-Swiss adolescents, who reflected newly immigrated children to Switzerland, viewed exclusion based on nationality as more wrong than did Swiss nationals, and attributed more positive emotions to the excluder than did Swiss children. These findings revealed the interrelationships of moral judgments and emotion attributions, as well as the distinction children made between intergroup and interpersonal exclusion. In a series of studies, Gasser and his colleagues (2014) studied judgments and emotion attributions about the exclusion of disabled children (Gasser, Malti, & Buholzer, 2013, 2014). Based on a sample of 442 children from Switzerland, the researchers studied how 6-, 9-, and 12-year-old children judge and feel about exclusion based on disabilities (Gasser et al., 2014). Overall, the majority of children judged as morally wrong to exclude children with mental or physical disabilities. Yet, participants were less likely to expect the inclusion of children with mental or physical disabilities in academic and athletic contexts compared to social contexts. As shown in Figure 1A and B, 6-year-old children did not coordinate situational context with disability type when making decisions about inclusion and exclusion of children with physical disability and attributing emotions to excluders. In contrast, 9- and 12-year-olds differentiated athletic from social contexts when making decisions about exclusion and anticipating moral emotions when excluding children with physical disabilities. With age, children were less likely to expect the inclusion of children with physical disabilities in athletic contexts, and they attributed less moral emotions to excluders in athletic than social contexts for situations describing children with physical


100

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Social context

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0 6 years

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Figure 1 (A) Expected decision about inclusion of children with physical disabilities by age group and situational context (i.e., social vs. athletic). (B) Expected moral emotions to excluders of children with physical disabilities by age group and situational context (i.e., social vs. athletic). (A) Reprinted data from Gasser et al. (2014). (B) Reprinted data from Gasser et al. (2014).

disabilities. These findings resonate with studies on social exclusion based on race and ethnicity. They indicate that children sometimes judge it as right to exclude children with certain individual characteristics in relevant contexts because they balance group norms with moral considerations when evaluating exclusion. Emotion attributions to excluders may reveal underlying biases because these emotions reflect the anticipated ambivalence in contexts in which peer group norms and moral norms collide. Importantly, these biases do not seem to decrease but rather increase with age, suggesting that group considerations become increasingly important in middle and late childhood. Interestingly, children with high levels of sympathy toward children with disabilities were more likely to report frequent contact with children with disabilities (Gasser et al., 2013). This finding shows that the anticipation of other-oriented emotions to outgroup peers (e.g., sympathy, respect) may support intergroup relationships and decrease bias (Malti et al., in press (b)). Recently, Sierksma and colleagues (2014) examined children’s intergroup helping intentions, which is the positive side of intergroup relationships. Based on a large sample of children, findings revealed that in low need situations and when helping behavior was public, children intended to help outgroup peers more than ingroup peers. When the need was relatively high, children’s empathy concerns outweighed children’s group norm considerations. This study reveals one way in which moral emotions, such as empathy, provide motivation for intergroup helping behavior, a connection not previously made in the literature. Future research may help to clarify if

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Group inclusion judgment

and when judgments of exclusion based on individual characteristics (e.g., mental disability) are associated with interpersonal rejection and victimization as well the role emotions play, such as empathy, in reducing ingroup preference and bias. In another set of studies, Hitti and her colleagues (Hitti & Killen, 2014; Hitti, Malti, & Killen, 2014) investigated three factors, group norms, individual characteristics, and stereotypes that contributed to intergroup exclusion based on ethnic membership. Specifically, non-Arab American adolescents evaluated inclusive decisions by their own group or the “outgroup” to invite a member to join who was the same ethnic group but had different interests from the group (e.g., music and sports) or the “other” ethnic group with the same interests. The goal was to determine whether participants gave priority to ethnicity, a group-level factor, or shared (or lack of ) interests, which was an individual-level factor. There were two conditions, group norms that were inclusive (“We like others who are different from us”) and exclusive (“We like others who are similar to us”). As shown in Figure 2, the findings indicated that non-Arab Americans expected their own group to be inclusive and invite Arab-American peers to join them. However, non-Arab Americans expected Arab groups to be Arab American group

Non-Arab American group

6 5 4

4.32b

4.07e

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Figure 2 TBA. Group inclusion judgments for both targets by ethnic group identity and group norm. Note: Inclusion judgments were made on a Likert-type scale, 1, really not likely; to 6, really likely. Error bars represent standard deviations. an.s. compared with 3.5 midpoint inclusion judgment; bt(99) ¼ 5.47, p < 0.001, Cohen's d ¼ 0.55; ct(97) ¼ 4.19, p < 0.001, Cohen's d ¼ 0.42; dn.s. compared with 3.5 midpoint inclusion judgment; e t(97) ¼ 3.60, p < 0.001, Cohen's d ¼ 0.36; ft(98) ¼ 5.57, p < 0.001, Cohen's d ¼ 0.56; g t(99) ¼ 9.20, p < 0.001, Cohen's d ¼ 0.92; ht(97) ¼ 11.31, p < 0.001, Cohen's d ¼ 1.00. Reprinted from Hitti and Killen (2014).

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exclusive, preferring only to be with other Arab Americans. This type of asymmetry in group-level expectations can perpetuate ethnic segregation, unfortunately. This is because when children and adolescents expect members of an “outgroup” to be exclusive they are less likely to initiate integrated social encounters with the anticipation of rejection. This outcome is even more likely when the majority “high status” group holds an expectation that the minority “low status” group will be exclusive. Moreover, non-Arab Americans who reported stereotypes expected their ingroup to be less inclusive, and age-based exclusion increased with age. The relationship of stereotypic attributions to exclusive behavior reflects another factor contributing to segregated interactions in early development. In a follow-up study with the same design, results on emotion attributions indicated that with age, adolescents attributed more positive emotions, more apathy and less sadness to ethnic outgroups in the context of intergroup exclusion than did younger adolescents, suggesting that emotion attributions provide another window into understanding the dynamics of social exclusion (Hitti et al., 2014). In summary, multiple concerns are clearly involved in both contexts of peer exclusion and victimization. Both contexts concern others’ welfare, fair treatment of others, and care, and both require children and adolescents to distinguish, reflect upon, and balance group functioning, moral norms, and self-oriented interests. In children’s everyday interactions with peers, the boundaries of these contexts often overlap, thus emphasizing the need to understand the complex interplay between moral concerns, individual desires and needs, and group processes more completely.

9. IMPLICATIONS AND CONCLUSIONS In this chapter, we aimed to deepen our understanding of social exclusion and victimization by discussing individual-group relationships and the role of social hierarchies, context, and attributions of emotions and intentions of others in social exclusion and victimization. We reviewed both theoretical accounts and lines of research on exclusion and victimization, as well as research at the intersection of these considerations, as this integrative research will be particularly useful for identifying best practices and intervention strategies to address exclusion and victimization. More recent research at the intersection of these two lines of work is particularly promising, and future research that systematically investigates similarities and differences in


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children’s reasoning about, and emotions associated with, experiences of social exclusion and victimization will help refine and contribute to this integrative approach. It is clear that the boundaries between experiences of exclusion and bullying are difficult to disentangle. For example, if a child bullies others in an extreme way, it is likely that this child is being rejected and excluded from the peer group at some point. Children who are being excluded because of their ethnic group membership might become increasingly angry or disengaged over time, which may lead to increasing intergroup tension and/or bullying incidents. Therefore, combining these two lines of research will contribute to the question when the boundaries between exclusion and victimization become difficult to differentiate, how children and adolescents think and feel about exclusion and victimization based on individual characteristics (e.g., shyness), and if and how combined experiences of exclusion and victimization have negative cumulative effects on children’s development and long-term health outcomes. Longitudinal approaches appear particularly useful since they can address questions of when and how exclusion and victimization overlap over the developmental course, how hierarchies and status differences change over time and affect role changes (e.g., from being excluded to being included), and how changes in group processes and individual development contribute to exclusion and victimization. Given that actual bullying or exclusion stories are often complex, it will also be important in future research to carefully assess and identify the excluder or excluded, and/or the victim or victimizer. Ultimately, this work can also contribute to the question of whether psychological interventions against bullying in childhood and adolescence become more effective if social exclusion at large is addressed, and why. It is important to emphasize that the relations between experiences of exclusion and victimization are intertwined, as they involve societal structures that can contribute to contradictions, ambivalence, and conflict. This is because incidents of exclusion and victimization reflect, in part, social hierarchies and status differences among individuals. These differences can be subtle at the surface, but tend to have their roots in the different environmental conditions in which children grow up, and, at a larger scale, in social inequalities. With respect to experiences of peer exclusion, hierarchies may be entrenched in stigma that stems from societal markers (e.g., race, ethnicity, gender), unequal opportunities, economic inequalities, and/or cultural boundaries. For experiences of victimization, status differences may emerge

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because of power imbalances between the bully and the victim, which inherently affect dynamics of social interaction and how bullies treat potential victims and observing, third-party peers. Facilitating the development of these principles in childhood and adolescence is important beyond the absence of extreme bullying and victimization. Morality in the form of promoting equality, mutual respect, and fairness creates healthy societies. Cultures that are solely based on power-induced status differences and hierarchies are ultimately limited and contradict humans’ basic needs for freedom, mutual respect, and for reaching one’s potential (Appiah, 2005; Nussbaum, 1999; Sen, 2009). Extreme cases of social exclusion and victimization of children creates the conditions for inequality and inequity throughout development, contributing to discontent and turmoil among social relationships in adulthood (Abrams & Killen, 2014). Thus, integrating theoretical and empirical approaches to the study of peer exclusion and victimization has great potential to advance our understanding of what, when, and why these experiences matter for maladaptive and adaptive outcomes, and how we can best intervene to reduce their occurrence and potential long-term negative impact.

ACKNOWLEDGMENTS We thank Michael T. Rizzo, University of Maryland, and Na Young Bae, University of Toronto, for their editorial assistance with the manuscript. The first author was funded, in part, by a Tier I Seed Grant from the Vice President for Research at the University of Maryland. The second author was funded, in part, by the Social Sciences and Humanities Research Council of Canada (SSHRC).

REFERENCES Aboud, F. E., & Doyle, A. B. (1996). Parental and peer influences on children’s racial attitudes. International Journal of Intercultural Relations, 20, 371–383. http://dx.doi.org/ 10.1016/0147-1767(96)00024-7f. Aboud, F. E., & Levy, S. R. (2000). Interventions to reduce prejudice and discrimination in children and adolescents. In S. Oskamp (Ed.), Reducing prejudice and discrimination (pp. 269–293). Mahwah, NJ: Lawrence Erlbaum Associates. Abrams, D., & Killen, M (Eds.). (2014). Social exclusion in childhood: Developmental origins of prejudice. Journal of Social Issues, 70, 1–11. http://dx.doi.org/10.1111/josi.12043. Abrams, D., & Rutland, A. (2008). The development of subjective group dynamics. In S. R. Levy & M. Killen (Eds.), Intergroup attitudes and relations in childhood through adulthood (pp. 47–65). New York, NY: Oxford University Press. Abrams, D., Rutland, A., Pelletier, J., & Ferrell, J. (2009). Children’s group nous: Understanding and applying peer exclusion within and between groups. Child Development, 80, 224–243. http://dx.doi.org/10.1111/j.1467-8624.2008.01256.x. Appiah, K. A. (2005). The ethics of identity. Princeton, NJ: Princeton University Press.


273

Arsenio, W. F. (2014). Moral emotion attributions and aggression. In M. Killen & J. G. Smetana (Eds.), Handbook of moral development (2nd ed., pp. 235–255). New York, NY: Psychology Press. Arsenio, W. F., & Lemerise, E. A. (2004). Aggression and moral development: Integrating the social information processing and moral domain models. Child Development, 75, 987–1002. http://dx.doi.org/10.1111/j.1467-8624.2004.00720.x. Berndt, T. J. (2004). Children’s friendships: Shifts over a half-century in perspectives on their development and their effects. Merrill Palmer Quarterly, 50, 206–222. http://dx.doi.org/ 10.1353/mpq.2004.0014. Cameron, L., & Rutland, A. (2006). Extended contact through story reading in school: Reducing children’s prejudice towards the disabled. Journal of Social Issues, 62, 469–488. http://dx.doi.org/10.1111/j.1540-4560.2006.00469.x. Cillessen, A. H. N., & Rose, A. J. (2005). Understanding popularity in the peer system. Current Directions in Psychological Science, 14, 102–105. http://dx.doi.org/10.1111/j.09637214.2005.00343.x. Crystal, D., Killen, M., & Ruck, M. D. (2008). It is who you know that counts: Intergroup contact and judgments about race-based exclusion. British Journal of Developmental Psychology, 26, 51–70. http://dx.doi.org/10.1348/026151007X198910. Dixon, J. A., Durrheim, K., & Tredoux, C. G. (2005). Beyond the optimal contact strategy. American Psychologist, 60, 697–711. http://dx.doi.org/10.1037/0003-066X. 60.7.697. Eisner, M. P., & Malti, T. (2015). Aggressive and violent behavior. In M. E. Lamb (Vol. Ed.) & R. M. Lerner (Series Ed.), Handbook of child psychology and developmental science, Vol. 3: Social, emotional and personality development (7th ed., pp. 795-884). New York, NY: Wiley. Eisenberg, N., Spinrad, T. L., & Knafo, A. (2015). Prosocial development. In M. E. Lamb (Vol. Ed.) & R. M. Lerner (Series Ed.), Handbook of child psychology and developmental science, Vol. 3: Social, emotional and personality development (7th ed., pp. 795-884.). New York, NY: Wiley. Feddes, A. R., Noack, P., & Rutland, A. (2009). Direct and extended friendship effects on minority and majority children’s interethnic attitudes: A longitudinal study. Child Development, 80, 377–390. http://dx.doi.org/10.1111/j.1467-8624.2009.01266.x. Gaertner, S. L., & Dovidio, J. F. (2005). Categorization, recategorization and intergroup bias. In J. F. Dovidio, P. Glick, & L. Rudman (Eds.), Reflecting on the nature of prejudice: Fifty years after Allport (pp. 71–88). Malden, MA: Blackwell. Gasser, L., Malti, T., & Buholzer, A. (2013). Children’s moral judgments and moral emotions following exclusion of children with disabilities: Relations with inclusive education, age, and contact intensity. Research in Developmental Disabilities, 34, 948–958. http://dx.doi. org/10.1016/j.ridd.2012.11.017. Gasser, L., Malti, T., & Buholzer, A. (2014). Swiss children’s moral and psychological judgments about inclusion and exclusion of children with disabilities. Child Development, 85, 532–548. http://dx.doi.org/10.1111/cdev.12124. Gutzwiller-Helfenfinger, E., Gasser, L., & Malti, T. (2010). Moral emotions and moral judgment in children’s narratives: Comparing real-life and hypothetical transgressions. New Directions for Child and Adolescent Development, 2010, 11–32. http://dx.doi.org/ 10.1002/cd.273. Hitti, A., & Killen, M. (2014). Group norms, stereotypes, and social exclusion in ethnic peer groups. Unpublished manuscript. Tulane University. Hitti, A., Malti, T., & Killen, M. (2014). Adolescents’ attributions of group emotions regarding inter-ethnic social exclusion. Unpublished manuscript. Tulane University. Hitti, A., Mulvey, K. L., Rutland, A., Abrams, D., & Killen, M. (2013). When is it okay to exclude a member of the ingroup? Children’s and adolescents’ social reasoning. Social Development, 23, 451–469. http://dx.doi.org/10.1111/sode.12047.

274


Hughes, J. M., Bigler, R. S., & Levy, S. R. (2007). Consequences of learning about historical racism among European American and African American children. Child Development, 78, 1689–1705. http://dx.doi.org/10.1111/j.1467-8624.2007.01096.x. Jugert, P., Noack, P., & Rutland, A. (2011). Friendship preferences among German and Turkish preadolescents. Child Development, 82, 812–829. http://dx.doi.org/10.1111/ j.1467-8624.2010.01528.x. Keller, M., Lourenço, O., Malti, T., & Saalbach, H. (2003). The multifaceted phenomenon of “happy victimizers”: A cross-cultural comparison of moral emotions. British Journal of Developmental Psychology, 21, 1–18. http://dx.doi.org/10.1348/026151003321164582. Killen, M., Kelly, M. C., Richardson, C. B., Crystal, D., & Ruck, M. D. (2010). European American children’s and adolescents’ evaluations of interracial exclusion. Group Processes & Intergroup Relations, 13, 283–300. http://dx.doi.org/10.1177/ 1368430209346700. Killen, M., Mulvey, K. L., & Hitti, A. (2013). Social exclusion in childhood: A developmental intergroup perspective. Child Development, 84, 772–790. http://dx. doi.org/10.1111/cdev.12012. Killen, M., & Rutland, A. (2011). Children and social exclusion: Morality, prejudice, and group identity. Children and social exclusion: Morality, prejudice, and group identity. New York, NY: Wiley-Blackwell. http://dx.doi.org/10.1002/9781444396317. Killen, M., & Smetana, J. G. (2015). Origins and development of morality. In M. Lamb (Ed.), Handbook of child psychology: Vol. III. (7th ed., pp. 701–749). NY: Wiley-Blackwell Publishing Ltd. Killen, M., & Stangor, C. (2001). Children’s social reasoning about inclusion and exclusion in gender and race peer group contexts. Child Development, 72, 174–186. http://dx.doi.org/ 10.1111/1467-8624.00272. Lagattuta, K. H. (2014). Linking past, present, and future: Children’s ability to connect mental states and emotions across time. Child Development Perspectives, 8, 90–95. http://dx.doi. org/10.1111/cdep.12065. Malti, T. (2014). Toward an integrated clinical-developmental model of guilt. Unpublished manuscript, University of Toronto. Malti, T., Gasser, L., & Buchmann, M. (2009). Aggressive and prosocial children’s emotion attributions and moral reasoning. Aggressive Behavior, 35, 90–102. http://dx.doi.org/ 10.1002/ab.20289. Malti, T., Killen, M., & Gasser, L. (2012). Social judgments and emotion attributions about exclusion in Switzerland. Child Development, 83, 697–711. http://dx.doi.org/10.1111/ j.1467-8624.2011.01705.x. Malti, T., & Krettenauer, T. (2013). The relation of moral emotion attributions to prosocial and antisocial behavior: A meta-analysis. Child Development, 84, 397–412. http://dx.doi. org/10.1111/j.1467-8624.2012.01851.x. Malti, T., & Latzko, B. (2012). Moral emotions. In V. Ramachandran (Ed.), Encyclopedia of human behavior (2nd ed., pp. 644–649). Maryland Heights, MO: Elsevier. http://dx.doi. org/10.1016/B978-0-12-375000-6.00099-9. Malti, T., Noam, G. G., Beelmann, A., & Sommer, S. (Eds.). (in press (a)). Interventions for children and adolescents: From adoption to adaptation—From programs to systems. Guest-edited special issue, Journal of Clinical Child and Adolescent Psychology. Malti, T., & Ongley, S. F. (2014). The development of moral emotions and moral reasoning. In M. Killen & J. G. Smetana (Eds.), Handbook of moral development (2nd ed., pp. 163–183). New York, NY: Psychology Press. Malti, T., Zuffiano` , A., Cui, L., Colasante, T., Peplak, J., & Bae, N. Y. (in press (b)). Healthy social-emotional development and peer group inclusion and exclusion. In C. Spiel, N. J. Cabrera, & B. Leyendecker (Eds.), Handbook of positive development of minority children. Netherlands: Springer.


275

Menesini, E., & Camodeca, M. (2008). Shame and guilt as behaviour regulators: Relationships with bullying, victimization and prosocial behaviour. British Journal of Developmental Psychology, 26, 183–196. http://dx.doi.org/10.1348/026151007X205281. Mulvey, K. L., Hitti, A., Rutland, A., Abrams, D., & Killen, M. (2014). When do children dislike ingroup members? Resource allocation from individual and group perspectives. Journal of Social Issues, 70, 29–46. http://dx.doi.org/10.1111/josi.12045. Mulvey, K. L., & Killen, M. (2014). Challenging gender stereotypes: Resistance and exclusion. Child Development. http://dx.doi.org/10.1111/cdev.12317. Nesdale, D. (2004). Social identity processes and children’s ethnic prejudice. In M. Bennett & F. Sani (Eds.), The development of the social self (pp. 219–245). New York, NY: Psychology Press. Nesdale, D. (2007). The development of ethnic prejudice in early childhood: Theories and research. In O. Saracho & B. Spodek (Eds.), Contemporary perspectives on socialization and social development in early childhood education (pp. 213–240). Charlotte, NC: Information Age Publishing. Nesdale, D. (2008). Social identity development and children’s ethnic attitudes in Australia. In S. M. Quintana & C. McKown (Eds.), Handbook of race, racism and the developing child (pp. 313–338). New Jersey, NJ: John Wiley & Sons. Nesdale, D., & Lawson, M. J. (2011). Social groups and children’s intergroup attitudes: Can school norms moderate the effects of social group norms? Child Development, 82, 1594–1606. http://dx.doi.org/10.1111/j.1467-8624.2011.01637.x. Nesdale, D., Maass, A., Griffiths, J., & Durkin, K. (2003). Effects of in-group and outgroup ethnicity on children’s attitudes towards members of the in-group and out-group. British Journal of Developmental Psychology, 21, 177–192. http://dx.doi. org/10.1348/026151003765264039. Nussbaum, M. (1999). Sex and social justice. NY: Oxford University Press. Ojala, K., & Nesdale, D. (2004). Bullying and social identity: The effects of group norms and distinctiveness threat on attitudes towards bullying. British Journal of Developmental Psychology, 22, 19–35. http://dx.doi.org/10.1348/026151004772901096. Olthof, T., Goossens, F. A., Vermande, M. M., Aleva, E. A., & van der Meulen, M. (2011). Bullying as strategic behavior: Relations with desired and acquired dominance in the peer group. Journal of School Psychology, 49, 339–359. http://dx.doi.org/10.1016/j. jsp.2011.03.003. Olweus, D. (1993). Bullying at school: What we know and what we can do. Cambridge, MA: Blackwell. Pettigrew, T. F., & Tropp, L. R. (2006). A meta-analytic test of intergroup contact theory. Journal of Personality and Social Psychology, 90, 751–783. http://dx.doi.org/10.1037/00223514.90.5.751. Rubin, K. H., Bukowski, W. M., & Parker, J. G. (2006). Peer interactions, relationships, and groups. In N. Eisenberg, W. Damon, & R. M. Lerner (Eds.), Handbook of child psychology: Social, emotional, and personality development: Vol. III. (6th ed., pp. 571–645). Hoboken, NJ: John Wiley & Sons Inc. Ruble, D. N., Martin, C. L., & Berenbaum, S. (2006). Gender development. In W. Damon & N. Eisenberg (Eds.), Handbook of child psychology: Vol. 3, personality and social development (6th ed., pp. 858–932). New York: Wiley Publishers. Rutland, A., Killen, M., & Abrams, D. (2010). A new social-cognitive developmental perspective on prejudice: The interplay between morality and group identity. Perspectives on Psychological Science, 5, 279–291. http://dx.doi.org/10.1177/ 1745691610369468. Salmivalli, C. (2010). Bullying and the peer group: A review. Aggression and Violent Behavior, 15, 112–120. http://dx.doi.org/10.1016/j.avb.2009.08.007. Sen, A. (2009). The idea of justice. Cambridge, MA: Harvard University Press.

276


Sierksma, J., Thijs, J., Verkuyten, M., & Komter, A. (2014). Children’s reasoning about the refusal to help: The role of need, costs and social perspective taking. Child Development, 86, 1134–1149. http://dx.doi.org/10.1111/cdev.12195. Smetana, J. G. (2006). Social-cognitive domain theory: Consistencies and variations in children’s moral and social judgments. In M. Killen & J. G. Smetana (Eds.), Handbook of moral development (pp. 119–154). Mahwah, NJ: Lawrence Erlbaum Associates. Strohmeier, D., & Noam, G. G. (2012). Bullying in schools: What is the problem, and how can educators solve it? New Directions for Youth Development, 2012, 7–14. http://dx.doi. org/10.1002/yd.20003. Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp. 33–47). Monterey, CA: Brooks-Cole. Thijs, J., & Verkuyten, M. (2012). Ethnic attitudes of minority students and their contact with majority group teachers. Journal of Applied Developmental Psychology, 33, 260–268. http://dx.doi.org/10.1016/j.appdev.2012.0. Tropp, L. R., & Prenovost, M. A. (2008). The role of intergroup contact in predicting children’s inter-ethnic attitudes: Evidence from meta-analytic and field studies. In S. R. Levy & M. Killen (Eds.), Intergroup attitudes and relations in childhood through adulthood (pp. 236–248). Oxford, UK: Oxford University Press. Turiel, E. (2002). The culture of morality: Social development, context, and conflict. New York, NY: Cambridge University Press. van Noorden, T. H., Haselager, G. J., Cillessen, A. H., & Bukowski, W. M. (2014). Empathy and involvement in bullying in children and adolescents: A systematic review. Journal of Youth and Adolescence. http://dx.doi.org/10.1007/s10964-014-0135-6. Verkuyten, M. (2002). Ethnic attitudes among minority and majority children: The role of ethnic identification, peer group victimization and parents. Social Development, 11, 558–570. http://dx.doi.org/10.1111/1467-9507.00215. Verkuyten, M. (2007). Ethnic in-group favoritism among minority and majority groups: Testing the self-esteem hypothesis among preadolescents. Journal of Applied Social Psychology, 37, 486–500. http://dx.doi.org/10.1111/j.1559-1816.2007.00170.x. Verkuyten, M., & Steenhuis, A. (2005). Preadolescents’ understanding and reasoning about asylum seeker peers and friendships. Journal of Applied Developmental Psychology, 26, 660–679. http://dx.doi.org/10.1016/j.appdev.2005.08.002. Verkuyten, M., & Thijs, J. (2001). Ethnic and gender bias among Dutch and Turkish children in late childhood: The role of social context. Infant & Child Development, 10, 203–217. http://dx.doi.org/10.1002/icd.279. Wainryb, C., Brehl, B., & Matwin, S. (2005). Being hurt and hurting others: Children’s narratives accounts and moral judgments of their own interpersonal conflicts. Monographs of the Society for Research in Child Development, 70, 1–125. http://dx.doi.org/10.1111/ j.1540-5834.2005.00350.x.

AUTHOR INDEX Note: Page numbers followed by “f ” indicate figures, “t ” indicate tables and “np ” indicate footnotes.

A Aboud, F.E., 251, 265–266 Abrams, D., 252, 253, 254, 256–258, 259, 263–264, 272 Adisetiyo, V., 18–19, 20, 31 Adler, J., 152–153 Adolph, K.E., 21–22, 144 Ahade, S., 200 Akiyama, L.F., 7–9, 19, 41 Alade, F., 222 Alaerts, K., 9–10, 44 Albert, D., 59–62, 68–69, 70 Aleva, E.A., 262–263 Alijabar, P., 9–10 Aljabar, P., 18–19, 20 Al-Jabari, R.M., 191–192 Allen, C.L., 127, 129–130, 143–144 Allen, N.B., 67–68, 70 Allison, T., 139 Allom, R., 18–19, 20 Almli, C.R., 6, 7–11, 12t, 14, 25–27, 30–31, 32–34, 44 Almoammer, A., 109 Alpe´rovitch, A., 6–7, 22–23, 24, 27–28 Als, H., 33–34 Altaye, M., 6, 8–9, 17 Altshuler, J.L., 195–196 Amadon, A., 103–104, 105, 106–107 Ames, E.W., 136–137 Amsterlaw, J., 195–196 Anbeek, P., 34 Andersen, R.E., 221–222 Anderson, C.A., 221–222 Anderson, D.R., 221–222, 225t, 226, 227–228, 240 Anderson, M.L., 106–107 Andrews, G., 196–197 Andrews, J.A., 67–68 Angold, A., 59–62, 67, 78, 84 Ansari, D., 94–111 Anzures, G., 130, 141, 142–143

Appelbaum, L.G., 142–143 Apperly, I.A., 38t Appiah, K.A., 272 Arachacki, M.A., 191–192 Archibald, S.L., 2–3, 24 Arnold, A.P., 75–76 Arnott, B., 173–174 Arseneault, L., 221, 235–236 Arsenio, W.F., 252–254, 261 Ashburner, J., 5, 6–7, 10–11, 24, 27–28 Aslin, R.N., 38t, 164–165 Astington, J.W., 187, 204–205, 206 Aubert-Broche, B., 33–34 Austin, W.G., 254, 256–257, 265 Avants, B.B., 10–11

B Babb, J.S., 6–7, 24 Bada, H., 132 Bae, N.Y., 267–268 Bahrami, B., 106 Bailey, D.H., 101 Baird, J.A., 204–205 Baker, L., 220 Baldwin, D.A., 169 Baltazar, N.C., 176 Bamford, C., 193, 195–196 Banaji, M.R., 168 Band, G.P.H., 237 Banerjee, M., 197 Banerjee, R., 200, 203–204, 205–206 Banich, M., 59–62, 68–69 Bardi, L., 127, 130 Bargh, J., 228 Bar-Haim, Y., 141–142 Barkovich, A.J., 33–34 Barkow, J., 176 Barna, A.C., 127, 129–130, 143–144 Barner, D., 109 Barnes, P.D., 34 Barnett, T.A., 222 277

278 Baron, J., 190 Baron-Cohen, S., 186, 199, 200 Barr, R., 222 Bartelet, D., 101, 103 Barth, H., 173–174 Bartlett, J.C., 132 Bartlett, S.J., 221–222 Barton, B.A., 63 Bashore, T.R., 237 Bathelt, J., 38t, 39–40 Baudouin, J., 135–136, 140–141 Baumeister, R.F., 174–175, 223–224 Bayen, U.J., 190 Bayer, J.K., 67–68 Beckmann, C.F., 5, 9–10 Bednar, J.A., 122–123 Bedny, M., 188 Behrend, D.A., 170, 172 Behrens, T.E., 5, 9–10 Beilock, S.L., 99–100, 102–103, 105–106, 107–109 Bell, M.A., 167–168 Belsky, D., 221, 235–236 Belsky, J., 56–57, 62–63, 64–65, 67–68 Beltz, A.M., 54–57, 58–85 Bender, P.K., 196–197 Bennett, M., 256–257 Benning, S.D., 71–72, 75, 81 Benson, J.B., 220 Bentin, S., 139 Berenbaum, S.A., 54–57, 58–85, 254–255 Bergelson, E., 156 Berhow, M.T., 2–3, 24 Berkowitz, L., 221–222 Berndt, T.J., 263 Bernhardt, P.C., 58 Bernstein, D.M., 190 Berteletti, I., 99–100 Bertin, E., 131–133, 140–141 Bertrand, J., 142–143 Best, J.R., 200–201, 220 Bhatt, R.S., 131–133, 140–141 Bigelow, A.E., 202 Bigler, E.D., 7 Bigler, R.S., 265–266 Bingman, H., 167 Birch, S.A.J., 177–178, 190 Bird, G., 66, 68–69, 71, 77 Biro, F.M., 58–59, 62, 63–66, 81, 82

Author Index

Bjork, J.M., 70 Blacker, D., 18–19 Blair, C., 98–99, 221 Blair, C.B., 230 Blakemore, J.E.O., 75–76 Blakemore, S.J., 66, 68–71, 72, 77 Blanton, R.E., 6–7 Blasi, A., 26, 36–37, 38t Blattman, A.J., 188–189, 190, 192, 197–198, 201–202, 205 Blomert, L., 101, 102–103 Bloom, P., 172–173, 190, 227np Blume, W.T., 41–42 Blumenthal, J., 2–3, 22–23, 24, 27–28, 33–34 Boardman, J.P., 19, 20 Boas, D.A., 98–99, 235–236 Bogusweski, K., 220–242 Bolls, P.D., 225t, 226–227, 240–242 Bommer, W., 3 Bonny, J.W., 99–100, 103 Booth, A., 77 Borenshteyn, N., 2–3, 26–27 Borofsky, L.A., 70–71 Borzekowski, D.L., 222 Bosacki, S.L., 189, 190, 192, 197–198, 201–202, 205, 206 Botteron, K.N., 7, 8–11 Bourgeois, J.P., 2–3 Bowlby, J., 203 Boxer, A., 59 Boyce, C.A., 70 Boyer, T.W., 197–198 Bradley, S.J., 80 Bradmetz, J., 196–197 Brady, M., 17 Brain Development Cooperative Group, 7 Braine, M.D., 160 Braman, D., 190–191 Brankaer, C., 101 Brannon, E.M., 95, 97, 98–101, 103–104 Bratslavsky, E., 174–175 Braver, T.S., 2 Breedlove, S.M., 75–76 Brehl, B., 262 Brent, E., 199 Breton, C., 200–201 Bricker, J.B., 62, 63, 64–65, 82 Brody, B., 2–3, 26–27

Author Index

Brook, J.S., 222 Brooks, R.M., 119 Brooks-Gunn, J., 62, 67, 82 Brosseau-Liard, P., 177–178 Brown, E.D., 3–4, 5, 10, 122 Brown, J.R., 204–205 Brown, T.T., 2, 43 Bruce, J., 203–204 Brumariu, L.A., 203 Brunner, P., 3 Bruno, J.P., 239 Bruyer, R.R., 118, 139 Bryant, J., 225–226, 225t, 227–228 Bryson, S., 220 Buchmann, M., 252–253 Buckley, P.B., 105 Buckner, R.L.P., 6–7, 22–23, 24 Budreau, D., 121 Buholzer, A., 267–268, 268f Bukowski, W.M., 259–260, 261–262 Bulik, C., 62, 82 Bull, R., 221 Bulthe´, J., 106–107 Burgund, E.D., 6–7 Burnett Heyes, S., 66, 68–69 Burt, S.A., 59–62, 77–78, 81 Butterworth, B., 106 Buza, R.C., 41–42 Byars, A.W., 3 Bynner, J., 94

C Cabrera, N.J., 267–268 Cai, D.S., 41–42 Calero, C.I., 205 Calvert, S.L., 222, 226–227, 239–240, 241 Cameron, L., 265–266 Camodeca, M., 261 Campbell, T., 225t, 228, 240 Cannon, G., 38t Cantlon, J.F., 97–99, 103–104 Caputi, M., 205–206 Carey, S., 98–99, 108, 109, 118, 132, 144–145, 154–155, 161, 228 Carlin, J.B., 67–68 Carlson, S.M., 164, 200–201, 220, 233 Carpendale, J.I.M., 188–189, 190–191, 204–206 Carpenter, M., 172–173

279 Carroll, K., 197–198 Carter, E.J., 98–99 Casasola, M., 129–130, 140–141 Caser, C., 8–9 Casey, B.J., 2, 26, 36–37, 68–69, 72–73 Cashon, C.H., 118–119, 120–145, 128f, 129f Caspi, A., 62–63, 67–68 Cassidy, K.W., 190 Castellanos, F.X., 2–3, 9–10, 22–23, 24, 27–28, 44 Castellanos-Ryan, N., 62, 82 Castelli, P., 195 Castronovo, J., 101 Cauffman, E., 56–57, 59–63, 64–65, 68–69 Caulfield, F., 168 Ceulemans, A., 99–100 Chan, C.C., 169 Chandler, M.J., 188–189 Chappell, M., 9–10 Charman, T., 199, 202–203 Chase, E., 173–174 Chebrolu, H., 24, 27–28 Chein, J., 66, 70 Chen, C., 30 Chen, E.E., 161, 177–178 Chen, K., 30 Chen, Q., 101, 102–103 Cheskin, L.J., 221–222 Chevalier, N., 230 Chien, S., 124–125, 140–141 Child, A., 54, 59–62, 68–69 Chinello, A., 108np Choe, M.S., 41 Chow, V., 155–156 Christakis, D.A., 222, 223 Christy, K., 222 Chrysikou, E.G., 229 Chu, F.W., 99–100, 101, 103 Chugani, D.C., 5–7, 17 Chugani, H.T., 5–7, 17 Chun, M.M., 138–139, 226–227 Chung, Y., 2, 43 Cicchetti, D., 203–204 Cillessen, A.H.N., 261–262, 263 Claessens, A., 94, 221 Clark, C.A., 230 Clark-Carter, D., 202

280 Clasen, L.S., 69–70, 72 Clayden, J.D., 38t, 39–40 Cle´ment, F., 152–153, 154, 157, 163 Clements, W., 204–205 Coady, C.A.J., 152–153 Cogsdill, E.J., 168 Cohen Kadosh, R., 106 Cohen, D., 100–101 Cohen, D.J., 186 Cohen, J.D., 2, 200–201 Cohen, L., 100–101, 108np Cohen, L.B., 127, 128–130, 128f, 129f, 136–137, 140–141, 143–144 Cohen, P., 222 Cohen-Kettenis, P.T., 80 Colasante, T., 267–268 Cole, C.A., 153, 163, 164 Cole, C.E., 164 Cole, K., 204–205 Cole, M., 188 Cole, S.R., 188 Coleman, J., 59 Coleman, L., 59 Coleman, M., 141–142 Collins, D.L., 3–4, 5, 8–9, 10–11, 33–34 Collins, P.A., 70, 240 Collins, P.F., 70 Collins, R., 132 Conel, J.L., 2–3, 26–27 Conger, R.D., 62–63 Connington, A., 11, 19–20, 21–22 Connor, C.M., 228–229 Conte, S., 99–100 Content, A., 103 Cook, P.A., 10–11 Cooke, T., 196–197 Cooper, N., 225t, 226 Copeland, W., 67 Corbly, C.R., 131–132, 140–141 Corley, R.P., 54–57, 58–85 Cornelius, R., 197 Correira, T., 38t Corriveau, K.H., 156–157, 159, 161, 166–167, 172, 173–174, 175–176, 177–178 Cosmides, L., 176 Costello, E.J., 59–62, 67, 78, 84

Author Index

Cottam, K., 190–191 Coughlin, C.A., 195 Courage, M.L., 38t Crawford, P.B., 63 Crawley, S.L., 167 Crespo, C.J., 221–222 Crivello, F., 6–7, 19, 22–23, 24, 27–28 Crockett, L., 59 Croft, A.C., 163 Croft, C., 202 Crone, E.A., 69, 70–71, 72, 77 Crosier, B.S., 67–68 Cross, D., 187, 188–189 Cross, J.H., 38t, 39–40 Crouter, A.C., 77 Crystal, D., 265 Csibra, G., 154–155, 156–157, 168–169 Cuccaro, P., 67 Cuddy, A.J., 170, 173–174, 175–176 Cuevas, K., 167–168 Cui, L., 267–268 Culbert, K.M., 77–78, 81 Culver, J.P., 235–236 Cumberland, A., 221 Cwik, M., 221

D Dabbs, J.M., 58 Dager, S.R., 7–9, 19, 41 Dahl, R.E., 58–59, 63–66, 69, 70–72, 75, 77, 81 Dalla Barba, B., 122–124 Dalsgaard, S., 222 Damarla, S.R., 106–107 Damon, W., 254–255, 259–260 Dan, H., 41–42 Dan, I., 41–42 Dannemiller, J.L., 121 Dansky, J., 229 Das Gupta, M., 202 Davidoff, J., 139 Davidson, D., 152–153 Davidson, R., 2 Davies, S., 67 Davis, B., 5–6, 10, 17 Davis, E.L., 195–196 de Haan, M., 38t, 39–40, 139–140, 141–142 de Heering, A., 144–145

281

Author Index

De Moor, C., 7, 9–10 De Moor, W., 105 de Rosnay, M., 194, 195–197, 202 de Schonen, S., 126–127, 140–141 De Smedt, B., 101, 106–107 De Weerdt, F., 99–100 Defever, E., 103 Dehaene, S., 94–95, 97–99, 100–101, 103–104, 105, 106–107, 108np, 110–111 Dehaene-Lambertz, G., 98–99 DeHart, G., 56–57, 62–63, 64–65 Delcroix, N., 19 DeLoache, J.S., 227np DeNicola, C.A., 142–144 Denio, E., 9–10, 44 Dennis, M., 205–206 Deoni, S.C., 26, 36–37 Desikan, R.S., 18–19 Desoete, A., 99–100 Despland, P.P., 118, 139 Devine, A., 101 Devine, R.T., 200–201, 206 Dewind, N.K., 100–101 Dhillon, K., 241–242 Di Giorgio, E., 127, 130 Di Martino, A., 9–10, 44 Diamond, A., 220, 221, 229, 230, 234, 237–238 Diamond, R., 118, 132, 144–145 Dickerson, B.C., 18–19 Dickson, N., 221, 235–236 Diester, I., 103–104, 105 Ding, L., 59 Dinov, I.D., 3–4, 5, 10–11, 28–30, 29f, 31 Dixon, J.A., 265 Dodell-Feder, D., 188 Dodson, J.D., 237–238 Doebel, S., 160–161, 163–164, 174–175, 176–177 Dolan, C.V., 230 Domanic, J.A., 192 DonCarlos, L.L., 78–79, 80 Donelan-McCall, N., 204–205 Dong, Q., 241–242 Donlan, C., 109 Donnerstein, E., 221–222 Doom, J.R., 71, 74

Dorn, L.D., 58–59, 62, 63–66, 67–68, 81, 82 Dorris, L., 199 Dotterer, A.M., 77 Douros, K., 63 Dovidio, J.F., 265–266 Dowsett, C.J., 94, 221 Doyle, A.B., 265–266 Draper, P., 67–68 Drummond, K.D., 80 Drummy, A.B., 167–168 Duarte, C.P., 199 Dubas, J.S., 63–64 Dubow, E., 222 Duncan, G.J., 94, 221 Dunfield, K.A., 175–176 Dunn, J., 204–205 Dunn, R.S., 3 Dunning, D., 189 Dupont, P., 105 Durand, K., 135–136, 140–141 Durkin, K., 252 Durrheim, K., 265 Durston, S., 72–73 Dyet, L.E., 19, 20 Dziurawiec, S., 120–121, 120f

E Eaves, B., 174 Eaves, L., 62, 82 Echols, C.H., 156–157, 158, 163–164 Edwards, A.D., 19, 20, 33–34 Eger, E., 103–104, 105, 106–107 Eimer, M., 139 Einav, S., 157–158 Eisbach, A.O., 195 Eisenberg, N., 221, 254–255, 259–260 Eisenberger, N.I., 70–71 Elder, G.H., 62–63 Elliot, L., 173–174 Elliott, M.N., 67 Ellis, A., 194 Ellis, A.E., 135–137, 138, 144 Ellis, B.J., 67–68 Ellis, H., 120–121, 120f Elwell, C.E., 7–8, 21–22, 38t Emberson, L., 38t Emerson, M.J., 220

282 Engle, R.W., 54–55, 66 Ensor, R., 202, 204–205 Epsie, C.A.E., 199 Epstein, C.L., 10–11 Erdfelder, E., 190 Ericsson, A., 9–10 Erisir, A., 223 Erkanli, A., 62, 78, 82, 84 Ernst, M., 70 Ernst, R.R., 3 Espy, K.A., 221, 230 Etard, O., 19 Evans, A.C., 3–4, 5, 6–9, 10–11, 17, 18–19, 22–23, 24, 27–28 Evans, D., 22–24, 27–28 Evans, E.M., 188–189 Evans, J., 139 Evans, J.St.B.T., 154 Ewing, L., 168

F Fabes, R.A., 221 Fabricius, W.V., 197–198 Facoetti, A., 99–100 Fagan, J.F., 125–127, 140–141 Faja, S., 220 Fan, L., 28–30, 29f, 31 Fan, Y., 8–9, 22–24, 34–36 Farhadian, M., 204–205 Farris, C.L., 228–229 Faulkner, P., 152–153 Fazio, L.K., 101 Feddes, A.R., 265 Feigenson, L., 97–98, 99–100, 103–104, 109 Feldman, J., 140–141 Feranil, A.B., 58 Ferguson, C.J., 222 Ferguson, K.T., 130, 140–141 Fernandez, E.P., 99–100 Fernyhough, C., 173–174, 202–203 Ferrell, J., 257–258, 263–264 Fias, W., 103–104, 105, 110–111 Field, D.E., 240 Fielden, J.A., 58 Finkenauer, C., 174–175 Fiori, N., 139 Fireman, G., 192 Fischl, B., 18–19

Author Index

Fisher, P.A., 203–204 Fiske, S.T., 170, 173–174, 175–176 Fitneva, S.A., 175–176 Fitzpatrick, C., 222 Fjell, A.M., 2, 43 Flavell, E.R., 195 Flavell, J.H., 193, 194, 195, 221–222 Flax, J., 2–3, 26–27 Flin, R.H., 144–145 Foley, D., 62, 82 Fonagy, P., 202 Fonov, V.S., 6–7, 8–9, 10–11, 17, 33–34 Forbes, E.E., 66, 69, 70–71 Forbes, P.W., 7, 9–10 Ford, S., 190–191 Foster, D.S., 2–3, 24 Foster, E.M., 222 Fotenos, A.F.S., 6–7, 22–23, 24 Fox, M., 3–4 Fox, P.T., 3–4, 5, 10, 18–19, 235–236 Frackowiak, R.S.J., 6–7, 24, 27–28 Fradley, E., 202 Franke, T.M., 222 Frankish, K., 154 Free, S.L., 18–19, 20 Freeman, C., 21–22 Freitas, C.S., 3–4 Fretzayas, A., 63 Fricker, E., 152–153 Friedman, N.P., 220 Friedman, O., 169 Friedrich, L., 224 Friston, K.J., 5, 6–7, 10–11, 24, 27–28, 239 Frith, U., 199 Froimowitz, M.P., 7 Frye, D., 186 Fu, G., 191–192 Fuhs, M.W., 101, 103 Fujita, A., 41–42 Fukuda, H., 6–7 Fumarola, A., 108np Fusaro, M., 172, 173–174

G Gabriel, F., 101 Gaertner, S.L., 265–266 Gallay, M., 135–136, 140–141 Gallistel, C.R., 103–104

Author Index

Ganea, P.A., 161–162, 227np Gao, W., 23–24 Gao, Y., 227, 237, 239–241 Garon, N., 220 Garrison, M., 222, 223 Gaser, C., 6, 8–9, 17 Gasser, L., 206, 252–253, 262, 267–268, 268f Gasston, D., 26, 36–37 Gautam, P., 72 Gauthier, I.I., 118, 125, 139 Gazanizad, N., 204–205 Ge, L., 141–142 Ge, X., 62–63, 67 Ge, Y., 6–7, 24 Geary, D.C., 99–100, 101, 103 Gee, J.C., 10–11 Geier, C.F., 54, 59–62, 68–69 Geiger, S., 225t, 240–241 Geist, E.A., 224 Gelman, R., 103–104 Gelman, S.A., 168–169, 171–172 Gentile, D.A., 222 George, N., 139 Gergely, G., 168–169 Gerig, G., 5–6, 10, 17, 34 Germine, L., 99–100 Gersh, T.L., 239–240 Gerstadt, C.L., 234 Gesselman, A.N., 67–68 Gettler, L.T., 58 Gevers, W., 105 Ghesquie`re, P., 101 Ghetti, S., 195 Ghossainy, M.E., 153, 161 Gibson, M., 224 Giedd, J.G., 72, 73f Giedd, J.N., 2–3, 5–6, 22–23, 24, 26, 27–28, 33–34, 36–37, 69–70, 72–73 Gilbert, A.L., 105 Gilles, F., 2–3, 26–27 Gillman, C.B., 105 Gilmore, C.K., 99–100, 101, 103 Gilmore, J.H., 5–6, 8–9, 17, 19, 22–24, 34–36 Gilmore, R.O., 21–22 Giovanello, K.S., 23–24 Girton, L.E.B., 6–7, 22–23, 24

283 Givens, B., 239 Glick, P., 170, 173–174, 175–176, 265–266 Gliga, T., 154–155, 156–157, 168–169 Glover, G.H., 70–71, 188 Glutting, J., 103 Gnepp, J., 192 Gobbini, M., 138–139 G€ obel, S.M., 101 Goddings, A.-L., 66, 68–70, 71, 72, 77 Gogtay, N., 5–6, 22–23, 24, 27–28, 72 Goldberg, S., 152–153 Goldfarb, D., 186–207 Goldstein, A.G., 119 Golomb, J.D., 226–227 Gonzalez, S., 41 Good, C.D., 6–7, 24, 27–28 Goodman, R., 229 Goodrich, S.A., 240 Goossens, F.A., 262–263 Gopnik, A., 166–167, 187 Goren, C., 120–121 Goswami, U., 186, 187 Gotlieb, I.H., 78 Goto, R., 6–7, 22–23, 24, 27–28 Goulding, S.M., 66, 71 Gousias, I.S., 19, 20 Gouttard, S., 22–24, 27–28 Graber, J.A., 62–64, 66, 67–68, 82 Graf, P., 166–167 Graham, K.A., 38t Graham, S.A., 59–62, 68–69, 168–169 Granger, D.A., 77 Grant, M.G., 191–192 Grant, P.E., 41 Grassiot, B., 6–7, 22–23, 24, 27–28 Grave de Peralta, R., 41 Gray, S.A., 99–100, 103 Grazzani, I., 202–203 Green, F.L., 195 Greenstein, D.K., 5–6, 22–23, 24, 27–28, 72 Griffiths, J., 252 Grimm, K.J., 62, 82 Groenendaal, F., 34 Grossman, M., 10–11 Grossman, R.I., 6–7, 24 Grossmann, T., 174–175 Guimond, A., 10–11 Gunderson, E.A., 109

284 Gunnar, M.R., 71–72, 74, 75, 81 Gunther Moor, B., 69, 77 Guo, X., 30 G€ urog˘lu, B., 69, 77 Gutzwiller-Helfenfinger, E., 262 Guz, G.R., 193, 195–196 Gweon, H., 188

H Ha, O.-R., 127, 129–130, 142–144 Haggard, P., 103–104, 105, 110–111 Hagler, D.J., 2, 43 Haidt, J., 190–191 Haigh, S.N., 167 Hajnal, J.V., 9–10, 18–19, 20 Halberda, J., 99–100, 103–104, 109 Halfon, N., 222 Halit, H., 139–140 Hamburger, S.D., 2–3, 24 Hamby, A., 221 Hamlin, J.K., 172–173, 227np Hammen, C.L., 78 Hammers, A., 18–19, 20 Hampson, S.E., 67–68 Hancock, P.J.B., 142–143 Hancox, R.J., 221, 222, 235–236 Handelsman, D.J., 58 Happe´, F.G., 199 Harden, K.P., 62, 82 Hardin, C.A., 192–193 Hardy, T.C., 41 Harnad, S., 111 Harrington, H., 221, 235–236 Harris, P.L., 153, 156–157, 159, 161, 163, 172, 173–174, 175–176, 177–178, 193, 194, 195–197, 202, 204–205 Harter, S., 188 Hartkens, T., 9–10, 18–19, 20 Harvey, C., 188–189, 190, 192, 197–198, 200–202 Haselager, G.J., 261–262 Hashtroudi, S., 166–167 Hasselhorna, M., 99–100 Hasson, U., 160 Hatry, A., 142–143 Haxby, J., 138–139 Hayashi, K.M., 5–6 Hayden, A., 131–133, 140–141

Author Index

Hayes, A.F., 189 Hayes, R., 122 Heckemann, R.A., 9–10, 18–19, 20 Heintz, C., 152–153, 154 Helm, D., 188–189 Hembacher, E., 195 Henderson, D., 152–153 Henderson, J.M., 38t, 39–40 Hendrickson, C., 173–174, 176–177 Henriksen, T.B., 222 Henson, R.N.A., 6–7, 24, 27–28 Herman, J., 41–42 Herting, M.M., 72 Hesselink, J.R., 2–3, 24 Hetherington, C., 173–174, 176–177 Heyes, S.B., 71, 77 Heyman, G.D., 172–173, 191–192 Hill, D.L.G., 9–10, 18–19, 20 Hill, E., 199 Hillman, J., 59 Hilt, L., 78 Hirst, W., 158 Hitti, A., 252, 253, 256, 258, 259, 260 Hix, H.R., 164 Hjortsvang, K., 186–207 Ho, S., 158 Hock, A., 132 Hodes, R.M., 141–142 Hoehl, S., 139–140 Hoeksma, M.R., 5–6 Hofer, B.K., 190–191 Hofer, F., 144–145 Hoffman, E., 138–139 Hojatkashani, C., 18–19, 20, 28–30, 29f, 31 Holland, S.K., 3, 5–6, 8–9, 17 Holloway, I.D., 101 Holm, S.A., 70 Holt, N.A., 118–119, 120–145 Holtzman, C.W., 66, 71 Homae, F., 41–42 Homan, R.W., 41–42 Hong, Y.J., 234 Hoshi, Y., 235–236 Houts, R.M., 56–57, 62–63, 64–65, 82 Hovell, M.F., 59 Howerter, A., 220 Hsiao, I.T., 6–7 Huang, B., 59, 62, 63, 82

285

Author Index

Huang, C.M., 6–7 Huang, J., 228 Hubbard, E.M., 103–104 Hueppi, P.S., 33–34 Huesmann, L.R., 221–222 Huettel, S.A., 2–3 Huetteroth, W., 105 Hughes, C., 200–201, 202, 204–205, 206 Hughes, D., 188 Hughes, J.M., 265–266 Huizinga, M., 230 Hulme, C., 101 Hulshoff Pol, H.E., 72 Hume, D., 152–153 Hunter, M.A., 136–137 Huot, R., 71 H€ uppi, P.S., 34 Huston, A.C., 94, 221–222, 225t, 226–228, 240, 241 Huttenlocher, P.R., 2–3 Hutton, D., 196–197 Hyde, D.C., 98–99, 100

I Iacono, W.G., 77–78, 81 Ichikawa, H., 139 Imada, T., 7–9, 19, 41 Inder, T.E., 33–34 Irvin, V.L., 59 Irwin, W., 2 Ishaak, N., 226 Ishai, A., 239 Ivry, R.B., 105 Izard, V., 98–99

J Jacques, S., 234 James, W., 195 Jampol, N., 197 Jankowski, J., 140–141 Jaswal, V.K., 157–158, 159, 161–162, 163, 164, 169, 170–172, 175–176 Jbabdi, S., 9–10 Jeffries, N.O., 2–3, 22–23, 24, 27–28 Jenkins, J.M., 204–205 Jenkinson, M., 5, 9–10 Jenkins-Smith, H., 190–191 Jernigan, T.L., 2–3, 24

Jewkes, A.M., 228–229 Jezzard, P., 2 Jia, H., 8–9, 19 Jiang, S., 33–34 Jin, Z., 30 Johansen-Berg, H., 5, 9–10 Johnson, C.N., 196–197 Johnson, D.J., 188 Johnson, D.R., 77 Johnson, F., 202–203 Johnson, J.D., 221–222 Johnson, J.E., 188 Johnson, J.G., 222 Johnson, M.H., 7–8, 21–22, 26, 36–37, 38t, 120–123, 120f, 139–140 Johnson, M.K., 166–167 Johnson, S., 154–155 Johnsrude, I.S., 6–7, 24, 27–28 Johnston, V., 130–131 Jolesz, F.A., 34 Jolin, E.M., 222 Jolliffe, T., 199 Jomier, M., 5–6, 10, 17 Jones, R., 200 Jones, R.M., 68–69, 70–71 Jonides, J., 2 Jordan, N.C., 103 Joseph, J.E., 131–132, 140–141 Joshi, S., 5–6, 10, 17 Jovanovic, B., 127 Jugert, P., 265 Juhasz, C., 5–7, 17 Juraska, J.M., 78–79, 80 Jurcak, V., 41–42 Just, M.A., 106–107

K Kagan, J., 203–204 Kahan, D.M., 190–191 Kakigi, R., 139 Kalish, C.W., 197 Kamenskaya, V.G., 109 Kan, E., 72 Kanazawa, S., 139 Kang, C., 22–24, 27–28 Kang, E., 28–30 Kang, H.C., 6–7 Kang, K.W., 28–30

286 Kangas, A., 132 Kanwisher, N., 105, 118, 138–139 Karasik, L.B., 144 Karthigasan, J., 2–3, 26–27 Kasen, S., 222 Kastner, S., 239 Katagiri, M., 41–42 Kato, M., 135–137 Kawahara, K., 225t, 226–227, 240–241 Kawashima, R., 6–7 Kay, P., 105 Kaysen, D., 2–3, 24 Keating, K., 59 Keel, P.K., 59–62, 77–78 Keibel, S., 5 Keil, F.C., 191–192 Keller, M., 206, 252–253 Kelly, D.J., 141–142 Kelly, J.E., 6–7 Kelly, M.C., 265 Kelly, R.L., 3–4, 5, 10 Kemner, C., 5–6 Kenemans, J.L., 5–6 Kennedy, K., 186–207 Kerns, K.A., 203 Kesek, A., 200–201 Khalil, S.L., 197–198 Khanum, S., 100 Kidd, C., 164–165 Kikinis, R., 34 Killen, M., 173–174, 197, 250–272 Kim, J.Y., 28–30, 77 Kim, Y.K., 28–30 Kinney, H., 2–3, 26–27 Kinnison, J., 106–107 Kinzler, K.D., 173–174, 176 Kirkham, N.Z., 155 Kirschner, D., 2–3, 26–27 Klapwijk, E.T., 66, 68–69 Klayman, J., 192 Klebanov, P., 94, 221 Klein, A., 10–11 Kleinschmidt, A., 103–104, 105, 106–107 Klein-Tasman, B.P., 142–143 Kloman, A., 2–3, 26–27 Kloo, D., 195 Klpper, K.M., 224 Klump, K.L., 59–62, 66, 77–78, 81

Author Index

Knauff, M., 155 Knickmeyer, R.C., 22–24, 27–28, 34 Knott, F., 199 Ko, H.J., 6–7 Kobayashi, C., 188 Kobayashi, M., 139 Kobayashi, N., 235–236 Koeman, A., 34 Koenig, M.A., 152–178 Koepp, M.J., 18–19, 20 Kohno, S., 41–42 Koizumi, M., 204 Kolkman, M.E., 101, 103 Kolson, D.L., 6–7, 24 Kondrad, R.L., 157–158, 164 Konishi, Y., 135–137 Koolstra, C.M., 226 Kramer, H.J., 186–207 Krettenauer, T., 252–253, 261–262 Kroesbergen, E.H., 101, 103 Krogh-Jespersen, S., 163–164 Kuan, W.C., 6–7 Kuczaj, S.A., 197–198 Kuhl, P.K., 7–9, 19, 41 Kuhlmeier, V., 172–173 Kulkofsky, S., 130, 140–141 Kuperman, J.M., 2, 43 Kurita, S., 227, 237, 239–241 Kushnir, T., 171–172 Kuzawa, C.W., 58 Kwitny, S., 142–143 Kwon, P., 166–167 Kyutoku, Y., 41–42

L Lackey, J., 152–153 LaDoueur, C.D., 71–72, 81 Lagattuta, K.H., 186–207, 261 Laible, D.J., 202 Lainhart, J.E., 7 Lalonde, C.E., 188–189 Lamb, M., 253 Lamm, C., 200–201 Lammertyn, J., 105 Lamy, D., 141–142 Lan, X., 233 Lancaster, J.L., 3–4, 5, 10, 18–19 Landauer, T.K., 95, 96–97

Author Index

Landeau, B., 19 Landerl, K., 108np Landhuis, C., 222 Lang, A., 225t, 226–227, 237–238, 239–242 Lange, N., 2–3, 7, 24 Langley, M., 190–191 Lantz, G., 41 Larsen, J.T., 192 Larson, K., 222 Larson, R.W., 188 Latzko, B., 253 Lauricella, A., 222 Lawson, J., 199 Lawson, M.J., 257 Le Bihan, D., 103–104, 105, 106 Le Corre, M., 108, 109 Le Grand, R., 118, 127 Leboyer, M., 200 Lecce, S., 205–206 Lecours, A.R., 26–27 Lećuyer, R., 135–136, 140–141 Lee, D.S., 28–30 Lee, J.S., 28–30 Lee, K., 130, 141–142, 191–192, 200–201, 221, 230, 233 Lee, S.H., 6–7 Lee, S.J., 237–238, 241 Leekam, S.R., 204–205, 206 Legare, C.H., 191–192, 233 Legerstee, M., 186 Leibenluft, E., 70–71 Lemaıˆtre, H., 6–7, 22–23, 24, 27–28 Lemerise, E.A., 261 Lemieux, L., 18–19, 20 Lemoine, C., 135–136, 140–141 Lench, H.C., 195–196 Lenroot, R.K., 5–6, 22–23, 24, 27–28, 72, 73f Leo, I., 122–124, 125, 133–134, 134np, 140–141 Leonard, G., 7, 9–10 Leppert, I.R., 7, 33–34 Lerch, J., 6–7, 22–23, 24, 27–28 Lerner, R.M., 66, 67–68, 220, 259–260 Lerva˚g, A., 101 Leseman, P.P.M., 101, 103 Leung, K., 9–10, 18–19, 20 Levin, S., 225t, 226

287 Levine, L.J., 195–196 Levine, S.C., 109 Levy, S.R., 251, 256–258, 259, 265–266 Lew, S., 41 Lewinsohn, P.M., 67 Lewis, C., 205–206 Lewis, D.K., 152–153 Lewis, J., 155–156 Lewis, M.D., 200–201 Lewis, R., 190–191 Lewis, T.L., 121 Leybaert, J., 103 Leyendecker, B., 267–268 Li, H., 220–242 Li, J., 70–71, 101, 102–103 Li, Q., 9–10, 44 Li, Y., 99–100, 101, 103 Libby, V., 70–71 Liben, L.S., 75–76 Libertus, K., 143 Libertus, M.E., 95, 99–100, 103–104, 109 Liddle, B., 198 Lidstone, J., 202–203 Lightfoot, C., 188 Lillard, A.S., 220–242 Lim, K., 70 Lin, A.C., 105 Lin, W.L., 5–6, 8–9, 17, 19, 22–24, 34–36 Lin, X., 28–30, 29f, 31 Lindberg, S., 99–100 Lindsay, D.S., 166–167 Lindshield, C.J., 6–7 Linebarger, D.L., 221–222, 226 Linkersd€ orfera, J., 99–100 Linnet, K.M., 222 Linotte, S.S., 118, 139 Linz, D., 221–222 Liotti, M., 3–4 Lipian, M.S., 193, 195–196 Lipsitt, L.P., 136–137 Liston, C., 72–73 Liu, D., 172–173 Liu, H., 2–3, 22–23, 24, 27–28 Liu, S., 141–142 Lloyd-Fox, S., 7–8, 21–22, 38t Loftus, G.R., 190 Lohaus, A., 224 Longobardi, E., 202–203

288 Lonnemanna, J., 99–100 Looney, C.B., 34 Lorch, E.P., 225t, 226, 240 Lourenço, O., 252–253 Lourenco, S.F., 99–100, 103 Lucangeli, D., 99–100 Lucassen, N., 226 Luciana, M., 54–55, 68, 70 Luck, S.J., 108np Lucky, A.W., 63 Lugar, H.M., 6–7 Luke, N., 203–204 Luke, S.G., 38t, 39–40 Lusk, L., 5–6 Lutter, C.D., 58 Luyster, R.J., 140 Ly, R., 99–100 Lydon, D.M., 54, 59–62, 68–69 Lynam, D., 67–68 Lynne-Landsman, S.D., 67–68, 70 Lyons, I.M., 94–111 Lyons, K.E., 195

M Maass, A., 224, 252 Macchi Cassia, V., 121–122, 123–125, 140–141 MacDonald, A.N., 66, 71 Macdonald, K., 173–174 MacLean, K., 202 Macris, D.M., 159–160 Madrid, V., 130–131 Maertens, B., 103 Maes, H., 62, 82 Magnuson, K., 94, 221 Mahajan, R., 3–4, 5, 10–11 Makni, S., 9–10 Malone, L.S., 170–171 Malti, T., 250–272, 268f Manczack, E.M., 168–169 Mandal, P.K., 3–4, 5, 10–11 Mandler, G., 108np Mannon, L.J., 6–7, 24 Man-Shu, Z., 193, 195–196 Manuck, S.B., 66, 69, 70–71 Marceau, K., 54, 62, 82 Margie, N.G., 173–174 Markesbery, W.R., 24, 27–28

Author Index

Marklein, E., 67–68 Markman, E.M., 169 Marshall, W.A., 56–57 Martı´-Henneberg, C., 63 Martin, C.L., 190, 254–255 Marusˇicˇ, F., 109 Mascaro, O., 152–153, 154, 172–173, 176–177 Mash, C., 188–189 Masten, C.L., 70–71 Matsui, M., 41–42 Matthews, J.S., 221, 228–229 Matwin, S., 262 Maughan, A., 203–204 Maurer, D., 118, 121, 127, 130, 141, 142–143, 144–145 Mazoyer, B., 6–7, 22–23, 24, 27–28 Mazziotta, J.C., 3–4, 5, 10, 18–19, 70–71 Mazzocco, M.M., 99–100, 103 McAlister, A.R., 204–205 McAnulty, G.B., 33–34 McCarthy, G., 2–3, 139 McCarthy, R.A., 139 McCarthy, S.E., 99–100, 103 McCleery, J.P., 38t, 41 McClelland, M.M., 221, 228–229 McClure, E.B., 70–71 McCollum, J., 225–226, 225t McDade, T.W., 58 McDermott, J., 138–139 McDiarmid, M., 221 McGrew, K.S., 228–229 McGue, M., 77–78, 81 McGurk, H., 125–126 McHale, S.M., 77 McKee, E., 192 McKercher, D.A., 159 McKinstry, R.C., 7–11, 33–34 McKown, C., 256–257 McLaughlin, J., 202 McMains, S., 239 McMyler, B., 152–153 McNealy, K., 70–71 McNeil, N.M., 101, 103 Mcquaid, N., 202 Mechelli, A., 239 Mega, M.S., 6–7 Meins, E., 173–174, 202–203

289

Author Index

Meints, K., 173–174, 175–176 Melcher, D., 108np Meltzoff, A.N., 190, 195–196 Mendle, J., 56, 62–64, 65–66, 82 Menesini, E., 261 Mensah, F.K., 67–68 Mercier, H., 152–153, 154 Mercure, E., 26, 36–37 Mervis, C.B., 142–144 Meunier, J., 10–11 Mewes, A.U., 33–34 Michel, C.M., 41 Michel, F., 224 Michel, V., 103–104, 105, 106–107 Mierkiewicz, D., 95 Miesenb€ ock, G., 105 Miikkulainen, R., 122–123 Milani, I., 124–125 Miller, P.H., 200–201, 220 Miller, S., 67 Miller, S.A., 186, 192–193, 198, 200–201, 205 Millet, K., 161–162 Millman, D., 21–22 Mills, C.M., 191–192 Mills, K.L., 69–70, 72 Mills, S.L., 3–4, 5, 10 Milner, B., 3–4 Minkovitz, C.S., 222 Minkowski, A., 26–27 Mirza, M., 18–19, 20, 31 Mischel, W., 221, 228–229, 239 Mistry, K.B., 222 Mitchell, P., 204–205 Miyake, A., 220 Moffitt, T.E., 62–63, 67–68, 221, 235–236 Molenda-Figueira, H.A., 67–68, 75–76, 78–79, 79f, 80 Moll, K., 101 Mondloch, C.J., 118, 121, 127, 142–143 Montgomery, D.E., 192–193 Moore, C., 186 Morris, B.J., 160 Morris, C.A., 142–143 Morris, J.C.M., 6–7, 22–23, 24 Morris, N.M., 59 Morrison, F.J., 221, 228–229, 233 Mortimore, C., 199

Morton, J., 120–123, 120f Moses, L.J., 164, 200–201, 233 Moulson, M.C., 142 Moyer, R.S., 95, 96–97 Moyles, D.L., 66, 69, 70–71 Mrug, S., 67 Mueller, U., 220 Muetzel, R., 70 Mulkern, R.V., 7, 33–34 Mull, S.M., 188–189 Mulsow, M., 222 Mulvey, K.L., 197, 252, 253, 256, 258, 259, 260 Murray, M.M., 41 Mussolin, C., 103 Muzik, O., 5–7, 17 Myers, R., 18–19, 20

N Naiman, D.Q., 99–100 Naito, M., 204–205 Nakato, E., 139 Nakayama, K., 118, 138–139 Narr, K.L., 18–19, 20, 31 Nathanson, A.I., 222 Natsuaki, M.N., 67 Navarro, D.J., 174 Neale, M., 78 Needham, A., 143 Neelin, P., 5, 6–7, 10, 22–23, 24, 27–28 Neely, L.A., 163 Negriff, S., 67–68 Nelson, C.A., 2–3, 41–42, 130, 140, 141–142 Nelson, E.E., 70–71 Nelson, J.M., 230 Nesdale, D., 251–252, 256–257, 267 Nettle, D., 198 Newcombe, N.S., 167–168 Ng, R., 203–204 Nichols, J., 5 Nichols, J.F., 59 Nicholson, E., 141–142 Nicolaidou, P., 63 Nieder, A., 94–95, 98–99, 103–104, 105 Nigg, J.T., 77–78 NIH, 7–8 Noack, P., 265

290 Noam, G.G., 266 Nobes, A., 101 Noe¨l, M.P., 101 Nolen-Hoeksema, S., 78 Noll, D.C., 2 Noveck, I.A., 158 Nucci, L., 189, 190, 192, 197–198, 201–202, 205 Nurmsoo, E., 158, 164, 171–172 Nussbaum, M., 272 Nys, J., 103 Nystrom, L.E., 2

O O’Brien, K., 204–205 O’Craven, K., 2 O’Donnell, T., 109 O’Neill, D.K., 171–172 Oakes, L.M., 129–130, 135–137, 138, 141, 144 Obel, C., 222 Odic, D., 99–100 Ogura, T., 109 Ojala, K., 267 Okada, Y., 41 Okamoto, M., 41–42 Okazaki, H., 41–42 Olthof, T., 262–263 Olweus, D., 266 Ongley, S.F., 252–254, 260–261, 262 Ontai, L.L., 202 Op de Beeck, H.P., 106–107 Op de Macks, Z.A., 69, 77 Orban, G.A., 105 O’Reilly, H., 38t, 39–40 Orendi, J.L., 2 Origgi, G., 152–153, 154 O’Riordan, M., 200 Ornaghi, V., 202–203 Oskamp, S., 251 Ostapenko, L.J., 71–72, 81 Otsuka, Y., 139 Overgaauw, S., 69, 77

P Pagani, L.S., 222 Pagnin, A., 205–206 Palmeri, H., 38t, 164–165 Palmquist, C.M., 164

Author Index

Pantsiotou, S., 63 Papademetriou, M.D., 38t Papadimitriou, A., 63 Papathanassiou, D., 19 Parent, S., 62, 82 Park, J., 100–101 Parker, J.G., 259–260 Parkin, L., 204–205 Parnas, M., 105 Parsons, L.M., 3–4 Parsons, S., 94 Pascalis, O., 123–124, 130, 139–140, 141–142 Pasquini, E.S., 175–176 Patenaude, B., 9–10 Patton, G.C., 67–68 Pauen, M., 155 Paus, T., 33–34 Pears, K.C., 203–204 Pelletier, J., 257–258, 263–264 Pelphrey, K.A., 98–99, 188 Pempek, T.A., 222, 240 Peng, D., 30 Penner-Wilger, M., 106–107 Penny, W., 5 Peper, J.S., 72 Peplak, J., 267–268 Perez, E., 139 Perez-Edgar, K., 164 Perfors, A., 174 Peria, W., 190 Perner, J., 187, 195, 198, 204–205 Perusse, D., 6–7, 8–9, 10–11, 17 Pessoa, L., 106–107 Peters, T.M., 3–4, 5, 10 Petersen, A.C., 59, 63–64 Petersen, S.E., 6–7, 237–238 Peterson, C.C., 199, 204–205 Peterson, M., 67–68 Peterson-Badali, M., 80 Pettifer, J., 225t, 226 Pettigrew, T.F., 265 Peykarjou, S., 139–140 Pfefferbaum, A., 6–7, 24, 27–28 Pfeifer, J.H., 70–71 Phillips, M.C., 11, 19–20, 21–22 Phillips, M.L., 66, 69, 70–71 Piazza, M., 99–100, 103–104, 105, 106, 108np

Author Index

Pickard, K., 159 Pickard, M.B., 227np Pierpaoli, C., 7, 33–34 Pieters, S., 99–100 Pike, G.B., 33–34 Pillow, B.H., 186, 188–189 Pine, D.S., 70–71 Pinel, P., 103–104, 105, 106 Pintrich, P.R., 190–191 Plaisted, K., 200 Plate, R.C., 70 Pohl, R.F., 190 Poldrack, R.A., 105, 106–107 Pollak, S.D., 59 Ponitz, C.C., 221, 228–229, 233 Pons, F., 194, 195–197 Popescu, T., 106 Porter, D., 57f Posner, M.I., 237–238 Potter, R.F., 225t, 226–227, 240–242 Poulin-Dubois, D., 155–156 Poulton, R., 222 Pourtois, G., 241–242 Prastawa, M.W., 5–6, 17, 34 Pratt, M., 221–222 Prencipe, A., 200–201 Prenovost, M.A., 265 Price, C.J., 106, 239 Price, G.R., 101, 102–103 Priftis, K., 63 Pritchard, M., 205–206 Puce, A., 139 Purdy, P., 41–42 Putnam, S.P., 231 Pylyshyn, Z.W., 108np

Q Quas, J.A., 195–196 Quevedo, K.M., 71–72, 75, 81 Quine, W.V., 157–158 Quinn, B.T., 18–19 Quinn, P.C., 122, 130–131, 141–142 Quintana, S.M., 256–257

R Rabin, M.L., 6–7, 24 Racine, S.E., 78 Raichle, M.E., 235–236

291 Raikes, H.A., 202 Rainey, L., 3–4 Rajan, V., 167–168 Rajapakse, J.C., 2–3, 24 Rakison, D.H., 129–130, 141 Rakover, S.S., 118, 119, 132, 134, 137 Ram, N., 62, 82 Ramachandran, V., 253 Ramani, G.B., 109 Ramineni, C., 103 Ramirez, G., 109 Ramirez, J., 223 Ramscar, M., 229 Rao, S., 99–100 Rasmussen, E.E., 222 Ravaja, N., 241–242 Razza, R.P., 221 Read, A., 168 Recchia, H.E., 188–189, 190–191, 204–205 Redcay, E., 2, 42–43 Redfern, S., 202 Reed, A., 131–133, 140–141 Reeve, R.A., 99–100, 103 Regier, T., 105 Reid, T., 152–153 Reiser, M., 221 Renault, B., 139 Renna, M., 202–203 Repacholi, B., 202 Revkin, S.K., 100–101 Reynolds, G.D., 37–40, 38t, 41 Reynvoet, B., 101, 103, 105 Rhodes, G., 168 Riby, D.M., 142–143 Rice, K., 2, 42–43 Rice, M.L., 225t, 228, 240 Richards, C.A., 152–153 Richards, J.E., 2–4, 5–43, 8f, 12t, 38t, 44 Richards, J.M., 70 Richards, M., 59 Richards, T.R., 7–9, 19, 41 Richardson, C.B., 197, 265 Richardson, R.A., 203 Ridderinkhof, K.R., 237 Rideout, V.J., 221–222 Riggins, T., 2, 42–43 Rios, P., 199 Rivkin, M.J., 7–8, 9–10, 33–34

292 Robbins, R.A., 142–143 Roberts, K.P., 166–167 Robertson, M., 199 Robinson, E.J., 157–158, 164, 167, 171–172 Rodriguez, J., 188 Rodriguez, M.L., 221, 228–229, 239 Roebers, C.M., 195 Rogosch, F.A., 203–204 Rohwer, M., 195 Rolandelli, D., 226–227, 241 Romeo, R.D., 71 Ronfard, S., 196–197 Ronford, S., 196–197 Rose, A.J., 263 Rose, S., 140–141 Rosenbloom, M., 6–7, 24, 27–28 Ross, H.S., 188–189, 190–191, 204–205 Ross, R.P., 226–227, 241 Rossetti, Y., 103–104, 105, 110–111 Rossion, B., 118, 125, 127, 139, 144–145 Rothbart, M.K., 231, 239 Rovee-Collier, C., 136–137 Rowell, S., 160–161, 163–164 Royzman, E.B., 190 Rubenking, B., 227, 237, 239–241 Ruberry, E.J., 70–71 Rubin, K.H., 259–260 Ruble, D.N., 195–196, 254–255 Ruck, M.D., 265 Rudman, L., 265–266 Rueckert, D., 9–10, 18–19, 20, 33–34 Ruel, J., 126–127, 140–141 Ruff, H.A., 239 Ruffman, T., 160, 204–205 Rumain, B., 160 Russ, S.A., 222 Russell, J., 202 Rutherford, M.A., 33–34 Rutland, A., 250, 252, 253, 254, 256–258, 259, 263–264, 265–266 Ryan, A.T., 66, 71 Ryan, N.D., 66, 69, 70–71 Rybicki, F.J., 33–34 Ryf, S., 144–145

S Saalbach, H., 252–253 Sabbagh, M.A., 156–157, 170–171, 200–201, 233

Author Index

Sabuwalla, Z., 71 Sakamoto, K., 41–42 Salamon, G., 18–19, 20, 31 Salles, A., 205 Salmivalli, C., 266 Salt, J., 199 Sanchez, C., 6, 8–9, 10–11, 12t, 14, 25–27, 30–31, 32–33, 44 Sanders, J., 240 Sanderson, J.A., 152–153 Sanderson, P., 21–22 Sangrigoli, S., 126–127, 140–141 Sani, F., 256–257 Saracho, O., 190, 192, 197–198, 200–202, 205–206, 251–252, 257 Sargent, J.D., 221–222 Sarnecka, B.W., 109 Sarter, M., 239 Sarty, M., 120–121 Sasanguie, D., 101, 103 Sato, K., 6–7 Sav, A., 200 Saxe, R.R., 188 Sayfan, L., 188–189, 190, 192–193, 194–198, 200–202, 205 Scahill, V., 199 Schember, T.O., 67–68 Schettino, A., 241–242 Schlaggar, B.L., 6–7 Schleifer, P., 108np Schmidt, J., 38t, 39–40 Schmidt, M.E., 221–222 Schmithorst, V.J., 3, 5–6, 8–9, 17 Schmitt, F.A., 24, 27–28 Schmitt, K.L., 221–222, 226, 240 Schmitz, K.E., 59 Schneider, L.A., 171–172 Schneider, R., 196–197 Scholz, J., 188 Schubert, A.B., 2 Schug, M., 173–174 Schulz, K.M., 67–68, 75–76, 78–79, 79f, 80 Schuster, M.A., 67 Schwaninger, A., 144–145 Schwartz, N., 226–227, 241–242 Schwartzman, J.S., 199 Schwarzer, G., 127 Scofield, J., 170, 172 Scott, L.S., 130, 141–142

Author Index

Searcy, J., 132 Sebanz, N., 155 Seeley, J.R., 67 Segalowitz, S.J., 54–55 Segonne, F., 18–19 Seguin, J.R., 62, 82 Sekuler, R., 95 Semelman, M., 205 Sen, A., 272 Senju, A., 154–155, 199 Senn, T., 221 Sera, M., 158 Serventi, K.L., 6–7, 24, 27–28 Setia, A.R., 163 Shafman, D., 156–157, 170–171 Shafto, P., 174 Shakerian, A., 204–205 Shanahan, L., 67 Shannon, R.W., 141–142 Shapiro, D.I., 66, 71 Sharif, I., 221–222 Sharp, M.L., 222 Shattuck, D.W., 18–19, 20, 31 Shaw, L.A., 190–191 Shebo, B.J., 108np Sheffield, T., 230 Shen, C.G., 5–7, 17 Shen, D.G., 8–9, 22–24, 34–36 Shepard, S.A., 221 Shepherd, P.A., 125–126 Shi, F., 8–9, 19, 22–24, 34–36 Shimizu, K., 41–42 Shirtcliff, E.A., 54, 59 Shmueli-Goetz, Y., 202–203 Shoda, Y., 221, 228–229, 239 Shtulman, A., 161, 228 Shuman, M., 105 Shutts, K., 176 Siegle, G.J., 71–72, 81 Siegler, R.S., 101, 109 Sigman, M., 205 Silberg, J., 62, 82 Silk, J.S., 71–72, 81 Silva, P.A., 67–68 Simion, F., 121–125, 127, 130, 133–134, 134np, 140–141 Simon, G.M., 59 Singh, A.K., 41–42 Sinno, S., 173–174

293 Sirocco, K., 70 Sisk, C.L., 54–55, 59–62, 67–68, 75–76, 77–79, 79f, 80 Skajaa, E., 222 Slane, J.D., 77–78 Slater, A.M., 122, 123–124, 130, 141–142 Slaughter, V., 154–155, 199, 204–206 Sliva, D.D., 41 Slotnick, S.D., 41 Smetana, J.G., 173–174, 197, 252–254, 258, 260–261, 262 Smets, K., 101 Smith, A.R., 66 Smith, C.D., 24, 27–28 Smith, E.E., 2 Smith, E.P., 188 Smith, I., 220 Smith, J.K., 22–24, 27–28 Smith, P.K., 206 Smith, S.M., 5, 9–10, 17 Snyder, A.Z., 6–7 Snyder, A.Z.P.M.D., 6–7, 22–23, 24 Sobel, D.M., 155, 159–160 Sodian, B., 186 Soltesz, F., 101 Somerville, L.H., 68–69, 70–71 Song, A.W., 2–3 Song, G., 10–11 Sosa, E., 152–153 Southgate, V., 199 Sowell, E.R., 2–3, 5–6, 14–15, 22–23, 24, 72 Spataro, P., 202–203 Spear, L.P., 54, 68, 71 Spelke, E.S., 95, 97–98, 99–100, 103–104, 168, 228 Sperber, D., 152–153, 154, 172–173, 176–177 Spicer, P., 188 Spiel, C., 267–268 Spielberg, J.M., 72 Spinelli, L., 41 Spinrad, T.L., 221 Spodek, B., 190, 192, 197–198, 200–202, 205–206, 251–252, 257 Spong, A., 199 Srinivasan, L., 33–34 Stalets, M., 221 Stangor, C., 255

294 Starr, A., 99–100, 103 Steele, H., 202 Steele, M., 202 Steenhuis, A., 257 Stein, A., 224 Steinberg, L., 56–57, 59–63, 64–65, 66, 67–69, 70 Stenberg, G., 171–172 Stephens, B.R., 121 Stephens, E., 152–178 Stevens, M., 11, 19–20, 21–22 Stevens, T., 222 Stevenson, H.C., 188 Stolc, F., 225t, 226 Stone, V., 200 Stopin, A., 200 Strangman, G., 235–236 Strawsburg, R.H., 3 Streigel-Moore, R., 63 Strickwerda, M., 225t, 240–241 Strobino, D.M., 222 Strohmeier, D., 266 Sugden, N.A., 142 Sullivan, E.V., 6–7, 24, 27–28 Sullivan, J., 109 Summerlin, J.L., 3–4 Sumner, J., 225t, 240–241 Sun, B., 28–30, 29f, 31 Suarez, S., 152–178 Surtees, A., 38t Susman, E.J., 56–57, 59, 62–63, 64–65, 66, 67–68, 82 Sutherland, S.L., 169 Sutton, J., 206 Swettenham, J., 206 Swing, E.L., 222 Swingley, D., 156 Szu˝cs, D., 101

T Tager-Flusberg, H., 140, 186 Tajfel, H., 254, 256–257, 265 Takagishi, H., 204 Takeo, K., 41–42 Taki, Y., 6–7, 22–23, 24, 27–28 Talairach, J., 3–4, 5 Tamis-LeMonda, C.S., 144 Tamura, M., 235–236

Author Index

Tanaka, J.W., 118, 130–131, 141 Tang, S., 8–9, 34–36 Tang, Y., 28–30, 29f, 31 Tanner, J.M., 56–57, 62, 63, 65–66 Tardif, T., 169 Tarr, J.A., 70 Tarr, M.J., 118, 139 Tashjian, S., 186–207 Temple, E., 188 Terrace, H.S., 97 Thakkar, R., 222, 223 Thijs, J., 258–259 Thirion, B., 103–104, 105, 106–107 Thirion, J.P., 10–11 Thomas, K.M., 26, 36–37 Thompson, C.A., 101 Thompson, J.H., 235–236 Thompson, L.A., 130–131 Thompson, P., 132 Thompson, P.M., 5–7, 14–15, 22–23, 24 Thompson, R.A., 202 Thompson-Schill, S.L., 229 Thomsen, P.H., 222 Thomson, A., 26, 36–37 Tice, D., 223–224 To, Y.M., 192 Todorov, A.T., 168 Toga, A.W., 3–4, 5–6, 10, 14–15, 18–19, 22–23, 24 Toll, S.W., 101, 103 Tomasello, M., 172–173 Tong, F., 118, 138–139 Tooby, J., 176 Tortolero, S.R., 67 Toth, S.L., 203–204 Tottenham, N., 72–73 Tournoux, P., 3–4, 5 Trainor, R.J., 2 Trapolini, T., 202 Tredoux, C.G., 265 Tremblay, R.E., 62, 82 Trick, L.M., 108np Troiani, V., 103–104 Tropp, L.R., 265 Trotman, H.D., 66, 71 Trussardi, A.N., 99–100 Tsuzuki, D., 41–42 Tuckey, M., 202

295

Author Index

Tummeltshammer, K.S., 155 Turati, C., 122–125, 126–127, 130, 140–141 Turiel, E., 253, 254, 258 Turk-Browne, N.B., 226–227 Turner, J.C., 254, 256–257, 265 Turner, R., 2 Tustison, N.J., 10–11 Tzourio, C., 6–7, 22–23, 24, 27–28 Tzourio-Mazoyer, N., 19

U Uda, S., 41–42 Udry, J.R., 59 Uecker, A., 3–4 Uller, C., 225t, 226 Ullian, J.S., 157–158 Umilta`, C., 121–122, 123–125, 140–141 Urosˇevic´, S., 70

V Vaala, S.E., 221–222 Vaessen, A., 101, 102–103 Vaish, A., 172–173, 174–175 Vaituzis, A.C., 2–3, 5–6, 24 Valadian, I., 57f Valentine, T., 118, 119, 125 Valenza, E., 121–125, 140–141 van Anders, S.M., 58 van Cleve, J., 152–153 Van der Grond, J., 34 van der Meulen, M., 262–263 van der Molen, M.W., 230, 237 van Engeland, H., 5–6 van Honk, J., 72 Van Luit, J.E., 101, 103 van Noorden, T.H., 261–262 van Opstal, F., 103–104, 105 Van Osch, M.J., 34 van Zanten, J., 226 Vanderbilt, K.E., 172–173 VanderBorght, M., 159, 171–172 Vanderwert, R.E., 41–42 Vandewater, E.A., 221–222 vanMarle, K., 99–100, 101, 103 Velloso, R.D.L., 199 Verguts, T., 103–104, 105, 110–111 Verkuyten, M., 252, 256–257, 258–259

Verma, S., 188 Vermande, M.M., 262–263 Vetsa, Y.S., 34 Vincken, K.L., 34 Viner, R.M., 66, 68–69, 71, 72, 77 Viscomi, B., 2, 42–43 Vitaro, F., 62, 82 Vizmanos, B., 63 Vogel, E.K., 108np Vogel-Farley, V., 140 Vohns, K.D., 174–175 Vohs, K., 223–224 Volpe, J.J., 34 Vredenburgh, C., 171–172 Vuilleumier, P., 241–242

W Waber, D.P., 7, 9–10 Wachsmuth, I., 155 Wadsworth, S.J., 62, 63, 64–65, 82 Wagner, J.B., 140 Wai, Y.Y., 6–7 Wainryb, C., 190–191, 262 Wainwright, R., 202 Wake, M., 67–68 Walhovd, K.B., 2, 43 Walker, E.F., 71 Wallace, G.L., 22–23, 24, 27–28, 72 Wallien, M.S., 80 Walsh, D.A., 222 Walsh, V., 106 Wang, H., 34 Ware, E.A., 168–169 Warfield, S.K., 33–34 Wartella, E., 225t, 228, 240 Wartofsky, L., 58 Watanabe, E., 41–42 Watanabe, H., 41–42 Watanabe, S., 139 Watkins, B.A., 225t, 228, 240 Watkins, S., 222 Watling, D., 200, 206 Watson, J.S., 125–126, 187, 188–189 Watson, S.E., 101 Webster, G.D., 67–68 Weeks, L.A., 226–227, 241 Weil, J., 158 Weimer, A.A., 197–198

296 Weisenfeld, N.I., 33–34 Wekstein, D.R., 24, 27–28 Welch, D., 222 Weller, D., 197 Weller, R.A., 222 Wellman, H.M., 171–172, 186, 187, 188–189, 193, 194, 195–196, 197, 199 Wells, E.M., 22–23, 24, 27–28, 72 Werker, J.F., 130, 141 Westbrook, S., 130–131 Westling, E., 67–68 Whalen, D.J., 71–72, 81 Wheelwright, S., 199 Whitcombe, E.L., 164, 167 White, N., 202, 204–205 White, S., 199 Whitfield-Gabrieli, S., 188 Wiebe, S.A., 221, 230 Wilber, K., 22–24, 27–28 Wilke, M., 5–6, 8–9, 17 Williams, L., 228 Williams, M., 152 Willoughby, M.T., 230 Wills, T.A., 221–222 Wilmer, J.B., 99–100 Wilson, A.J., 100–101 Wilson, S.J., 54, 59–62, 68–69 Wimmer, H., 187, 198 Wirth, R., 230 Witzki, A.H., 220 Woldorff, M.G., 3–4 Wolters, C.H., 41 Wood, J.V., 188 Woodcock, R.W., 228–229 Woods, R.P., 6–7 Woodward, A.L., 156–157, 163, 174–175, 177–178, 197 Woolard, J., 59–62, 68–69 Woolley, J.D., 153, 161 Woolrich, M.W., 5, 9–10 Worchel, S., 254, 256–257, 265 Worsley, K., 33–34 Worthman, C.M., 59–62, 66, 69, 70–71, 78, 84 Wright, J.C., 190–191, 221–222, 225t, 226–228, 240, 241

Author Index

Wroblewski, L., 7–9, 19, 41 Wu, G., 8–9, 19 Wu, P., 120–121 Wu, R.W., 7–8, 21–22, 38t, 155 Wynn, K., 172–173, 227np

X Xie, W., 2–4, 5–43 Xu, F., 95, 200–201, 233 Xue, H., 33–34

Y Yakovlev, P.I., 26–27 Yamaguchi, M.K., 139 Yamana, Y., 109 Yan, C.G., 9–10, 44 Yao, L., 30 Yap, P.T., 8–9, 19 Yawkey, T.D., 188 Yerkes, R.M., 237–238 Yeung, E., 220 Yin, R., 118, 119 Yoon, U., 6–7, 8–9, 10–11, 17 Yudovina, Y.B., 109

Z Zack, E., 222 Zahn-Waxler, C., 54 Zalla, T., 200 Zˇaucer, R., 109 Zauner, N., 127 Zehr, J.L., 78–79, 80 Zelazo, P.D., 200–201, 220, 234 Zhang, Y.Y., 17 Zhou, S., 226–227, 241–242 Zhu, H., 23–24 Zieber, N., 7–8, 38t, 132 Zientara, G.P., 34 Zijdenbos, A., 2–3, 6–7, 22–23, 24, 27–28, 33–34 Zilles, K., 5, 10, 18–19 Zimmerman, F.J., 222 Ziv, T., 141–142 Zoumalan, C.I., 6–7 Zucker, K.J., 80 Zuffiano`, A., 267–268

SUBJECT INDEX Note: Page numbers followed by “f ” indicate figures and “t ” indicate tables.

A ABIDE. See Autism Brain Imaging Data Exchange (ABIDE) Adolescent development, 54–55. See also Puberty MRI templates, 12t pubertal influences brain development in, 72–73 pubertal status in, 68–72 pubertal timing in, 67–68 Age-specific templates, MRI, 9–10 ANCOVA, 230, 231 Appetitive motivation, 71–72, 75 Approximate number system, 100 Autism Brain Imaging Data Exchange (ABIDE), 9–10

B Brain activity, 37–42, 38t, 40f Brain average MRI templates, 16f Brain-behavior development, 42–43 Brain development in adolescent development, 72–73 children, 2 structural, volumetric analysis of, 22–28, 23f, 25f, 27f

C Cardinality principle, 108 Cerebrospinal fluid (CSF), 2–3 Child Behavior Questionnaire, 231, 235 Children’s evaluations executive function, 230–231 judgments, 252–253 moral emotions, 252–253 MRI templates, 12t social-cognitive competencies, 257 of speaker messages to coherence-checking, 154–156 of factual and episodic errors, 161–162 of grammatical errors, 159–160 illogical, 160–161

improbable statements, 160–161 of inconsistent, 160–161 of labeling errors, 156–159 message conflicts, 162–165 of speakers competence, 170–172 core dimensions of, 170–174 moral warmth, 172–174 natural pedagogy, 168–170 negativity bias, 174–177 and television media, 221–222 Chinese children age-specific reference data, 28–30, 32–33 brain and whole-head average MRI templates for, 28–30, 33f GM and WM function, 31 head and brain development in, 29f ICBM-152, 30 MRI templates, 28–33 neurostructural development, 28–33 and North American children, 31, 32 Cognitive development, 197–198 Cognitive function, 221 Cognitive tasks, 195 Coherence-checking, 154–156 Configural processing, 130–132 CSF. See Cerebrospinal fluid (CSF)

D Defensive and appetitive motivation, 71–72 Delay of gratification task, 228–229, 230 Dot-comparison task, 99–100 Dual-systems, 68–69

E EF. See Executive function (EF) Electroencephalogram (EEG), 37 Emotion understanding, 204 Epistemic vigilance, 152–154 Event-related potentials (ERP), 118, 139–140 297

298

Subject Index

Factual and episodic errors, 161–162 False-belief tasks, 187, 196–198, 206–207 Faux Pas test, 200 “FSL FAST” computer program, 17 Functional MRI (fMRI), 3 Functional near-infrared spectroscopy (fNIRS)., 234

Executive function (EF), 200–202, 220–221 children’s evaluations, 230–231 individual differences, ToM in middle childhood, 200–202 long-term media influences on, 222–224 results and characteristics of, 232t short-term studies of television, 224–226, 225t television, influence arousal, 241–242 attention, 239–240 encoding/processing, 240–241 negatively impact, 232 vs. television viewing, 233 Eye-tracking systems, 135–136

G

F

H

Face inversion effect definition of, 119 event-related potentials (ERPs), 118 experience matter, 143–144 exposure matter, 141–143 first year of life face preferences, 120–125, 120f face processing, 127–135 face recognition, 125–127 face-related neural responses, 138–140 infants’ scanning of faces, 135–138 functional magnetic resonance imaging (fMRI), 118 Face perception, 119, 127 Face preferences months and beyond, 124–125 newborns, 120–122, 120f newborns’ upright-face preference, 122–124 Face processing holistic processing, 127–130, 128f, 129f, 131f second-order configural processing, 130–132 Thatcher illusion, 132–135, 133f Face-related neural responses event-related potentials (ERP), 139–140 near-infrared spectroscopy (NIRS), 138–139 Face stimuli, 120f, 128f

Head and brain development, in Chinese children, 29f Heart rate (HR), 240 Holistic processing, 127–130, 128f, 129f, 131f Hormones ovarian hormones, 80 pubertal development, indices of, 58 puberty and brain changes, 85 and social experiences, 85 puberty-behavior links and behavior, 76–77 brain, 78–80, 79f gene expression, 77–78 salivary, 58 sex hormones, 55–57, 79f HR. See Heart rate (HR) Hypothalamic–pituitary–adrenal (HPA) axis, 71

Gene expression, 77–78 Grammatical errors, 159–160 Gray matter (GM), 5, 6–7, 15–16, 36f, 73f Group identity, developmental theories of, 254–256 Group inclusion judgments, 269f

I Individual differences, ToM in middle childhood executive function, 200–202 maltreatment, 203–204 parent–child interactions, 202–203 peer relationships, 205–206 siblings, 204–205 Individual-level models, 266–270

299

Subject Index

Infants. See also Children’s evaluations face preference, 140–141 scanning of faces, 135–138 Information Extracted from Medical Images database, 9–10 Integrating group-level, 266–270 Integrative clinical-developmental model, 261–262 Intergroup exclusion, 250, 251–253 International Consortium for Brain Mapping (ICBM), 5 Interpersonal victimization, 250, 251–253 Interpretation, 190–192 Interpretive diversity, 190–192 Interpretive theory of mind (IToM), 188–189 Intraparietal sulcus (IPS), 98f Inversion effect, 118–119. See also Face inversion effect IToM. See Interpretive theory of mind (IToM)

L Labeling errors, 156–159 Life span, neurostructural development, 25f LONI Probabilistic Brain Atlas project, 20

M Magnetic resonance imaging (MRI) applications brain activity, 37–42, 38t, 40f brain structural development, volumetric analysis of, 22–28, 23f, 25f, 27f in Chinese children, 28–33, 29f, 33f nonmyelinated axon tissue segmentation, in infants, 33–37, 35f, 36f brain-behavior development, 42–43 brain development, 2 three-dimensional (3D), 3 two-dimensional (2D), 3 Maltreatment, 203–204 Map number symbols, 108 Math skills, 94 McCausland Center for Brain Imaging (MCBI), 9–10 Media, 221–224

Menstrual cycle, 58 Montreal Neurological Institute (MNI), 3–4 Moral emotions, 253–254 Moral emotions clinical-developmental theory, 260–264 Morality, centrality of, 250–251 Moral judgments, 253–254 MRI templates. See also Magnetic resonance imaging (MRI) adolescent development, 12t age-specific templates, 9–10 brain average, 16f children’s evaluations, 12t Chinese children, 28–33, 33f pediatric, 5–9, 8f whole-head average, 15f Myelination, 2–3, 14–15

N NDE. See Numerical distance effect (NDE) Neurodevelopment and behavior, 2–4 structural development, 2–3 Neurodevelopmental MRI database access to, 21–22 pediatric MRI templates, 5–9, 8f stereotaxic atlas, 18–21, 21f tissue segmentation, 15–18, 16f, 18f web site, 23f 2 weeks to 89 years, average MRI templates from, 9–15, 12t, 15f Neuronal cell bodies, 33–34 NIH MRI Study of normal brain development (NIHPD), 7, 8–9 Nonmyelinated axons, 33–34 Nonmyelinated axonssegmented (NMA) tissue, 36f Nonmyelinated axon tissue segmentation, in infants, 33–37, 35f, 36f Nonsymbolic numerical magnitude, 99–100 NRE. See Numerical ratio effect (NRE) Number sense and mathematical achievement, 100 Number symbols, 102–103, 108 Numerical cognition, 111 Numerical distance effect (NDE), 96–97, 96f

300 Numerical magnitude approximate system for, 94–99, 98f distance and ratio, 95 intraparietal sulcus (IPS), 98f numerical distance effect (NDE), 96f numerical ratio effect (NRE), 96f symbolic and nonsymbolic representations of, 95f, 103–109 symbolic representation of, 99–103 Numerical ratio effect (NRE), 96–97, 96f

O Open Access Series of Imaging Studies (OASIS), 9–10 Ovarian hormones, 80 Overinterpretive mind error, 189

P Parent–child interactions, 202–203 Pediatric MRI templates, 5–9, 8f Peer relationships, 205–206 Peer victimization, 251–252 Physical disabilities, by age group and situational context, 268f Poor memory, 156–157 Preferential-looking paradigm, 121 Prefrontal cortex (PFC), 220, 236f Prejudice and victimization, 264–266 Prejudicial and discriminatory treatment, 250 Preschooler, 224, 226 Psychological-behavioral development, 2 Pubertal Development Scale (PDS), 59 Pubertal processes, 55–57, 57f Pubertal status, 58–62 Pubertal tempo, 63 Pubertal timing, 62–63 Puberty adolescent development, 54–55 brain development in, 72–73 pubertal status in, 68–72 pubertal timing in, 67–68 to behavior, 65–66 defining, 55 hormones and brain changes, 85 and social experiences, 85 measure of, 64–65

Subject Index

mechanisms and methods, 83–84 psychological development, 83 pubertal development, indices of hormone levels, 58 measures of, 60t pubertal status, 58–62 pubertal tempo, 63 pubertal timing, 62–63 subjective vs. objective, 63–64 pubertal processes, 55–57, 57f Puberty-behavior links adolescent change, nature of, 74 hormones and behavior, 76–77 brain, 78–80, 79f gene expression, 77–78 methodological issues age, 81–82 benefits and drawbacks, 82 nature and meaning of, 81 pubertal timing, 81 pubertal status and adolescent psychological change, 74–75

R Radio frequency (RF), 3 Risky decisions, 68–70

S Salivary hormones, 58 SC. See Skin conductance (SC) Scalp electrical activity, 37 School-entry math skills, 94 Secondary sex characteristics, 65–66 Second-order configural processing, 130–132 Selective learning, 157–158, 159 Selective trust, 165 Sex hormones, 55–57, 79f Siemens Tim Trio 3T scanner, 11 SIT. See Social identity theory (SIT) Skin conductance (SC), 240 Social and group identity, developmental theories of, 254–256 Social cognition, 186–187 Social-cognitive developmental changes, 258 Social exclusion, 251

301

Subject Index

Social identity theory (SIT), 254 Social reasoning developmental (SRD) model, 254–256 Social reorientation, 70–71 Source monitoring, 152 SRD model. See Social reasoning developmental (SRD) model Stereotaxic atlas label, 21f Stress reactivity, 68 Symbol-grounding problem, 111 Symbolic and nonsymbolic representations, numerical magnitude, 95f, 103–109

T Tanner stages, 56 Television media arousal, 241–242 attention, 239–240 children, 221–222 encoding/processing, 240–241 processing of, 226–228 Television viewing vs. executive function, 233 eye movements, 240 heart rate (HR), 240 information flow, 238f skin conductance (SC), 240 Testimony, 152 Thatcher illusion, 132–135, 133f Theory of mind (ToM), in middle childhood age-related improvements advances, 198–200 in emotional responses, 192–193 false-belief tasks, 196–198 integrate experiences, 194–195 interpretation, 190–192

mental states over time, 194–195 mind, interpretive understanding of, 188–190 thinking and emotions, 195–196 individual differences executive function, 200–202 maltreatment, 203–204 parent–child interactions, 202–203 peer relationships, 205–206 siblings, 204–205 lack of empirical investigation, 188 Third-order ToM, 198 Three-dimensional (3D) scan, 3 Tissue segmentation priors, 18f ToM. See Theory of mind (ToM) Transient-episodic claims, 163 Two-dimensional (2D) scan, 3

U Upright-face preference, 122–124

V Vascularization, 2–3 Victimization interpersonal, 250, 251–253 Peer victimization, 251–252 and prejudice, 264–266 Violation-detection device, 157–158 Vocabulary words, 232 Volumetric brain changes, 24

W Web site, neurodevelopmental MRI database, 23f White matter (WM), 5, 6–7, 14–16, 36f Whole-head average MRI templates, 15f


CONTENTS OF PREVIOUS VOLUMES VOLUME 1 Responses of Infants and Children to Complex and Novel Stimulation Gordon N. Cantor

Social Reinforcement of Children’s Behavior Harold W. Stevenson Delayed Reinforcement Effects Glenn Terrell

Word Associations and Children’s Verbal Behavior David S. Palermo

A Developmental Approach to Learning and Cognition Eugene S. Gollin

Change in the Stature and Body Weight of North American Boys during the Last 80 Years Howard V. Meredith

Evidence for a Hierarchical Arrangement of Learning Processes Sheldon H. White

Discrimination Learning Set in Children Hayne W. Reese Learning in the First Year of Life Lewis P. Lipsitt Some Methodological Contributions from a Functional Analysis of Child Development Sidney W. Bijou and Donald M. Baer The Hypothesis of Stimulus Interaction and an Explanation of Stimulus Compounding Charles C. Spiker The Development of “Overconstancy” in Space Perception Joachim F. Wohlwill Miniature Experiments in the Discrimination Learning of Retardates Betty J. House and David Zeaman Author Index–Subject Index

VOLUME 2 The Paired-Associates Method in the Study of Conflict Alfred Castaneda Transfer of Stimulus Pretraining to Motor Paired-Associate and Discrimination Learning Tasks Joan H. Cantor The Role of the Distance Receptors in the Development of Social Responsiveness Richard H. Walters and Ross D. Parke

Selected Anatomic Variables Analyzed for Interage Relationships of the Size-Size, Size-Gain, and Gain-Gain Varieties Howard V. Meredith Author Index–Subject Index

VOLUME 3 Infant Sucking Behavior and Its Modification Herbert Kaye The Study of Brain Electrical Activity in Infants Robert J. Ellingson Selective Auditory Attention in Children Eleanor E. Maccoby Stimulus Definition and Choice Michael D. Zeiler Experimental Analysis of Inferential Behavior in Children Tracy S. Kendler and Howard H. Kendler Perceptual Integration in Children Herbert L. Pick, Jr., Anne D. Pick, and Robert E. Klein Component Process Latencies in Reaction Times of Children and Adults Raymond H. Hohle Author Index–Subject Index

VOLUME 4 Developmental Studies of Figurative Perception David Elkind

303

304 The Relations of Short-Term Memory to Development and Intelligence John M. Belmont and Earl C. Butterfield Learning, Developmental Research, and Individual Differences Frances Degen Horowitz Psychophysiological Studies in Newborn Infants S.J. Hutt, H.G. Lenard, and H.F.R. Prechtl Development of the Sensory Analyzers during Infancy Yvonne Brackbill and Hiram E. Fitzgerald The Problem of Imitation Justin Aronfreed Author Index–Subject Index

VOLUME 5 The Development of Human Fetal Activity and Its Relation to Postnatal Behavior Tryphena Humphrey Arousal Systems and Infant Heart Rate Responses Frances K. Graham and Jan C. Jackson Specific and Diversive Exploration Corinne Hutt

Contents of Previous Volumes

Imitation and Language Development James A. Sherman Conditional Responding as a Paradigm for Observational, Imitative Learning and Vicarious-Reinforcement Jacob L. Gewirtz Author Index–Subject Index

VOLUME 7 Superstitious Behavior in Children: An Experimental Analysis Michael D. Zeiler Learning Strategies in Children from Different Socioeconomic Levels Jean L. Bresnahan and Martin M. Shapiro Time and Change in the Development of the Individual and Society Klaus F. Riegel The Nature and Development of Early Number Concepts Rochel Gelman Learning and Adaptation in Infancy: A Comparison of Models Arnold J. Sameroff

Developmental Studies of Mediated Memory John H. Flavell

Author Index–Subject Index

Development and Choice Behavior in Probabilistic and Problem-Solving Tasks L.R. Goulet and Kathryn S. Goodwin

VOLUME 8


VOLUME 6 Incentives and Learning in Children Sam L. Witryol

Elaboration and Learning in Childhood and Adolescence William D. Rohwer, Jr. Exploratory Behavior and Human Development Jum C. Nunnally and L. Charles Lemond

Habituation in the Human Infant Wendell E. Jeffrey and Leslie B. Cohen

Operant Conditioning of Infant Behavior: A Review Robert C. Hulsebus

Application of HulleSpence Theory to the Discrimination Learning of Children Charles C. Spiker

Birth Order and Parental Experience in Monkeys and Man G. Mitchell and L. Schroers

Growth in Body Size: A Compendium of Findings on Contemporary Children Living in Different Parts of the World Howard V. Meredith

Fear of the Stranger: A Critical Examination Harriet L. Rheingold and Carol O. Eckerman

305


Applications of HulleSpence Theory to the Transfer of Discrimination Learning in Children Charles C. Spiker and Joan H. Cantor

The Development of Selective Attention: From Perceptual Exploration to Logical Search John C. Wright and Alice G. Vlietstra



VOLUME 11 VOLUME 9 Children’s Discrimination Learning Based on Identity or Difference Betty J. House, Ann L. Brown, and Marcia S. Scott Two Aspects of Experience in Ontogeny: Development and Learning Hans G. Furth The Effects of Contextual Changes and Degree of Component Mastery on Transfer of Training Joseph C. Campione and Ann L. Brown Psychophysiological Functioning, Arousal, Attention, and Learning during the First Year of Life Richard Hirschman and Edward S. Katkin Self-Reinforcement Processes in Children John C. Masters and Janice R. Mokros

The Hyperactive Child: Characteristics, Treatment, and Evaluation of Research Design Gladys B. Baxley and Judith M. LeBlanc Peripheral and Neurochemical Parallels of Psychopathology: A Psychophysiological Model Relating Autonomic Imbalance to Hyperactivity, Psychopathy, and Autism Stephen W. Porges Constructing Cognitive Operations Linguistically Harry Beilin Operant Acquisition of Social Behaviors in Infancy: Basic Problems and Constraints W. Stuart Millar Mother–Infant Interaction and Its Study Jacob L. Gewirtz and Elizabeth F. Boyd


Symposium on Implications of Life-Span Developmental Psychology for Child Development: Introductory Remarks Paul B. Baltes

VOLUME 10

Theory and Method in Life-Span Developmental Psychology: Implications for Child Development Aletha Huston-Stein and Paul B. Baltes

Current Trends in Developmental Psychology Boyd R. McCandless and Mary Fulcher Geis The Development of Spatial Representations of Large-Scale Environments Alexander W. Siegel and Sheldon H. White Cognitive Perspectives on the Development of Memory John W. Hagen, Robert H. Jongeward, Jr., and Robert V. Kail, Jr. The Development of Memory: Knowing, Knowing About Knowing, and Knowing How to Know Ann L. Brown Developmental Trends in Visual Scanning Mary Carol Day

The Development of Memory: Life-Span Perspectives Hayne W. Reese Cognitive Changes during the Adult Years: Implications for Developmental Theory and Research Nancy W. Denney and John C. Wright Social Cognition and Life-Span Approaches to the Study of Child Development Michael J. Chandler Life-Span Development of the Theory of Oneself: Implications for Child Development Orville G. Brim, Jr.

306 Implication of Life-Span Developmental Psychology for Childhood Education Leo Montada and Sigrun-Heide Filipp Author Index–Subject Index

VOLUME 12 Research between 1960 and 1970 on the Standing Height of Young Children in Different Parts of the World Howard V. Meredith The Representation of Children’s Knowledge David Klahr and Robert S. Siegler Chromatic Vision in Infancy Marc H. Bornstein Developmental Memory Theories: Baldwin and Piaget Bruce M. Ross and Stephen M. Kerst Child Discipline and the Pursuit of Self: An Historical Interpretation Howard Gadlin Development of Time Concepts in Children William J. Friedman Author Index–Subject Index


The Economics of Infancy: A Review of Conjugate Reinforcement Carolyn Kent Rovee-Collier and Marcy J. Gekoski Human Facial Expressions in Response to Taste and Smell Stimulation Jacob E. Steiner Author Index–Subject Index

VOLUME 14 Development of Visual Memory in Infants John S. Werner and Marion Perlmutter Sibship-Constellation Effects on Psychosocial Development, Creativity, and Health Mazie Earle Wagner, Herman J.P. Schubert, and Daniel S.P. Schubert The Development of Understanding of the Spatial Terms Front and Back Lauren Julius Harris and Ellen A. Strommen

VOLUME 13

The Organization and Control of Infant Sucking C.K. Crook

Coding of Spatial and Temporal Information in Episodic Memory Daniel B. Berch

Neurological Plasticity, Recovery from Brain Insult, and Child Development Ian St. James-Roberts

A Developmental Model of Human Learning Barry Gholson and Harry Beilin


The Development of Discrimination Learning: A Levels-of-Functioning Explanation Tracy S. Kendler The Kendler Levels-of-Functioning Theory: Comments and an Alternative Schema Charles C. Spiker and Joan H. Cantor Commentary on Kendler’s Paper: An Alternative Perspective Barry Gholson and Therese Schuepfer

VOLUME 15 Visual Development in Ontogenesis: Some Reevaluations J€ uri Allik and Jaan Valsiner Binocular Vision in Infants: A Review and a Theoretical Framework Richard N. Aslin and Susan T. Dumais

Reply to Commentaries Tracy S. Kendler

Validating Theories of Intelligence Earl C. Butterfield, Dennis Siladi, and John M. Belmont

On the Development of Speech Perception: Mechanisms and Analogies Peter D. Eimas and Vivien C. Tartter

Cognitive Differentiation and Developmental Learning William Fowler

307


Children’s Clinical Syndromes and Generalized Expectations of Control Fred Rothbaum Author Index–Subject Index

VOLUME 16 The History of the Boyd R. McCandless Young Scientist Awards: The First Recipients David S. Palermo Social Bases of Language Development: A Reassessment Elizabeth Bates, Inge Bretherton, Marjorie Beeghly-Smith, and Sandra McNew Perceptual Anisotropies in Infancy: Ontogenetic Origins and Implications of Inequalities in Spatial Vision Marc H. Bornstein Concept Development Martha J. Farah and Stephen M. Kosslyn Production and Perception of Facial Expressions in Infancy and Early Childhood Tiffany M. Field and Tedra A. Walden Individual Differences in Infant Sociability: Their Origins and Implications for Cognitive Development Michael E. Lamb The Development of Numerical Understandings Robert S. Siegler and Mitchell Robinson Author Index–Subject Index

Word Meaning Acquisition in Young Children: A Review of Theory and Research Pamela Blewitt Language Play and Language Acquisition Stan A. Kuczaj II The Child Study Movement: Early Growth and Development of the Symbolized Child Alexander W. Siegel and Sheldon H. White Author Index–Subject Index

VOLUME 18 The Development of Verbal Communicative Skills in Children Constance R. Schmidt and Scott G. Paris Auditory Feedback and Speech Development Gerald M. Siegel, Herbert L. Pick, Jr., and Sharon R. Garber Body Size of Infants and Children around the World in Relation to Socioeconomic Status Howard V. Meredith Human Sexual Dimorphism: Its Cost and Benefit James L. Mosley and Eileen A. Stan Symposium on Research Programs: Rational Alternatives to Kuhn’s Analysis of Scientific Progress–Introductory Remarks Hayne W. Reese, Chairman

VOLUME 17

World Views and Their Influence on Psychological Theory and Research: Kuhn-Lakatos-Laudan Willis F. Overton

The Development of Problem-Solving Strategies Deanna Kuhn and Erin Phelps

The History of the Psychology of Learning as a Rational Process: Lakatos versus Kuhn Peter Barker and Barry Gholson

Information Processing and Cognitive Development Robert Kail and Jeffrey Bisanz

Functionalist and Structuralist Research Programs in Developmental Psychology: Incommensurability or Synthesis? Harry Beilin

Research between 1950 and 1980 on UrbaneRural Differences in Body Size and Growth Rate of Children and Youths Howard V. Meredith

In Defense of Kuhn: A Discussion of His Detractors David S. Palermo

308 Comments on Beilin’s Epistemology and Palermo’s Defense of Kuhn Willis F. Overton From Kuhn to Lakatos to Laudan Peter Barker and Barry Gholson Overton’s and Palermo’s Relativism: One Step Forward, Two Steps Back Harry Beilin Author Index–Subject Index

VOLUME 19 Response to Novelty: Continuity versus Discontinuity in the Developmental Course of Intelligence Cynthia A. Berg and Robert J. Sternberg Metaphoric Competence in Cognitive and Language Development Marc Marschark and Lynn Nall


Content Knowledge: Its Role, Representation, and Restructuring in Memory Development Michelene T.H. Chi and Stephen J. Ceci Descriptions: A Model of Nonstrategic Memory Development Brian P. Ackerman Reactivation of Infant Memory: Implications for Cognitive Development Carolyn Rovee-Collier and Harlene Hayne Gender Segregation in Childhood Eleanor E. Maccoby and Carol Nagy Jacklin Piaget, Attentional Capacity, and the Functional Implications of Formal Structure Michael Chapman Index

VOLUME 21

The Concept of Dimensions in Developmental Research Stuart I. Offenbach and Francine C. Blumberg

Social Development in Infancy: A 25-Year Perspective Ross D. Parke

Effects of the Knowledge Base on Children’s Memory Strategies Peter A. Ornstein and Mary J. Naus

On the Uses of the Concept of Normality in Developmental Biology and Psychology Eugene S. Gollin, Gary Stahl, and Elyse Morgan

Effects of Sibling Spacing on Intelligence, Interfamilial Relations, Psychosocial Characteristics, and Mental and Physical Health Mazie Earle Wagner, Herman J.P. Schubert, and Daniel S.P. Schubet Infant Visual Preferences: A Review and New Theoretical Treatment Martin S. Banks and Arthur P. Ginsburg

Cognitive Psychology: Mentalistic or Behavioristic? Charles C. Spiker Some Current Issues in Children’s Selective Attention Betty J. House


Children’s Learning Revisited: The Contemporary Scope of the Modified Spence Discrimination Theory Joan H. Cantor and Charles C. Spiker

VOLUME 20

Discrimination Learning Set in Children Hayne W. Reese

Variation in Body Stockiness among and within Ethnic Groups at Ages from Birth to Adulthood Howard V. Meredith The Development of Conditional Reasoning: An Iffy Proposition David P. O’Brien

A Developmental Analysis of Rule-Following Henry C. Riegler and Donald M. Baer Psychological Linguistics: Implications for a Theory of Initial Development and a Method for Research Sidney W. Bijou

309


Psychic Conflict and Moral Development Gordon N. Cantor and David A. Parton Knowledge and the Child’s Developing Theory of the World David S. Palermo Childhood Events Recalled by Children and Adults David B. Pillemer and Sheldon H. White Index

VOLUME 22 The Development of Representation in Young Children Judy S. DeLoache Children’s Understanding of Mental Phenomena David Estes, Henry M. Wellman, and Jacqueline D. Woolley Social Influences on Children’s Cognition: State of the Art and Future Directions Margarita Azmitia and Marion Perlmutter

The Development of World Views: Toward Future Synthesis? Ellin Kofsky Scholnick Metaphor, Recursive Systems, and Paradox in Science and Developmental Theory Willis F. Overton Children’s Iconic Realism: Object versus Property Realism Harry Beilin and Elise G. Pearlman The Role of Cognition in Understanding Gender Effects Carol Lynn Martin Development of Processing Speed in Childhood and Adolescence Robert Kail Contextualism and Developmental Psychology Hayne W. Reese Horizontality of Water Level: A Neo-Piagetian Developmental Review Juan Pascual-Leone and Sergio Morra Author Index–Subject Index

Understanding Maps as Symbols: The Development of Map Concepts Lynn S. Liben and Roger M. Downs

VOLUME 24

The Development of Spatial Perspective Taking Nora Newcombe

Music and Speech Processing in the First Year of Life Sandra E. Trehub, Laurel J. Trainor, and Anna M. Unyk

Developmental Studies of Alertness and Encoding Effects of Stimulus Repetition Daniel W. Smothergill and Alan G. Kraut Imitation in Infancy: A Critical Review Claire L. Poulson, Leila Regina de Paula Nunes, and Steven F. Warren Author Index–Subject Index

VOLUME 23 The Structure of Developmental Theory Willis F. Overton Questions a Satisfying Developmental Theory Would Answer: The Scope of a Complete Explanation of Development Phenomena Frank B. Murray

Effects of Feeding Method on Infant Temperament John Worobey The Development of Reading Linda S. Siegel Learning to Read: A Theoretical Synthesis John P. Rack, Charles Hulme, and Margaret J. Snowling Does Reading Make You Smarter? Literacy and the Development of Verbal Intelligence Keith E. Stanovich Sex-of-Sibling Effects: Part I. Gender Role, Intelligence, Achievement, and Creativity Mazie Earle Wagner, Herman J.P. Schubert, and Daniel S.P. Schubert

310 The Concept of Same Linda B. Smith Planning as Developmental Process Jacquelyn Baker-Sennett, Eugene Matusov, and Barbara Rogoff Author Index–Subject Index

VOLUME 25 In Memoriam: Charles C. Spiker (1925–1993) Lewis P. Lipsitt Developmental Differences in Associative Memory: Strategy Use, Mental Effort, and Knowledge Access Interactions Daniel W. Kee A Unifying Framework for the Development of Children’s Activity Memory Hilary Horn Ratner and Mary Ann Foley Strategy Utilization Deficiencies in Children: When, Where, and Why Patricia H. Miller and Wendy L. Seier The Development of Children’s Ability to Use Spatial Representations Mark Blades and Christopher Spencer Fostering Metacognitive Development Linda Baker The HOME Inventory: Review and Reflections Robert H. Bradley


The Interaction of Knowledge, Aptitude, and Strategies in Children’s Memory Performance David F. Bjorklund and Wolfgang Schneider Analogical Reasoning and Cognitive Development Usha Goswami Sex-of-Sibling Effects: A Review Part II. Personality and Mental and Physical Health Mazie Earle Wagner, Herman J.P. Schubert, and Daniel S.P. Schubert Input and Learning Processes in First Language Acquisition Ernst L. Moerk Author Index–Subject Index

VOLUME 27 From Form to Meaning: A Role for Structural Alignment in the Acquisition of Language Cynthia Fisher The Role of Essentialism in Children’s Concepts Susan A. Gelman Infants’ Use of Prior Experiences with Objects in Object Segregation: Implications for Object Recognition in Infancy Amy Needham and Avani Modi Perseveration and Problem Solving in Infancy Andreá Aguiar and Reneé Baillargeon

Social Reasoning and the Varieties of Social Experiences in Cultural Contexts Elliot Turiel and Cecilia Wainryb

Temperament and Attachment: One Construct or Two? Sarah C. Mangelsdrof and Cynthia A. Frosch

Mechanisms in the Explanation of Developmental Change Harry Beilin

The Foundation of Piaget’s Theories: Mental and Physical Action Harry Beilin and Gary Fireman



VOLUME 26 Preparing to Read: The Foundations of Literacy Ellen Bialystok The Role of Schemata in Children’s Memory Denise Davidson

VOLUME 28 Variability in Children’s Reasoning Karl S. Rosengren and Gregory S. Braswell Fuzzy-Trace Theory: Dual Processes in Memory, Reasoning, and Cognitive Neuroscience C.J. Brainerd and V.F. Reyna

311


Relational Frame Theory: A Post-Skinnerian Account of Human Language and Cognition Yvonne Barnes-Holmes, Steven C. Hayes, Dermot Barnes-Holmes, and Bryan Roche The Continuity of Depression across the Adolescent Transition Shelli Avenevoli and Laurence Steinberg The Time of Our Lives: Self-Continuity in Native and Non-Native Youth Michael J. Chandler Author Index–Subject Index

VOLUME 29 The Search for What is Fundamental in the Development of Working Memory Nelson Cowan, J. Scott Saults, and Emily M. Elliott

Sexual Selection and Human Life History David C. Geary Developments in Early Recall Memory: Normative Trends and Individual Differences Patricia J. Bauer, Melissa M. Burch, and Erica E. Kleinknecht Intersensory Redundancy Guides Early Perceptual and Cognitive Development Lorraine E. Bahrick and Robert Lickliter Children’s Emotion-Related Regulation Nancy Eisenberg and Amanda Sheffield Morris Maternal Sensitivity and Attachment in Atypical Groups L. Beckwith, A. Rozga, and M. Sigman Influences of Friends and Friendships: Myths, Truths, and Research Recommendations Thomas J. Berndt and Lonna M. Murphy Author Index–Subject Index

Culture, Autonomy, and Personal Jurisdiction in Adolescent–Parent Relationships Judith G. Smetana Maternal Responsiveness and Early Language Acquisition Catherine S. Tamis-Lemonda and Marc H. Bornstein Schooling as Cultural Process: Working Together and Guidance by Children from Schools Differing in Collaborative Practices Eugene Matusov, Nancy Bell, and Barbara Rogoff Beyond Prototypes: Asymmetries in Infant Categorization and What They Teach Us about the Mechanisms Guiding Early Knowledge Acquisition Paul C. Quinn Peer Relations in the Transition to Adolescence Carollee Howes and Julie Wargo Aikins Author Index–Subject Index

VOLUME 30 Learning to Keep Balance Karen Adolph

VOLUME 31 Beyond Point And Shoot: Children’s Developing Understanding of Photographs as Spatial and Expressive Representations Lynn S. Liben Probing the Adaptive Significance of Children’s Behavior and Relationships in the School Context: A Child by Environment Perspective Gary W. Ladd The Role of Letter Names in the Acquisition of Literacy Rebecca Treiman and Brett Kessler Early Understandings of Emotion, Morality, and Self: Developing a Working Model Ross A. Thompson, Deborah J. Laible, and Lenna L. Ontai Working Memory in Infancy Kevin A. Pelphrey and J. Steven Reznick The Development of a Differentiated Sense of the Past and the Future William J. Friedman

312


The Development of Cognitive Flexibility and Language Abilities Gedeon O. Dea´k

The Mechanisms of Early Categorization and Induction: Smart or Dumb Infants? David H. Rakison and Erin R. Hahn

A Bio-Social-Cognitive Approach to Understanding and Promoting the Outcomes of Children with Medical and Physical Disorders Daphne Blunt Bugental and David A. Beaulieu


Expanding Our View of Context: The Bio-ecological Environment and Development Theodore D. Wachs Pathways to Early Literacy: The Complex Interplay of Child, Family, and Sociocultural Factors Megan M. McClelland, Maureen Kessenich, and Frederick J. Morrison Author Index–Subject Index

VOLUME 32 From the Innocent to the Intelligent Eye: The Early Development of Pictorial Competence Georgene L. Troseth, Sophia L. Pierroutsakos, and Judy S. DeLoache Bringing Culture into Relief: Cultural Contributions to the Development of Children’s Planning Skills Mary Gauvain A Dual-Process Model of Adolescent Development: Implications for Decision Making, Reasoning, and Identity Paul A. Klaczynski The High Price of Affluence Suniya S. Luthar and Chris C. Sexton Attentional Inertia in Children’s Extended Looking at Television John E. Richards and Daniel R. Anderson Understanding Classroom Competence: The Role of Social-Motivational and Self-Processes Kathryn R. Wentzel Continuities and Discontinuities in Infants’ Representation of Objects and Events Rachel E. Keen and Neil E. Berthier

VOLUME 33 A Computational Model of Conscious and Unconscious Strategy Discovery Robert Siegler and Roberto Araya Out-of-School Settings as a Developmental Context for Children and Youth Deborah Lowe Vandell, Kim M. Pierce, and Kimberly Dadisman Mechanisms of Change in the Development of Mathematical Reasoning Martha W. Alibali A Social Identity Approach to Ethnic Differences in Family Relationships during Adolescence Andrew J. Fuligni and Lisa Flook What Develops in Language Development? LouAnn Gerken The Role of Children’s Competence Experiences in the Socialization Process: A Dynamic Process Framework for the Academic Arena Eva M. Pomerantz, Qian Wang, and Florrie Ng The Infant Origins of Intentional Understanding Amanda L. Woodward Analyzing Comorbidity Bruce F. Pennington, Erik Willcutt, and Soo Hyun Rhee Number Words and Number Concepts: The Interplay of Verbal and Nonverbal Quantification in Early Childhood Kelly S. Mix, Catherine M. Sandhofer, and Arthur J. Baroody Author Index–Subject Index

VOLUME 34 Mapping Sound to Meaning: Connections Between Learning About Sounds and Learning About Words Jenny R. Saffran and Katharine Graf Estes

313


A Developmental Intergroup Theory of Social Stereotypes and Prejudice Rebecca S. Bigler and Lynn S. Liben Income Poverty, Poverty Co-Factors, and the Adjustment of Children in Elementary School Brian P. Ackerman and Eleanor D. Brown I Thought She Knew That Would Hurt My Feelings: Developing Psychological Knowledge and Moral Thinking Cecilia Wainryb and Beverely A. Brehl Home Range and The Development of Children’s Way Finding Edward H. Cornel and C. Donald Heth The Development and Neural Bases of Facial Emotion Recognition Jukka M. Leppa¨nen and Charles A. Nelson Children’s Suggestibility: Characteristics and Mechanisms Stephen J. Ceci and Maggie Bruck The Emergence and Basis of Endogenous Attention in Infancy and Early Childhood John Colombo and Carol L. Cheatham The Probabilistic Epigenesis of Knowledge James A. Dixon and Elizabeth Kelley Author Index–Subject Index

VOLUME 35 Evolved Probabilistic Cognitive Mechanisms: An Evolutionary Approach to Gene Environment Development Interactions David F. Bjorklund, Bruce J. Ellis, and Justin S. Rosenberg Development of Episodic and Autobiographical Memory: A Cognitive Neuroscience Perspective Nora S. Newcombe, Marianne E. Lloyd and Kristin R. Ratliff

Children’s Experiences and Judgments about Group Exclusion and Inclusion Melanie Killen, Stefanie Sinno, and Nancy Geyelin Margie Working Memory as the Interface between Processing and Retention: A Developmental Perspective John N. Towse, Graham J. Hitch, and Neil Horton Developmental Science and Education: The NICHD Study of Early Child Care and Youth Development Findings from Elementary School Robert C. Pianta The Role of Morphology in Reading and Spelling Monique Seńećhal and Kyle Kearnan The Interactive Development of Social Smiling Daniel Messinger and Alan Fogel Author Index–Subject Index

VOLUME 36 King Solomon’s Take on Word Learning: An Integrative Account from the Radical Middle Kathy Hirsh-Pasek and Roberta Michnick Golinkoff Orthographic Learning, Phonological Recoding, and Self-Teaching David L. Share Developmental Perspectives on Links between Attachment and Affect Regulation Over the Lifespan Lisa M. Diamond and Christopher P. Fagundes Function Revisited: How Infants Construe Functional Features in their Representation of Objects Lisa M. Oakes and Kelly L. Madole

Advances in the Formulation of Emotional Security Theory: An Ethologically Based Perspective Patrick T. Davies and Melissa L. Sturge-Apple

Transactional Family Dynamics: A New Framework for Conceptualizing Family Influence Processes Alice C. Schermerhorn and E. Mark Cummings

Processing Limitations and the Grammatical Profile of Children with Specific Language Impairment Laurence B. Leonard

The Development of Rational Thought: A Taxonomy of Heuristics and Biases Keith E. Stanovich, Maggie E. Toplak, and Richard F. West

314 Lessons Learned: Recent Advances in Understanding and Preventing Childhood Aggression Nancy G. Guerra and Melinda S. Leidy The Social Cognitive Neuroscience of Infancy: Illuminating the Early Development of Social Brain Functions Mark H. Johnson, Tobias Grossmann, and Teresa Farroni Children’s Thinking is Not Just about What is in the Head: Understanding the Organism and Environment as a Unified System Jodie M. Plumert Remote Transfer of Scientific-Reasoning and Problem-Solving Strategies in Children Zhe Chen and David Klahr Author Index–Subject Index

VOLUME 37


Finding the Right Fit: Examining Developmentally Appropriate Levels of Challenge in Elicited-imitation Studies Melissa M. Burch, Jennifer A. Schwade, and Patricia J. Bauer Hearing the Signal Through the Noise: Assessing the Stability of Individual Differences in Declarative Memory in the Second and Third Years of Life Patricia J. Bauer, Melissa M. Burch, and Jennifer A. Schwade Declarative Memory Performance in Infants of Diabetic Mothers Tracy Riggins, Patricia J. Bauer, Michael K. Georgieff, and Charles A. Nelson The Development of Declarative Memory in Infants Born Preterm Carol L. Cheatham, Heather Whitney Sesma, Patricia J. Bauer, and Michael K. Georgieff

The Role of Dyadic Communication in Social Cognitive Development Maria Legerstee

Institutional Care as a Risk for Declarative Memory Development Maria G. Kroupina, Patricia J. Bauer, Megan R. Gunnar, and Dana E. Johnson

The Developmental Origins of Naı¨ve Psychology in Infancy Diane Poulin-Dubois, Ivy Brooker, and Virginia Chow

Declarative Memory in Abused and Neglected Infants Carol L. Cheatham, Marina Larkina, Patricia J. Bauer, Sheree L. Toth, and Dante Cicchetti

Children’s Reasoning About Traits Gail D. Heyman

Declarative Memory in Infancy: Lessons Learned from Typical and Atypical Development Patricia J. Bauer

The Development of Autobiographical Memory: Origins and Consequences Elaine Reese The Development of Temperament from a Behavioral Genetics Perspective Kimberly J. Saudino Developmental Changes in Cognitive control Through Adolescence Beatriz Luna Author Index–Subject Index

VOLUME 38 Declarative Memory In Infancy: An Introduction to Typical and Atypical Development Patricia J. Bauer


VOLUME 39 Poor Working Memory: Impact and Interventions Joni Holmes, Susan E. Gathercole, and Darren L. Dunning Mathematical Learning Disabilities David C. Geary The Poor Comprehender Profile: Understanding and Supporting Individuals Who Have Difficulties Extracting Meaning from Text Paula J. Clarke, Lisa M. Henderson, and Emma Truelove

315


Reading as an Intervention for Vocabulary, Short-term Memory and Speech Development of School-Aged Children with Down Syndrome: A Review of the Evidence Glynis Laws Williams Syndrome Deborah M. Riby and Melanie A. Porter Fragile X Syndrome and Associated Disorders Kim M. Cornish, Kylie M. Gray, and Nicole J. Rinehart Author Index–Subject Index

VOLUME 40 Autobiographical Memory Development From an Attachment Perspective: The Special Role of Negative Events Yoojin Chae, Gail S. Goodman, and Robin S. Edelstein

Andrea L. Barrocas, Jessica L. Jenness, Tchikima S. Davis, Caroline W. Oppenheimer, Jessica R. Technow, Lauren D. Gulley, Lisa S. Badanes, and Benjamin L. Hankin More Similarities Than Differences in Contemporary Theories of Social Development?: A Plea for Theory Bridging Campbell Leaper Monitoring, Metacognition, and Executive Function: Elucidating The Role of Self-Reflection in the Development of Self-Regulation Kristen E. Lyons and Philip David Zelazo Author Index–Subject Index

VOLUME 41

Links Between Attachment and Social Information Processing: Examination of Intergenerational Processes Matthew J. Dykas, Katherine B. Ehrlich, and Jude Cassidy

Positive Youth Development: Research and Applications for Promoting Thriving in Adolescence Richard M. Lerner, Jacqueline V. Lerner, and Janette B. Benson

The Development of Episodic Foresight: Emerging Concepts and Methods Judith A. Hudson, Estelle M.Y. Mayhew, and Janani Prabhakar

The Development of Intentional SelfRegulation in Adolescence: Describing, Explaining, and Optimizing its Link to Positive Youth Development Christopher M. Napolitano, Edmond P. Bowers, Steinunn Gestsdo´ttir, and Paul A. Chase

From Little White Lies to Filthy Liars: The Evolution of Honesty and Deception in Young Children Victoria Talwar and Angela Crossman A Model of Moral Identity: Applications for Education M. Kyle Matsuba, Theresa Murzyn, and Daniel Hart Cultural Patterns in Children’s Learning Through Keen Observation and Participation in their Communities Maricela Correa-Cha´vez, Amy L.D. Roberts, and Margarita Martıńez Pe´rez Family Relationships and Children’s Stress Responses Rachel G. Lucas-Thompson and Wendy A. Goldberg Developmental Perspectives on Vulnerability to Nonsuicidal Self-Injury in Youth

Youth Purpose and Positive Youth Development Jenni Menon Mariano and Julie Going Positive Pathways to Adulthood: The Role of Hope in Adolescents’ Constructions of Their Futures Kristina L. Schmid and Shane J. Lopez Intrinsic Motivation and Positive Development Reed W. Larson and Natalie Rusk School Engagement: What it is and Why it is Important for Positive Youth Development Yibing Li Religion, Spirituality, Positive Youth Development, and Thriving Pamela Ebstyne King, Drew Carr, and Ciprian Boitor

316


The Contribution of the Developmental Assets Framework to Positive Youth Development Theory and Practice Peter L. Benson, Peter C. Scales, and Amy K. Syvertsen

Social-Emotional Development Through a Behavior Genetics Lens: Infancy Through Preschool Lisabeth Fisher Dilalla, Paula Y. Mullineaux, and Sara J.W. Biebl

Youth Activity Involvement and Positive Youth Development Megan Kiely Mueller, Selva Lewin-bizan, and Jennifer Brown Urban

The Relation Between Space and Math: Developmental and Educational Implications Kelly S. Mix and Yi-Ling Cheng

Media Literacy and Positive Youth Development Michelle J. Boyd and Julie Dobrow

Testing Models of Children’s Self-regulation Within Educational Contexts: Implications for Measurement C. Cybele Raver, Jocelyn Smith Carter, Dana Charles Mccoy, Amanda roy, Alexandra Ursache, and Allison Friedman

Advances in Civic Engagement Research: Issues of Civic Measures and Civic Context Jonathan F. Zaff, Kei Kawashima-Ginsberg, and Emily S. Lin Shortridge Academy: Positive Youth Development in Action within a Therapeutic Community Kristine M. Baber and Adam Rainer Integrating Theory and Method in the Study of Positive Youth Development: The Sample Case of Gender-specificity and Longitudinal Stability of the Dimensions of Intention Self-regulation (Selection, Optimization, and Compensation) Alexander Von Eye, Michelle M. Martel, Richard M. Lerner, Jacqueline V. Lerner, and Edmond P. Bowers Author Index–Subject Index

Producing and Understanding Prosocial Actions in Early Childhood Markus Paulus and Chris Moore Food and Family: A Socio-Ecological Perspective for Child Development Barbara h. Fiese and Blake L. Jones Author Index Subject Index

VOLUME 43 The Probable and the Possible at 12 Months: Intuitive Reasoning about the Uncertain Future Nicolo` Cesana-Arlotti, Erno Te´gla´s and Luca L. Bonatti Probabilistic Inference in Human Infants Stephanie Denison and Fei Xu

VOLUME 42 Loneliness in Childhood: Toward the Next Generation of Assessment and Research Molly Stroud Weeks and Steven R. Asher Cognitive and Linguistic Correlates of Early Exposure to More than One Language Nameera Akhtar and Jennifer A. Menjivar The Legacy of Early Interpersonal Experience Glenn I. Roisman and R. Chris Fraley Some (But Not Much) Progress Toward Understanding Teenage Childbearing: A Review of Research from the Past Decade Claire A. Coyne and Brian M. D’onofrio

Reasoning about Instrumental and Communicative Agency in Human Infancy Gyo¨rgy Gergely and Pierre Jacob Can Rational Models Be Good Accounts of Developmental Change? The Case of Language Development at Two Time Scales Colin R. Dawson and LouAnn Gerken Learning about Causes from People and about People as Causes: Probabilistic Models and Social Causal Reasoning Daphna Buchsbaum, Elizabeth Seiver, Sophie Bridgers, and Alison Gopnik

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Rational Randomness: The Role of Sampling in an Algorithmic Account of Preschooler’s Causal Learning E. Bonawitz, A. Gopnik, S. Denison, and T.L. Griffiths Developing a Concept of Choice Tamar Kushnir When Children Ignore Evidence in Category-Based Induction Marjorie Rhodes A Number of Options: Rationalist, Constructivist, and Bayesian Insights into the Development of Exact-Number Concepts Barbara W. Sarnecka and James Negen Finding New Facts; Thinking New Thoughts Laura Schulz Unifying Pedagogical Reasoning and Epistemic Trust Baxter S. Eaves Jr. and Patrick Shafto The Influence of Social Information on Children’s Statistical and Causal Inferences David M. Sobel and Natasha Z. Kirkham The Nature of Goal-Directed Action Representations in Infancy Jessica A. Sommerville, Michaela B. Upshaw, and Jeff Loucks Subject Index Author Index

VOLUME 44 Relationism and Relational Developmental Systems: A Paradigm for Developmental Science in the Post-Cartesian Era Willis F. Overton Developmental Systems Theory: What Does It Explain, and How Does It Explain It? Paul E. Griffiths and James Tabery Emergence, Self-Organization and Developmental Science Gary Greenberg, Kristina Schmid, and Megan Kiely Mueller The Evolution of Intelligent Developmental Systems Ken Richardson

Embodiment and Agency: Toward a Holistic Synthesis for Developmental Science David C. Witherington and Shirley Heying The Origins of Variation: Evolutionary Insights from Developmental Science Robert Lickliter Cytoplasmic Inheritance Redux Evan Charney Evolutionary Psychology: a House Built on Sand Peter T. Saunders A ContemporaryView of Genes and Behavior: Complex Systems and Interactions Douglas Wahlsten Genetic Causation: A Cross Disciplinary Inquiry Sheldon Krimsky Pathways by which the Interplay of Organismic and Environmental Factors Lead to Phenotypic Variation within and across Generations Lawrence V. Harper Subject Index Author Index

VOLUME 45 Introduction: Embodiment and Epigenesis: A View of the Issues Richard M. Lerner and Janette B. Benson Dynamic Models of Biological Pattern Formation Have Some Surprising Implications for Understanding the Epigenetics of Development Peter C.M. Molenaar and Lawrence Lo A Developmental Systems Approach to Executive Function Ulrich M€ uller, Lesley Baker, and Emanuela Yeung No Genes for Intelligence in the Fluid Genome Mae-Wan Ho The Lost Study: A 1998 Adoption Study of Personality That Found No Genetic Relationship between Birthparents and Their 240 Adopted-Away Biological Offspring Jay Joseph

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A Relational Developmental Systems Approach to Moral Development Jeremy I.M. Carpendale, Stuart I. Hammond, and Sherrie Atwood

Development of Adaptive Tool-Use in Early Childhood: Sensorimotor, Social, and Conceptual Factors Gedeon O. Dea´k

Adolescent Rationality David Moshman

Edge Replacement and Minimality as Models of Causal Inference in Children David W. Buchanan and David M. Sobel

Developing through Relationships: An Embodied Coactive Systems Framework Michael F. Mascolo Multiple Trajectories in the Developmental Psychobiology of Human Handedness George F. Michel, Eliza L. Nelson, Iryna Babik, Julie M. Campbell, and Emily C. Marcinowski Positive Movement Experiences: Approaching the Study of Athletic Participation, Exercise, and Leisure Activity through Relational Developmental Systems Theory and the Concept of Embodiment Jennifer P. Agans, Reidar Sa¨fvenbom, Jacqueline L. Davis, Edmond P. Bowers, and Richard M. Lerner Integration of Culture and Biology in Human Development Jayanthi Mistry Author Index Subject Index

VOLUME 46 Demystifying Internalization and Socialization: Linking Conceptions of How Development Happens to OrganismicDevelopmental Theory Catherine Raeff Adolescents’ Theories of the Commons Constance Flanagan and Erin Gallay LGB-Parent Families: The Current State of the Research and Directions for the Future Abbie E. Goldberg and Nanette K. Gartrell The Impact of Parental Deployment to War on Children: The Crucial Role of Parenting Abigail H. Gewirtz and Osnat Zamir Shining Light on Infants’ Discovery of Structure Jennifer K. Mendoza and Dare Baldwin

Applying Risk and Resilience Models to Predicting the Effects of Media Violence on Development Sara Prot and Douglas A. Gentile Bringing a Developmental Perspective to Early Childhood and Family Interventionists: Where to Begin Anne E. Hogan and Herbert C. Quay Vocabulary Development and Intervention for English Learners in the Early Grades Doris Luft Baker, Stephanie Al Otaiba, Miriam Ortiz, Vivian Correa, and Ron Cole Author Index Subject Index

VOLUME 47 Motivation in Educational Contexts: Does Gender Matter? Ruth Butler Gender-Related Academic and Occupational Interests and Goals Jennifer Petersen and Janet Shibley Hyde Developmental Interventions to Address the STEM Gender Gap: Exploring Intended and Unintended Consequences Lynn S. Liben and Emily F. Coyle Physical Education, Sports, and Gender in Schools Melinda A. Solmon Gendered-Peer Relationships in Educational Contexts Carol Lynn Martin, Richard A. Fabes, and Laura D. Hanish Sexism in Schools Campbell Leaper and Christia Spears Brown Analysis and Evaluation of the Rationales for Single-Sex Schooling

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Rebecca S. Bigler, Amy Roberson Hayes, and Lynn S. Liben Factors Affecting Academic Achievement Among Sexual Minority and GenderVariant Youth V. Paul Poteat, Jillian R. Scheer, and Ethan H. Mereish Framing Black Boys: Parent, Teacher, and Student Narratives of the Academic Lives of Black Boys

Stephanie J. Rowley, Latisha Ross, Fantasy T. Lozada, Amber Williams, Adrian Gale, and Beth Kurtz-Costes Creating Developmentally Auspicious School Environments for African American Boys Oscar A. Barbarin, Lisa Chinn, and Yamanda F. Wright Author Index Subject Index