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HUMOV 1615

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16 November 2013 Highlights

Demonstrates interrelations between fine motor skills and cognitive performance. Reveals executive functions as underlying. Earlier fine motor skills predict academic achievement 1 and 2 years later. His effect is negligible when executive functions are included in the prediction.

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Contents lists available at ScienceDirect

Human Movement Science journal homepage: www.elsevier.com/locate/humov 7 8

The relation between cognitive and motor performance and their relevance for children’s transition to school: A latent variable approach

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Claudia M. Roebers ⇑, Marianne Röthlisberger, Regula Neuenschwander, Patrizia Cimeli, Eva Michel, Katja Jäger

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Center for Cognition, Learning, and Memory, University of Bern, Switzerland

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a r t i c l e

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Article history: Available online xxxx

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Keywords: Motor control Fine motor skills Cognitive performance Executive function Academic achievement School readiness

a b s t r a c t Both theoretically and empirically there is a continuous interest in understanding the specific relation between cognitive and motor development in childhood. In the present longitudinal study including three measurement points, this relation was targeted. At the beginning of the study, the participating children were 5–6-year-olds. By assessing participants’ fine motor skills, their executive functioning, and their non-verbal intelligence, their cross-sectional and cross-lagged interrelations were examined. Additionally, performance in these three areas was used to predict early school achievement (in terms of mathematics, reading, and spelling) at the end of participants’ first grade. Correlational analyses and structural equation modeling revealed that fine motor skills, non-verbal intelligence and executive functioning were significantly interrelated. Both fine motor skills and intelligence had significant links to later school achievement. However, when executive functioning was additionally included into the prediction of early academic achievement, fine motor skills and non-verbal intelligence were no longer significantly associated with later school performance suggesting that executive functioning plays an important role for the motor-cognitive performance link. Ó 2013 Elsevier B.V. All rights reserved.

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⇑ Corresponding author. Address: Center for Cognition, Learning, and Memory, University of Bern, Hochschulzentrum vonRoll, Fabrikstrasse 8, 3012 Bern, Switzerland. E-mail addresses: [email protected], [email protected] (C.M. Roebers). 0167-9457/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.humov.2013.08.011

Please cite this article in press as: Roebers, C. M., et al. The relation between cognitive and motor performance and their relevance for children’s transition to school: A latent variable approach. Human Movement Science (2013), http://dx.doi.org/10.1016/j.humov.2013.08.011

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1. Introduction

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Theoretically, the notion that motor and cognitive development are closely intertwined goes back to Gesell’s maturational theory, a biological perspective assuming that physical, motor, and cognitive development alike are determined primarily by biological predispositions (Gesell & Thompson, 1934). Similarly, according to Piaget’s cognitive-developmental theory, motor and cognitive development are strongly related and driven by heredity: in his view, a child’s unfolding motor skills give rise to increasing possibilities to explore and understand the environment, leading to more and more differentiated cognitive structures (Piaget & Inhelder, 1966). In the present paper, a latent variable approach will be presented exploring (a) the relation between motor and cognitive performance (in terms of intelligence and executive functioning) longitudinally and (b) their relative contributions to early academic achievement in a sample of 5–6-year-olds. There has been an unchanging curiosity about the relation between cognitive and motor development, especially in young children, providing different lines of evidence supporting the theoretical assumption of a relationship between cognitive and motor development: Research documents that both aspects follow similar developmental timetables, with an accelerated progression in the kindergarten and elementary school years and a protracted development into adolescence (e.g., Ahnert, Schneider, & Bös, 2009). Delayed or atypical motor development typically co-occurs with certain cognitive deficits and vice versa (e.g., Piek et al., 2004). And, there is increasing neurophysiological and neuroimaging evidence that the prefrontal cortex, the cerebellum, and the connecting structures (among others the basal ganglia) get co-activated in certain cognitive and motor tasks (for an overview see Diamond, 2000). Thelen and Adolph outlined a dynamic system theory based on which motor development is a domain of development in which general principles such as, adapting task mastery from feedback loops, using motor actions for generating new information, and prospective control of motor and cognitive actions, is acquired (Adolph & Berger, 2006; Thelen & Smith, 1998). Moreover, with an increasing interest in prerequisites for a successful transition into school, that is, when searching for reliable and valid indicators of young children’s school readiness, especially fine motor skills (especially manual dexterity/hand-eye-coordination) have been found to be substantial predictors for academic achievement in the first elementary school years (Bart, Hajami, & Bar-Haim, 2007; Grissmer, Grimm, Aiyer, Murrah, & Steele, 2010; Luo, Jose, Huntsinger, & Pigott, 2007; Pagani, Fithpatrick, Archambault, & Janosz, 2010; Son & Meisels, 2006). And finally, not only individual differences in early academic achievement appear to be related to earlier fine motor skills, but also precursors of mathematics and literacy, such as letter knowledge, phonological awareness, and number sense in kindergarten are specifically predicted by preschoolers’ fine motor skills (Cameron et al., 2012). Despite these different lines of evidence suggesting an association between motor and cognitive development, relatively little theoretical progress has been made trying to explain the underlying mechanisms leading to this relation. In the following paragraphs, we will outline two possibilities that are – among others – discussed in the literature and will then, with our own data, seek for empirical support of these two different assumptions. For one, according to Piaget’s theoretical assumptions development in different domains is driven by a general biological factor that may explain why motor and cognitive performance are substantially associated. Moreover, developing (thus improving) motor skills (such as locomotion or manual dexterity) give rise to the formation and differentiation of cognitive concepts (such as object permanence or tool use), which in turn will affect a child’s examination and manipulation of her or his environment, suggesting a reciprocal relation between cognitive and motor development. From this perspective, one might assume that general cognitive abilities may explain the relation between motor performance and, for example, academic performance. Support for this perspective is provided in the Munich Longitudinal Study (LOGIC; Weinert & Schneider, 1999). In that study, indicators of intelligence (i.e., verbal intelligence) and motor performance were matched onto latent variables (allowing error-free estimations) and revealed a substantial, cross-sectional link between general motor and intellectual performance in kindergarten children accounting for 40% of the explained variance (Schneider, 1993). Moreover, both cross-lagged paths (from earlier verbal intelligence to later motor skills and from earlier motor skills to later intelligence) were reliable pointing to a bi-directional relationship

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between these two domains. More recently, cross-sectional correlations of r = .41 to .69 between global cognitive (including fluid and crystallized intelligence) and motor scores (including fine motor skills, balancing, and strength) in 4–6-year-olds were reported by Davis, Pitchford, and Limback (2011). Rhemtulla and Tucker-Drob (2011) showed that individual differences in cognitive, linguistic but also motor performance shared 42% of the variance, even after controlling for influential background characteristics (age, SES etc.), proposing a common factor of cognitive (especially crystallized aspects of intelligence) and motor abilities. However, as in these studies no more specific indicators of brain maturation were available, an alternative, more specific explanation cannot be ruled out. Following the line of argumentation of a maturational factor underlying performance and development, one might also expect a more specific theoretical account for the cognitive motor link: maturation of brain areas that are typically activated during both certain cognitive and motor tasks (the prefrontal cortex, the cerebellum and their connecting structures, the basal ganglia and the striatum) is responsible for this association, with executive functioning (EF) being the behavioral correlate (e.g., Diamond, 2000; Fuster, 2008). On the one side, supporting evidence for this specific relationship stems from the Northern Finland 1966 Birth Cohort Study documenting very long-term significant links between infants reaching important developmental motor milestones (such as standing and walking) and scholastic achievements at age 8 (Murray, Jones, Kuh, & Richards, 2007; see also Piek, Dawson, Smith, & Gasson, 2008), reading comprehension at age 23 (Murray et al., 2007), two subdomains of executive functioning at age 33 (i.e., categorization; Murray et al., 2006), and verbal fluency at age 53 (Murray et al., 2007). In an interesting attempt to delineate these links, a small but representative subsample of this Northern Finland 1966 Birth Cohort Study was drawn for a neuroimaging study (MRI). Results revealed that early developmental motor milestones were associated with increased gray matter density in the adult premotor cortex, the striatum and the cerebellum and, at the same time, with increased white matter density in the frontal lobe (Ridler et al., 2006). On the other side, the assumption of a specific EF-driven relationship between motor and cognitive development is also supported by cross-sectional studies including typically developing participants of different ages. For example, Wassenberg et al. (2005) found in their cross-sectional sample of 5–6-year-olds that general verbal and non-verbal cognitive ability was not related to motor performance but rather that attention, working memory, and verbal fluency – central aspects of EF – were substantially associated with a global measure of motor performance (including fine and gross motor skills). Rigoli, Piek, Kane, and Oosterlaan (2012) most recently reported a significant overlap between motor coordination and EF in adolescents, with this link being mainly driven by an association between manual dexterity and inhibition. Supporting evidence also stems from a study in which EF and motor skills in young children were found to be even more closely interrelated when tasks were new (Roebers & Kauer, 2009). Together, the existing evidence supports the idea of EF as a specific underlying factor of the motor-cognitive performance link. However, as in most of these studies no measures of general cognitive abilities were included and no cross-lagged relations between motor and cognitive performance were tested, additional empirical evidence supporting this EF-hypothesis is still needed. In the present paper, these two different accounts for the motor-cognitive-association will be examined. A longitudinal study including kindergarten children is presented in which measures of intelligence, fine-motor skills, and EF were included. While previous longitudinal studies have mostly included measures of motor skills and general cognitive abilities (Ahnert et al., 2009) or have focused on early developmental motor milestones (Murray et al., 2006, 2007), the present approach allows to more specifically illuminate the contribution of EF in this relation. A focus on fine rather than gross motor skills (or even aspects of strength) seemed warranted as the most recent studies reported that the motor-cognitive link is mainly driven by fine motor skills, or, in a more circumscribed sense manual dexterity (Cameron et al., 2012; Grissmer et al., 2010; Rigoli et al., 2012). Additionally, while a synchronic (cross-sectional) interrelation between EF and fine motor skills has consistently been established in young children (Davis et al., 2011; Roebers & Kauer, 2009; Wassenberg et al., 2005), the diachronic (longitudinal) nature of this connection, controlling for general cognitive ability, is yet to be explored. Finally, although fine-motor skills and EF have both been found to be powerful predictors of school readiness and of subsequent academic achievement (Blair & Diamond, 2008; Cameron et al., 2012; Please cite this article in press as: Roebers, C. M., et al. The relation between cognitive and motor performance and their relevance for children’s transition to school: A latent variable approach. Human Movement Science (2013), http://dx.doi.org/10.1016/j.humov.2013.08.011

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Grissmer et al., 2010), their relative importance when simultaneously being included in the prediction of early academic performance remains unclear. Grissmer et al. (2010) who reported a joint analysis of 3 large longitudinal data sets recently addressed this issue: The relative predictive power of finemotor skills compared to measures of ‘‘attention’’, a core aspect of EF, varied across data sets, leaving the question open whether the executive demands of fine-motor tasks were responsible for the significant link between motor skills and academic achievement or whether there is a unique contribution of fine-motor skills for later achievement. Moreover, since no data on intelligence were available in these data sets, one might also assume that individual differences in intelligence shared variance with both fine-motor skills and attentional skills. There are a few studies suggesting that dimensions of EF, such as inhibition and working memory, mediate the relationship between motor skills and school achievement (Alloway & Archibald, 2008; Alloway & Temple, 2007; Rigoli et al., 2012). Therefore, we also addressed the relative predictive value of these constructs compared to each other and to intelligence for mathematics and literacy in the end of children’s first grade in the present study.

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2. Methods

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2.1. Overview

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A large sample of 5–6-year-olds were followed over 2 consecutive years (t1, t2, and t3 covering 3 academic years: preschool, kindergarten, first grade) and assessed in terms of intelligence, fine motor skills, and EF at the first two measurement points (preschool and kindergarten), and in terms of early school achievement (mathematics, reading and spelling) at the third measurement point (end of children’s first grade). Intelligence was measured with standardized non-verbal intelligence tests. Fine motor skills were selected as previous studies revealed that fine but not necessarily gross motor skills are related to cognitive performance (Cameron et al., 2012; Davis et al., 2011; Grissmer et al., 2010; Rhemtulla & Tucker-Drob, 2011; Rigoli et al., 2012). Fine motor skills were operationalized as manual dexterity as it is widely considered as core dimension of fine motor skills and to be of central importance in school (Bart et al., 2007; Cameron et al., 2012; Davis et al., 2011; Luo et al., 2007; Pagani et al., 2010). EF was assessed with three different child-appropriate tasks, all of which targeted central dimensions of EF.

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2.2. Sample

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The sample consisted of 169 preschool children (45.6% girls) aged 5–6 years (M = 69.4 months; SD = 4.28 months) at the first measurement point (53 additional preschoolers were excluded because of missing data over the 3 measurement points. There were, however, no systematic differences between children with and without three completed assessments). Children were recruited through their institutions in different Swiss provinces and participated in the study if parents gave written consent. Children with firm diagnosis of disorders (e.g., ADHD, DCD, language impairment) were not included in the sample from the beginning on. The homogeneity of the resulting sample in terms of ethnicity (>97% White), and the diversity in terms of parental education was representative for Switzerland. Seventy-six percent of the children were reported by their parents to be native Swiss or German/Austrian, 6% were bilingual and 16% were non-native German speaking immigrants from Eastern and Southern European countries (2% missing information). All participants were sufficiently fluent in German. At the second measurement 12 months later, participants were in kindergarten, and at the third and last measurement, children had completed the transition into first grade.

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2.3. Procedure and materials

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Towards the end of each school year, children were tested individually during the morning hours in a quiet room at their institution. Assessments were divided in two 30-min sessions, realized on 2 different days. The order of tasks was counterbalanced.

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Fine motor skills were assessed by the manual dexterity scale (3 tasks at each assessment) from the Movement Assessment Battery for Children 2 (M-ABC-2; Henderson, Sugden, & Barnett, 2007), with the tasks slightly differing as children progressed into adjacent age bands of the test battery: posting coins into a box with a slot (t1)/placing pegs into holes (t2), threading beads on a lace (t1)/threading lace through holes in a board (t2), and a drawing trail task (t1/narrower trial at t2). The dependent variable in the threading beads and the threading lace task was the amount of time needed to complete the task. In the posting coin and the placing pegs task, the average amount of time needed to complete two trials of the task (one with the dominant and one with the non-dominant hand), and in the drawing trail task, the number of errors (touching or crossing the lines) served as dependent variable. Three different tasks tapping EF were included: In each trial of the Cognitive Flexibility task (e.g., Neuenschwander, Cimeli, Röthlisberger, & Roebers, 2012; Roebers & Kauer, 2009; Roebers, Röthlisberger, Cimeli, Michel, & Neuenschwander, 2011), two fish were presented simultaneously on a screen. Children were told that there are two families of fish (colored and plain) and that they have to feed the two families consecutively by pressing the button at the side where the to-be-fed fish appeared. As the position of the to-be-fed fish was randomized children had to change the response dimension trial after trial and thus had to continuously update the relevant response dimension. The task contained 46 trials with a short break in the middle; inter-stimuli-intervals varied from 300 to 700 ms. As dependent variable the percentage of accurate responses was used. Split-half reliability was r = .72. The second EF task included was a Fruit-Stroop task (Archibald & Kerns, 1999; Roebers et al., 2011), which contained four pages displaying 25 stimuli each. On the first page, squares in four different colors were presented and children had to name the correct color as quickly as possible. On the second page, four different fruits in their original color were presented (congruent trial), followed by a third page displaying the same fruits in black and white and children were asked to name the original colors. The last page showed the fruits in incongruent colors (incongruent trial) and children had to name the original colors of the fruits. As introduced by Archibald and Kerns, the dependent variable was the measure of interference, accounting for individual differences in time to task completion under lower inhibitory demands by subtracting time to task completion in the first pages of the task from the high interference trials (last two pages of the task). The third EF task was the Backwards Color Recall task (Roebers et al., 2011), comparable to a classical backward digit recall task. Children were asked to memorize a sequence of differently colored discs (1s/item) and to recall the colors in reverse order. Testing started with a two-item sequence. Every time the child recalled at least two of three sequences with a particular length correctly, the sequence length was increased by one item. As dependent variable the number of correctly recalled sequences was used. [As an aside, analyses of variance with repeated measures were conducted on the three EF tasks and consistently revealed substantial improvements over the course of 1 year (Cognitive Flexibility: F (1, 168) = 94.40, gp2 = .36, p < .001; Fruit-Stroop: F (1, 168) = 50.86, gp2 = .23, p < .001; Backwards Color Recall: F (1, 168) = 79.11, gp2 = .32, p < .001), with the Cognitive Flexibility and the Backwards Recall showing a stronger improvement compared to the Stroop task.] In order to deal with the problem that any EF task triggers different EF aspects at one time and also triggers non-EF tasks (e.g., spatial conflict, hand-eye-coordination; i.e., ‘‘task impurity’’ problem, see Miyake & Friedman, 2012), we applied confirmatory factor analyses and structural equation modeling techniques in which we mapped the EF tasks onto only one latent EF variable. This EF variable then represents only the shared EF processes inherent in these tasks. Academic achievement in the domain of math and literacy was measured with standardized tests. Mathematical achievement was assessed by four subtests of the Heidelberger Rechentest (HRT 1–4; Haffner, Baro, Parzer, & Resch, 2005): Quantity Comparison; Equations, Sequences, and Addition/Subtraction. Writing was assessed by a spelling task, in which children had to write down 22 words and one sentence read aloud. Reading was assessed by two tests (Würzburger Leise Lese Probe, WLLP; Küspert & Schneider, 1998; Salzburger Lese-Screening, SLS, Mayringer & Wimmer, 2003) in which children were asked to judge the accuracy of the sentence or to search for a match meaning. Please cite this article in press as: Roebers, C. M., et al. The relation between cognitive and motor performance and their relevance for children’s transition to school: A latent variable approach. Human Movement Science (2013), http://dx.doi.org/10.1016/j.humov.2013.08.011

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Table 1 Descriptive statistics of the included variables. M (SD)

Min.–Max.

46.0 (9.2) 19.9 (2.1) 2.1 (1.9)

27.0–83.5 14.9–27.5 0–10

32.7 (6.6) 35.8 (5.7) 7.8 (5.4)

20.8–59.6 24.9–61.2 0–27

3.1 (2.3) 46.2 (19.6) 58.9 (16.0)

0–9 10.7–124.7 26–95

Backward Recall (correct answers) Fruit-Stroop (interference) Cognitive Flexibility (% correct answers)

4.7 (2.2) 36.1 (13.1) 70.6 (15.6)

0–9 13.5–91.0 32–98

Intelligence t1

CFT-1 (raw scores)

4.9 (2.1)

0–11

t2

TONI-3 (raw scores)

10.3 (3.9)

2–22

35.8 (11.8) 124 (15.5) 56 (29.0)

3–63 53–149 6–161

Fine motor skills (M-ABC-2) t1 Threading Beads (s) Posting Coins (s) Drawing Trail (errors) t2

Threading Lace (s) Placing Pegs (s) Drawing Trail (errors)

Executive functioning t1 Backward Recall (correct answers) Fruit-Stroop (interference) Cognitive Flexibility (% correct answers) t2

Academic achievement t3 Maths (correct answers) Spelling (correct graphemes) Reading (correct answers)

Note: CFT-1 = culture-fair intelligence test; M-ABC-2 = movement assessment battery for children 2; t1, t2, and t3 = measurement points 1, 2, and 3, respectively; TONI-3 = test of non-verbal intelligence.

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Intelligence was assessed by the classification-subtest from the culture-fair intelligence test – scale 1 (CFT-1; Cattell, Weiss, & Osterland, 1997) at the first measurement point and by the test of non-verbal intelligence (TONI-3; Brown, Sherbenou, & Johnsen, 1997) at the second measurement point as performance of the CFT-1 was already approaching ceiling effects at t1. Both tests focus on the fluid, non-verbal aspects of intelligence and the test tasks are very similar (e.g., select the correct pattern for the presented sequence of stimuli out of four alternatives). Verbal intelligence tests were not included in order to avoid an inflation of the effect of intelligence for indicators of school achievement that included, of course, verbal processes (over and above the verbal instructions).

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2.4. Statistical analyses

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In Table 1, descriptive statistics of the included measures are presented. For the analyses reported below, the dependent measures were z-standardized to receive identical metrics across tasks. Indicators of fine motor skills were reversed so that higher values represent superior performance for all included variables. Outliers in the data (±4 standard deviations of the samples’ mean) were replaced with the value that corresponded to the fourth standard deviation of that variable. Structural equation modeling was realized using AMOS 19 software (Arbuckle, 2010). The model’s fit was assessed as good when the Tucker-Lewis index (TLI) and the comparative fit index (CFI) were greater than .95, the rootmean-square (RMSEA) smaller or equal than .06, and the normalized v2-value smaller than 2 (Byrne, 2001; Hu & Bentler, 1998). Two structural equation models were generated (see Figs. 1 and 2). This was done in order to assess the interrelations between fine motor skills, intelligence, and EF on academic achievement, respectively. For both models, any path between any two variables were allowed and estimated and the models were fully recursive in that (a) paths directed only in one direction following the timeline of the study and (b) any variable assessed at an earlier point in time was used to

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Fig. 1. Structural equation model testing the interrelations between and predictive power of fine motor skills and intelligence among each other and on academic achievement. TB = Threading Beads; TL = Threading Lace; PC = Posting Coins; PP = Placing Pegs; DT = Drawing Trail; t1, t2, and t3 = measurement point 1, 2, and 3, respectively; ⁄p < .05; ⁄⁄p < .001. Dashed lines represent non-significant paths, solid lines represent significant paths.

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predict any later variable. Covariances between the residuals of the corresponding EF and motor tasks between t1 and t2 were allowed, as these were identical or nearly identical tasks.

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3. Results

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As a first step, correlations among the aggregated measures (to simplify presentation of interrelations) were computed and are presented in Table 2 (Appendix A presents the correlational matrix of the indicators). Below the diagonal, Pearson correlations are presented; above the diagonal, partial correlations controlling for differences in chronological age are depicted. Correlations were highest when 1-year stabilities of motor skills and EF and time-lagged correlations between EF at t2 and academic achievement at t3 were considered. As can be seen in Table 2, fine motor skills were substantially associated to both EF and intelligence at both measurement points (approx. 18% and 8% shared variance) thereby confirming previous findings. Controlling for the differences in chronological age did not substantially affect the overall pattern of interrelations; minimal decreases in the magnitude of the associations were observed ruling out the possibility that differences in chronological age were a substantial driving force for these relations. Against the background of these significant interrelations and in order to address the theoretically important question whether these different indicators are empirically separable and represent the three theoretically distinguishable concepts of fine motor skills, intelligence, and EF already in this young sample, confirmatory factor analysis were conducted testing whether a 1-factor solution would represent the data better than the theoretically assumed 3-factor solution. When all indicators were mapped onto one single latent variable, the resulting fit of the model with the data was unsatisfactory (v2 = 30.78, df = 14, p < .01, normalized v2 = 2.20, CFI = .90, TLI = .84, RMSEA = .08), while a 3-factor solution with fine motor skills, intelligence and EF as separate factors resulted in a very good model’s

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Fig. 2. Structural equation model testing the interrelations between and the predictive power of fine motor skills, intelligence, and executive functioning among each other and on academic achievement. TB = Threading Beads; TL = Threading Lace; PC = Posting Coins; PP = Placing Pegs; DT = Drawing Trail; BR = Backward Recall; FS = Fruit-Stroop; CF = Cognitive Flexibility; t1, t2, and t3 = measurement point 1, 2, and 3, respectively; ⁄p < .05; ⁄⁄p < .001. Dashed lines represent non-significant paths, solid lines represent significant paths.

Table 2 Pearson Correlations among the included variables below the diagonal; partial correlation controlling for age above the diagonal.

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

Fine motor skills t1 Fine motor skills t2 Executive functioning t1 Executive functioning t2 Non-verbal IQ t1 Non-verbal IQ t2 Academic achievement t3

1.

2.

3.

4.

5.

6.

7.

– .58 .40 .46 .30 .27 .39

.57 – .35 .46 .27 .32 .43

.35 .33 – .66 .37 .43 .47

.39 .44 .63 – .37 .50 .55

.26 .26 .34 .34 – .34 .40

.21 .30 .40 .47 .31 – .43

.35 .42 .44 .52 .38 .40 –

Note: t1, t2, and t3 = measurement points 1, 2, and 3, respectively. All correlations are significant at p < .05. Correlations higher than .26 are significant at p < .001.


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fit (v2 = 10.70, df = 12, p = .56, normalized v2 = .89). Not surprisingly, the v2 difference test was also significant (p < .001) showing that the 3-factor solution much better mirrors the underlying data. Therefore in the analyses reported below, we assume the 3-factor solution. Next, the links between fine motor skills and intelligence cross-sectionally, longitudinally, and their relative importance for early academic achievement were investigated. Cross-lagged paths were also addressed to explore whether earlier fine motor skills predicted later intelligence or vice versa, over and above the constructs’ stability (auto-correlations). Fig. 1 presents the model that tested the stability and predictive power of fine motor skills on academic achievement, controlling for intelligence. In this model, fine motor skills were indexed by Posting Coins, Threading Beads, and Drawing Trail at the first measurement point and by Placing Pegs, Threading Lace, and Drawing Trail at the second measurement point. Intelligence was indexed by two different non-verbal intelligence scales at the first and second measurement, respectively. Academic achievement was indexed by 4 subtests of mathematics, one spelling test and two reading tests. As can be seen in Fig. 1, all factor loadings on the latent variables were significant at p < .001 (in order to facilitate presentation of results, most residuals are not shown in the figure). The model had a good fit to the data, with v2 (35, N = 169) = 55.49, p = .02, normalized v2 = 1.59, CFI = .96, TLI = .93, and RMSEA = .06. Fine motor skills and intelligence were significantly interrelated in preschool and both variables proved to be stable from preschool to kindergarten. [Because of the significant multicollinearity of the predictors, the paths from fine motor skills and intelligence at t1 onto the criterion were not included in the model.] Considering the cross-lagged paths, Fig. 1 reveals that earlier fine motor skills had a significant effect on later intelligence (1 year later), but earlier intelligence had no significant effect on later fine motor skills. Fine motor skills as well as intelligence in kindergarten were significant predictors for academic achievement in 1st grade. Additionally, we also addressed the question of a longer-term predictive power of fine motor skills and intelligence. This was done by estimating a structural equation model in which fine motor skills and intelligence as latent variables in preschool predicted school achievement in the end of first grade, that is, 2 years later. In other words, we estimated another model leaving data of the second measurement out. Interestingly, the pattern of the paths remained identical: The path of fine motor skills was somewhat stronger (.42) compared to the path of intelligence to school achievement (.30). Thus, both fine motor skills and intelligence were found to explain significant amounts of variance in school achievement, even over a 2 year delay. In the next step of the analyses, EF was additionally included in the model (Fig. 2). EF was indexed by the Backward Color Recall, the Fruit-Stroop, and the Cognitive Flexibility task at both measurement points. As can be seen, all factor loadings of the indicators onto their latent variables were significant at p < .001. Because of pronounced multicollinearity of the predictors (as identical or nearly identical tasks were used to build the same latent variables), the paths from t1 to t3 were not included and estimated in the model. The model reached a very good fit to the data, with v2 (97, N = 169) = 127.54, p = .02, normalized v2 = 1.31 CFI = .96, TLI = .95, RMSEA = .04. Fine motor skills, intelligence, and EF in preschool covaried significantly with each other, with the highest coefficient observed between fine motor skills and EF. Of relevance, fine motor skills and executive functioning were highly stable from preschool to kindergarten, decreasing the possibility for additional and significant cross-lagged correlations. EF in preschool seemed to be significantly related to intelligence 1 year later, while under the inclusion of EF earlier fine motor skills were no longer significantly related to later intelligence, as found and presented in Fig. 1. Additionally, the paths from fine motor skills and intelligence in kindergarten to academic achievement in 1st grade were no longer significant when EF was included into the model (Fig. 2). Rather, EF proved to be the only significant predictor for academic achievement in the end of children’s first grade. All other paths in the structural model were not significant. Following up on this strong predictive effect of EF for achievement, we addressed EF’s long-term predictive power over a 2 year delay, thus, from participants’ preschool to first grade. As was done for the model depicted in Fig. 1, another model similar to that depicted in Fig. 2 was estimated, leaving out the data of the second measurement testing the longer-term predictive power of EF for school achievement (over a 2 year period of time) when simultaneously including fine motor skills and intelligence in the model. This model, too, had a very good fit to the data (v2 = 40.83, df = 30, p = .09, normalized v2 = 1.36, CFI = .97, TLI = .96, RMSEA = .05). Identical to the model depicted in Fig. 2, results Please cite this article in press as: Roebers, C. M., et al. The relation between cognitive and motor performance and their relevance for children’s transition to school: A latent variable approach. Human Movement Science (2013), http://dx.doi.org/10.1016/j.humov.2013.08.011

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revealed a strong and substantial effect of EF on school achievement 2 years later (.53), and two nonsignificant, negligible paths from preschool fine motor skills (.17) and preschool intelligence (.15) on school achievement 2 years later. Finally, when the included measures for mathematics and literacy (three tasks each) were used separately for building a latent variable of mathematic and literacy achievement, respectively, the models’ fit were still very good. Interestingly however, these additional analyses revealed that the strong path from earlier EFt2 to later school achievement was mainly fuelled by mathematics; that is, the path from earlier EFt2 to later (t3) literacy was only .51, p < .05, compared to .83 for school achievement in more general terms.

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4. Discussion

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In the present longitudinal study including 5–6-year-olds (at the beginning of the study) and 3 measurement points in 2 consecutive years, the relation between cognitive and motor development and their relative contribution to early academic achievement was explored. By assessing participants’ fine motor skills, their executive functioning (EF), and their non-verbal intelligence at the first and second measurement, the cross-sectional and cross-lagged interrelations between these constructs were examined. Performance in these three areas was then used to predict early school achievement another year later (in terms of mathematics and literacy at the end of participants’ first grade). With the help of structural equation modeling the unique contributions of fine motor skills, EF, and general cognitive abilities were estimated. Starting with the relationship between fine motor skills and intelligence in young children, the present study revealed a substantial association in preschool and kindergarten (see Table 2 and Appendix A for the interrelations on the task level). The cross-sectional and the time-lagged correlations (on single-task level and on the level of aggregated measures) were significant suggesting bidirectional associations between the included variables. From this perspective, our results seem to confirm previous studies (Ahnert et al., 2009; Davis et al., 2011; Rhemtulla & Tucker-Drob, 2011), with the amount of shared variance between fine motor skills and our measures of non-verbal intelligence being altogether somewhat lower compared to these previous studies. One reason for this discrepancy may lie in the current operationalization of cognitive abilities in particular, as measures of verbal intelligence were not included. Broader measurement of cognitive abilities including verbal intelligence will most likely lead to stronger associations between motor and cognitive performance. On the level of latent variables, the link between fine motor skills and non-verbal intelligence was significant at the first measurement only; due to the high stabilities of the variables (especially of the fine motor skills) the detection of substantial cross-sectional correlations at later time points was (technically) relatively unlikely. When addressing the cross-lagged links between fine motor skills and non-verbal intelligence (t1 ? t2), both only the structural link between earlier fine motor skills and later non-verbal intelligence reached significance. Although both cross-lagged paths were not very strong and the path from earlier intelligence on later fine motor skills just missed significance, the error-free estimation of these links on the level of latent variables suggested a dominance of fine motor skills in this relationship (see Fig. 1). Thus, the theoretical assumption of a reciprocal relationship between cognitive and motor development was only partially confirmed in the present study. A similar pattern of inter-correlations between fine motor skills and cognitive abilities was obtained when executive functioning was additionally taken into consideration. There were significant correlations between all three constructs at the first and the second measurement (Table 2 and Appendix A). And also in the structural equation model, all three concurrent links were substantial at the first measurement point, but most of them were negligible 1 year later – when children were in kindergarten. These substantial links between the three included constructs are in line with previous studies including typically developing participants (Davis et al., 2011; Rigoli et al., 2012; Roebers & Kauer, 2009; Wassenberg et al., 2005). They may be interpreted as mirroring individual differences in the structural and functional connectivity in the frontal lobe, cerebellum, striatum, and basal ganglia (Diamond, 2000; Ridler et al., 2006) that may already be detectable early in development (Murray et al., 2006, 2007). Individual differences in processing speed may also be responsible for the links; this

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interpretation is, however, weakened as in our study speed-dependent (Stroop task, Posting Coins) but also accuracy-oriented (Drawing Trail, Backward Color Recall) tasks for both EF and fine motor skills were included. One might also argue that attentional skills are responsible for the link as attention is certainly involved in all three domains. Whether and how attention is separable from the EF construct used in the present study is a general, important but yet unanswered question (Miyake & Friedman, 2012) that was also beyond the scope of the present study. We would like to argue that mastery of a speed-accuracy trade-off, inherent in many motor coordination and EF tasks but also in many intelligence tests may account for the shared variances, at least to some extent (Roebers & Kauer, 2009). As to the cross-lagged links between fine motor skills, executive functioning, and non-verbal intelligence on the level of latent variables, only one significant path between earlier executive functioning and later non-verbal intelligence was found. All other cross-lagged links were not substantial. Again, an interpretation of high stabilities (especially of fine motor skills and executive functioning) absorbing main sources of variances in the model is very probable. When comparing the paths coefficients between Figs. 1 and 2 (in which executive functioning was additionally integrated) the substantial impact of executive functioning as a source of common variance becomes visible. This may again suggest that executive processes (including information processing speed, attention, and/or the mastery of speed-accuracy trade-offs) are shared mechanisms involved in both fine motor tasks and intelligence tests. The second major aim of the present study was to estimate the predictive power of kindergarteners’ fine motor skills, executive functioning, and cognitive abilities for first graders’ academic achievement. Based on the correlational analyses, these three predictors appeared to be about equally important, sharing between 25% and 36% of the variance (see Table 2). When fine motor skills and non-verbal intelligence were included in the structural equation model predicting academic achievement in the end of first grade both predictors yielded substantial longitudinal links to the outcome measure (Fig. 1). In other words, fine motor skills explained significant amounts of variance in children’s academic achievement, over and above non-verbal intelligence suggesting that fine motor skills in kindergarten may be considered as an indicator of school readiness (Grissmer et al., 2011). The unique contribution of fine motor skills for later academic achievements may lie in the mastery of school demands, some of which will also imply the ability to maintain goal-directed behavior (Marcovitch, Boseovski, & Knapp, 2007), the mastery of an accuracy-speed-trade-off (see above; Roebers & Kauer, 2009), and/or task mastery motivation (Blair & Diamond, 2008). These findings confirm and extend previous studies (Grissmer et al., 2010; Luo et al., 2007; Pagani et al., 2010) in so far as in the present approach individual differences in intelligence were taken into account and yet, a unique and substantial effect of fine motor skills on school achievement was found. However, the importance of fine motor skills for the prediction of academic outcomes decreased to a non-significant link when executive functioning was simultaneously taken into account. Only executive functioning proved to be a reliable predictor when fine motor skills, non-verbal intelligence and executive functioning were integrated in one model (see Fig. 2). This pattern also held true when predicting school achievement with EF, fine motor skills, and intelligence over a 2 year period of time (leaving out the data of the second measurement point). It is especially surprising how strong the link between EF and academic achievement turned out to be, given the extended delays and the different nature of the included tasks. More detailed modeling of school achievement (separating mathematics and literacy) further revealed that although EF were strong predictors of both included school domains, the path from EF to literacy was less strong. Results suggest that executive functioning serves as a common domain-general factor underlying the motor-cognitive performance link, with a special emphasis on mathematic achievement. The present study confirms existing empirical evidence stemming from cross-sectional investigations that repeatedly confirmed executive functioning as a driving force for motor and cognitive performance alike (e.g., Wassenberg et al., 2005). Remarkably, non-verbal intelligence and fine motor skills did no longer explain unique amounts of variance over and above executive functioning in academic achievement suggesting that information processes included under this umbrella term are candidates to explain underlying mechanisms. From this perspective, fine motor skills may be seen as an indicator of neural connectivity and of how well a child masters executive demands inherent in many everyday life situations. Please cite this article in press as: Roebers, C. M., et al. The relation between cognitive and motor performance and their relevance for children’s transition to school: A latent variable approach. Human Movement Science (2013), http://dx.doi.org/10.1016/j.humov.2013.08.011

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Taken together, our results seem to argue in favor of a maturational factor in brain development allowing a child to continuously monitor task mastery, and to adapt ongoing information processing by updating information, resisting interference, and to flexibly switch between task demands (e.g., Diamond, 2000; Fuster, 2008). As Adolph has pointed out (Adolph & Berger, 2006) motor development is a domain of development in which general principles of learning and mechanisms of development are to be discovered such as, for example, a child’s ability to react flexibly to feedback, prospective control of actions including goal-maintenance. Of course, the present study offers only indirect evidence and neurophysiological approaches and data are needed to support this notion. We are also well aware that there are more and alternative theoretical accounts addressing the motor-cognitive link in the literature: For example, Luo et al. (2007) have argued that family-characteristics and parenting practices are environmental factors (embracing genetic influences and gene–environment interactions) similarly affect cognitive abilities, motor skills, and executive functioning – a possibility we cannot rule out with the present data. Given the importance of EF for fine motor skills, intelligence, and the prediction of academic achievement, an additional interesting, yet unexplored issue concerns the impact of EF on growth in these domains of development. Unfortunately, we were not able to do growth curve modeling with the present data as fine motor skills and intelligence were assessed with different tasks across measurement points. This was, however, also not our central focus. Another limitation of the present study may be seen in our chosen focus on fine motor skills only. By additionally including individual differences in gross motor and balancing skills, as well as physical fitness, and physical activity level, the exact nature of the link between motor skills and cognitive performance as well as its specificity could be addressed. This would allow investigating the generalizability of the present results to other aspects of motor development. It should also be critically noted that for each core dimension of executive functioning (updating, shifting, inhibition) only one task was used. Although the structural equation modeling technique allowed compensating for the otherwise stronger task-dependency of the obtained results, it would be interesting to examine the unique contributions of the single EF domains to the structural links to different aspects of motor skills. Nevertheless, the present study was able to fill in a gap in the literature by confirming that executive functioning is an important factor for explaining (a) the motor-cognitive link and (b) the predictive power of fine motor skills for early academic achievement.

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Authors’ note

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This work was partially financed through the Jacobs Foundation Zürich (award to the first author for the project ‘‘Transition to School’’). We gratefully acknowledge the cooperation of the participating children, their parents and teachers, and the school officials. We also wish to thank Ulrich Orth for his assistance in the structural equation modeling analysis as well as the student research assistants and the master students for their assistance tracking the children and collecting the data. This work is dedicated to Balz Aklin’s fine motor skills proven – among other skills – during a knee arthroscopy, with the resulting painless walking being highly appreciated by the first author.

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Appendix A

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Correlations among all included variables (task level). 1. 2. 1. Threading Beads t1 2. Posting Coins t1 3. Drawing Trail t1

3.

4.

5.

6.

7.

8.

9.

10. 11. 12. 13. 14. 15. 16. 17.

– .23 .27 .39 .39 .33 .25 .22 .16 .36 .31 .21 .28 .21 .24 .36 .28 – .18 .24 .41 .32 .21 .30 .21 .27 .33 .10 .20 .19 .14 .27 .24 – .25 .23 .45 .27 .14 .15 .28 .12 .24 .19 .20 .13 .23 .23


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Appendix A (continued) 1. 2. 4. Threading Lace t2 5. Placing Pegs t2 6. Drawing Trail t2 7. Backward Recall t1 8. Fruit-Stroop t1 9. Cognitive Flexibility t1 10. Backward Recall t2 11. Fruit-Stroop t2 12. Cognitive Flexibility t2 13. Non-verbal IQ t1 14. Non-verbal IQ t2 15. Spelling t3 16. Reading t3 17. Maths t3

3.

4.

5.

–

.42 .25 .24 – .31 .19 – .19 –

6.

7.

8.

9.

10. 11. 12. 13. 14. 15. 16. 17.

.24 .22 .25 .35 –

.22 .11 .17 .33 .40 –

.31 .28 .21 .45 .32 .41 –

.30 .15 .28 .28 .45 .28 .34 –

.31 .23 .21 .32 .27 .51 .37 .25 –

.24 .21 .19 .24 .27 .32 .34 .22 .27 –

.28 .26 .19 .40 .27 .31 .42 .29 .42 .36 –

.25 .25 .10 .30 .13 .14 .29 .15 .27 .27 .23 –

.34 .35 .28 .39 .30 .28 .35 .37 .32 .34 .32 .56 –

.37 .35 .18 .45 .30 .44 .57 .32 .45 .41 .55 .46 .64 –

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Note: t1, t2, and t3 = measurement points 1, 2, and 3, respectively. Correlations higher than .15 are significant at p < .05, correlations higher than .19 are significant at p < .01, and correlations higher than .26 are significant at p < .001.

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