Gender Differences in Mouse and Cursor ... - Semantic Scholar

International Journal of Human-Computer Interaction

ISSN: 1044-7318 (Print) 1532-7590 (Online) Journal homepage: http://www.tandfonline.com/loi/hihc20

Gender Differences in Mouse and Cursor Movements Takashi Yamauchi, Jinsil Hwaryoung Seo, Noel Jett, Greg Parks & Casady Bowman To cite this article: Takashi Yamauchi, Jinsil Hwaryoung Seo, Noel Jett, Greg Parks & Casady Bowman (2015) Gender Differences in Mouse and Cursor Movements, International Journal of Human-Computer Interaction, 31:12, 911-921, DOI: 10.1080/10447318.2015.1072787 To link to this article: http://dx.doi.org/10.1080/10447318.2015.1072787

Accepted author version posted online: 12 Aug 2015.

Submit your article to this journal

Article views: 123

View related articles

View Crossmark data

Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=hihc20 Download by: [Texas A&M University Libraries]

Date: 15 March 2016, At: 06:41

Intl. Journal of Human–Computer Interaction, 31: 911–921, 2015 Copyright © Taylor & Francis Group, LLC ISSN: 1044-7318 print / 1532-7590 online DOI: 10.1080/10447318.2015.1072787

Gender Differences in Mouse and Cursor Movements Takashi Yamauchi1 , Jinsil Hwaryoung Seo2, Noel Jett1, Greg Parks1, and Casady Bowman1 1

Downloaded by [Texas A&M University Libraries] at 06:41 15 March 2016

2

Department of Psychology, Texas A&M University, College Station, Texas, USA Department of Visualization, Texas A&M University, College Station, Texas, USA

Computer cursor and mouse activities such as moving, pointing, selecting, and dragging are essential parts of everyday interactions. Yet it is unknown how men and women differ in the way they move computer cursors. This study examines gender differences in movements of computer cursors. In one experiment, the authors measured trajectories of computer cursors every 20 ms in a simple choice-reaching task and tested the extent to which movement features related to controlling and targeting diverge between male and female participants. Results showed significant gender differences in cursor motions. Female participants deviated from the straight path toward the target location to a larger degree than did male participants, and female participants showed more backward motions (deviating backward from the target location) than did male participants. Implications for sources of these gender differences, user interface and input device design, and musculoskeletal disorders in women are also discussed.

1. INTRODUCTION Everyday computing includes checking e-mail, navigating online bookstores, searching scholarly materials, and downloading movies. Computer cursor and mouse activities such as moving, pointing, selecting, and dragging are essential parts of these interactions. Yet it is unknown how men and women differ in the way they move computer cursors. Previous research documented gender differences in computer use related to spatial navigation (Castelli, Corazzini, & Geminiani, 2008; Martens & Antonenko, 2012), attitude and experience (Imhof, Vollmeyer, & Beierlein, 2007; Schumacher & Morahan-Martin, 2001; Todman & Day, 2006), computer-mediated communication (Sussman & Tyson, 2000), acceptance of social robots (Tay, Jung, & Park, 2014), cognitive learning strategy (Kesici, Sahin, & Akturk, 2009), and website design and preference (Cyr & Head, 2013). However, to our knowledge, no study has ever made a systematic investigation into gender differences in movements of computer mice and cursors; but see Hertzum and Hornbaek (2010) for impacts of aging on mouse motions. Address correspondence to Takashi Yamauchi, Department of Psychology, Texas A&M University, College Station, TX 77845, USA. E-mail: [email protected] Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/hihc.

Although gender differences in motor-spatial performance has been well documented for decades in psychology (Kimura, 1999), gender differences have been virtually ignored even in most influential input device usability and evaluation studies. For example, going back to the seminal studies by Fitts (Fitts, 1954; Fitts & Peterson, 1964; Fitts & Radford, 1966), Card (Card, English, & Burr, 1978), MacKenzie (MacKenzie, 1992), Accot and Zhai (Accot & Zhai, 1997, 1999; Zhai, 2004; Zhai, Kong, & Ren, 2004), and Kapoor and Picard (Kapoor, Burleson, & Picard, 2007), no information about gender differences was ever considered in these studies. The tacit assumption has typically been that women interact with computers just as men do and that input devices (e.g., computer mice, touch screen, digital pens) can be developed without regard to the different motor-spatial capacities that men and women possess. This assumption is problematic and even harmful because the bias that is ignored in the design phase will become pervasive in the marketplace, creating possibly detrimental side effects (Friedman & Nissenbaum, 1996). It is well known that musculoskeletal disorders—pain or injuries associated with necks, shoulders, joints, and ligaments—arise from excessive computer and mouse use; it is also well known that computer-related musculoskeletal disorders are far more common in women than in men (Al-Hashem & Khalid, 2008; Bamac et al., 2014; Cote, 2012; Jensen, Finsen, Sogaard, & Christensen, 2002; Lassen et al., 2004; Treaster & Burr, 2004; Wahlström, 2005). Understanding gender differences in cursor motions is important for the development of a personalized and user-friendly interface as well as an automatic nonvisual gender recognition system. The current study investigates the following research questions: Do men and women differ systematically in the way they move the computer mouse and cursor? If so, what kinds of motion properties are most indicative of gender differences? Where does the difference come from?

1.1. Theoretical Background Behavioral markers of gender differences. What human behavior manifests clear and robust gender differences? Much research in psychology, sociology, education, neuroscience, and

911


912

T. YAMAUCHI ET AL.

behavioral economics has documented a variety of gender differences in human behavior, including mathematical abilities, verbal aptitudes, emotion expression and regulation, autobiographical memory, and motor and visuo-spatial abilities. The latest review of meta-analytic studies suggests that many of these well-known gender differences are negligible and subject to sociocultural factors (Hyde, 2014). For example, a crosscultural study by Costa, Terracciano, and McCrae (2001) shows that the gender difference associated with expressions of anxiety is culture-specific. In the United States, females have been reported to experience and express anxiety more often than males, but this tendency is absent in the Japanese or Black South Africans. Another meta-analysis (Hyde, Lindberg, Linn, Ellis, & Williams, 2008) suggests that gender differences in math and verbal abilities in high school students are negligible. To date, the most drastic behavioral gender differences are found in motor and visuo-spatial activities, such as navigating a virtual maze, rotating images mentally, targeting a projectile at a particular object, and memorizing object locations. Men tend to outperform women in mental image rotation, virtual maze navigation, and projectile targeting tasks, whereas women outperform men in object location memory in significant degrees (Andreano & Cahill, 2009; Castelli et al., 2008; Hines, 2011; Hyde, 2014; Martens & Antonenko, 2012). These behavioral differences are found in different age groups, across cultures, and immune to training and experience (Kolb & Whishaw, 2009). Indeed, gender differences associated with motor-spatial abilities are said to have biological and evolutionary origins (Alexander, Wilcox, & Farmer, 2009; Silverman, Choi, & Peters, 2007). For example, women with Congenial Adrenal Hyperplasia— a genetic disorder that produces an elevated testosterone exposure before birth—exhibit enhanced motor-spatial abilities, male-typical toy preference (prefers cars over dolls), and aggressive behavior relative to non-Congenial Adrenal Hyperplasia women (Alexander & Peterson, 2004; Hines et al., 2003; Puts, McDaniel, Jordan, & Breedlove, 2008). Furthermore, a large-scale cross-cultural study involving more than 200,000 participants across 40 countries demonstrated consistent gender differences in motor-spatial activities. Medium to large sex differences in three-dimensional mental rotation tasks are reported cross-culturally in seven ethnic groups and 40 countries (Silverman et al., 2007). It is argued that these sex differences can be traced back to the Pleistocene era, in which males engaged primarily in hunting while females were gatherers of plant food. Thus, developing a long-range global spatial ability had adaptive advantage in males, whereas developing local location memory had adaptive advantage in females. This brief review suggests that motor-spatial activities exhibit strong and reliable gender differences, and these differences are likely to be impervious to contextual factors. Cursor-motion for human behavior understanding. A number of research studies have analyzed cursor movements for

human behavior understanding, such as the interest and emotion of computer users. Mueller and Lockerd (2001) examined online shopping tasks while users’ cursor activities were tracked. The recorded cursor movements were later reproduced for observational analysis, and the researchers reported “similarities” of cursor trajectories relative to users’ interest. Guo and Agichtein (2008) assessed users’ intention in queries from their cursor movement patterns. The researchers suggest that the average trajectory length of navigational queries was shorter than that of informational queries. Kapoor et al. (2007) integrated a pressure-sensitive mouse into their multichannel automatic emotion detection system. The researchers measured mean, variance, and skewness of mouse pressure while subjects learned to solve a Tower of Hanoi puzzle. The mouse pressure was as discriminable as the skin conductance measure for the detection of frustration. Azcarraga and Suarez (2012) evaluated electroencephalogram (EEG) signals and cursor activities (distance traveled, click duration) during algebra learning in an intelligent tutoring system to predict subjects’ emotions. When cursor activity data were augmented to the EEG data, accuracy rates increased to 92%, indicating that cursor activity data can supply useful information for intelligent tutoring system learning on top of EEG data. Scheirer, Fernandez, Klein, and Picard (2002) investigated subjects’ mouse-clicking patterns during a computer game and showed an association between mouse clicking and frustration. Finally, Yamauchi (2013) studied the relationship between cursor trajectories and generalized anxiety in human subjects. The researcher found that an assortment of about 20 trajectory features related to controlling and targeting cursors can predict users’ anxiety levels. As for specific studies examining gender differences in mouse use, Wahlstrom, Svensson, Hagberg, and Johnson (2000) investigated the muscle activity during mouse use and found that women tend to apply higher musculoskeletal loads than men do. Women exert twice more force than men do in pressing the mouse button. In cognitive science, a number of researchers have employed cursor motion to investigate cognitive mechanisms underlying reasoning, categorization, lexical processing, and unconscious priming. These studies show that cognitive decision making occurs dynamically and its mental processes can be captured by the movement of cursors (Dale, Kehoe, & Spivey, 2007; Freeman, Pauker, Apfelbaum, & Ambady, 2009; Spivey, Grosjean, & Knoblich, 2005; Xiao & Yamauchi, 2014; Yamauchi, Kohn, & Yu, 2007). To summarize, cursor-motion analysis has been shown to be successful for affective computing and cognitive decision making. However, no studies have examined gender differences in cursor motion, despite the importance of creating a friendly interface. In addition, studying the differences in cursor motion in men and women will inform us about sources of important gender difference in computer use and its impact, such as the prevalence of musculoskeletal disorders in women, and possible remedies for interface design (Sillanpaa et al., 2010).

GENDER DIFFERENCES IN MOUSE AND CURSOR MOVEMENTS

2. EXPERIMENT 2.1. Overview and Hypotheses Our review suggests that (a) men and women display clear differences in their motor-spatial performance, and (b) these differences are relatively free from contextual factors. On the basis of the aforementioned reviews, we formed the following hypotheses.


H1: Men and women will diverge significantly in the way they control and target computer mice/cursors. H2: The gender differences in cursor motions will be relatively free from contextual factors. To test these hypotheses, we employed a simple choicereaching task involving judgments of similarities of geometric figures (Kimchi & Palmer, 1982). Participants were presented with a triad of geometric figures on a computer monitor and were asked to select which choice figure, shown at the top-left or top-right corner of the monitor, was more similar to the base figure shown at the bottom (Figure 1). Participants indicated their choice by clicking a “left” or “right” button placed at the top of each choice figure. In each trial, our program recorded the x-y coordinates of the cursor location every 20 ms from the onset of a trial (participants pressing the “Next” button) until the end of the trial (participants pressing an either left- or right-choice button; Dale et al., 2007; Spivey et al., 2005; Yamauchi, 2013). Participants carried out 96 trials of the choice-reaching task. We selected this simple task because the perception of similarity is one of the most fundamental psychological functions that mediate decision making, memory, generalization, impression formation, and problem solving (Hahn & Ramscar, 2001; Yu, Yamauchi, Yang, Chen, & Gutierrez-Osuna, 2010). Thus, the basic characteristics of our choice-reaching task are likely to speak to more complex and realistic situations, such as comparing and selecting consumer products at an online shopping site. Because cursor motions can be influenced by emotional states of computer users (Yamauchi, 2013; Yamauchi & Right

Left

Next

FIG. 1. Screenshots of the choice-reaching task. Note. The task was to select which choice figure, left or right, was more similar to the bottom figure. A cursor trajectory is shown the right panel figure for illustrative purposes.

913

Bowman, 2014), we introduced different interactive situations in which happy, sad, or no music (control condition) was played through headsets while participants conducting the choice-reaching task. These different interactive situations were introduced to test the robustness of H1 and H2.

2.2. Method Participants. Participants (female = 117, male = 69; estimated age range = 19–22) were undergraduate students participating for course credit. Materials and procedure. In total, 16 basic triads were produced by varying the number of local shapes—figures made of three or four local shapes, nine or 10 shapes, 15 or 16 shapes, and 36 shapes (Figure 2). In the experiment, 32 triads were produced from the 16 basic triads by swapping the left–right locations of the choice figures, and participants received 32 triads three times. Each figure shows an overall shape (either a square or a triangle) with smaller squares or triangles. In each triad, two choice figures were similar to the base figure in either their overall shape or local shapes. For example, in the top-left box in Figure 6, the left-choice figure is similar to the base figure with their local shapes (e.g., the two figures consist of triangle components) and the right-choice figure is similar to the base figure with their global configuration (the two figures are arranged globally as a triangle; Figure 2). To start the choice-reaching task, participants pressed the Next button. The moment later, a triad stimulus was presented and their cursors were placed automatically at the center of the Next button, which was located on the midline of the screen. Participants indicated which choice figure, left or right, was more similar to the base figure shown at the bottom by pressing the “left” or “right” button (Figure 1). After the response, the Next button appeared again. This cycle was repeated 96 times. The entire experiment consisted of three parts, during which participants conducted the choice-reaching task three times while listening to happy, sad, or no music (control condition; Figure 3). Shortly after each choice-reaching task, participants rated their emotional states using a questionnaire—the Positive and Negative Affect Schedule–Expanded (PANAS-X; Watson & Clark, 1999). To elicit different moods, we supplied six pieces of classical music, which have been shown to evoke either happy or sad moods (Eich et al., 2007; Table 1). At the beginning of the experiment, all participants were asked to wear headsets (JVC Flats stereo headphones) and to adjust the volume as they preferred. The left–right locations of figures were counterbalanced for each participant. The order of presenting stimuli was determined randomly for each participant. Apparatus and data collection. We used six desktop computers (HP de 7900 systems with an E8400 Core 2 Duo 3.0 GHZ processor) and monitors (19-in.-wide flat panel display; HP L1908wi) for data collection. All participants used the same Dell Optical Mouse with USB connection (Dell 0C8639 USB


914

T. YAMAUCHI ET AL.

FIG. 2. Illustrations of stimuli used in the choice-reaching task. Note. Sixteen basic triads were produced by varying the number of local shapes—three or four, nine or 10, 15 or 16, and 36 shapes.

FIG. 3. A schematic depiction of the design of the experiment.

2 Button Scrollwheel Optical Mouse; Figure 4). The pointer speed of the mice was set as medium, and the resolution of the monitor was fixed at 1440 × 900.

Overall participants took approximately 20 min to complete all trials (10 min for each part, and there was no break), and participants did not mention any discomfort or usability issues with using the mouse. Trajectory features. Following the previous studies using cursor motions to examine emotion processing, decision making, and unconscious priming, we measured two types of cursor trajectory features—targeting and controlling (Dale et al., 2007; Freeman et al., 2009; Spivey et al., 2005; Xiao & Yamauchi, 2014; Yamauchi, 2013; Yamauchi & Bowman, 2014). In the targeting feature, we assessed the extent to which a cursor trajectory deviated from the ideal path—the straight line from the starting position (the center of the Next button) to the end position (the position at which a response button was clicked). Specifically, we measured the area of a trajectory deviated away from the ideal path (deviation-away) and the area of a trajectory deviated toward the final response location (deviation-toward; Figure 5). Another trajectory feature was cursor control. Every time step in a trajectory (100 time steps in total) was classified into one of the four directional angles (Angle 1, 2, 3, or 4 in Figure 5), and the cumulative distance (pixel values) traveled in each angle class was calculated for each trial. Thus, we examined two targeting features (deviation-away and deviation-toward) and four controlling


915

TABLE 1 List of Happy and Sad Music Song Title


Waltz of the Flowers Trepak Dance of the Flutes Prelude #4 in E minor Lullaby Symphony No. 9 Largo

Composer

Length

Emotion

Tchaikovsky Tchaikovsky Tchaikovsky Chopin Stravinsky Dvorak

6:36 1:07 2:24 2:32 3:54 11:51

Happy Happy Happy Sad Sad Sad

FIG. 4. Apparatus used for the experiment.

features (cumulative lengths of Angles 1, 2, 3, and 4) in the experiment. Design. The experiment was designed with one betweensubjects factor, gender (male, female), and one within-subjects factor, music (happy, sad, control), which were given in three segments (Figure 3). In the happy condition, happy music (Table 1) was played through a headset while participants carried out the choice-reaching task. In the sad condition, sad music was played throughout the task. In the control condition, no music was given. The happy and sad conditions were given in either the first or third segment of the experiment. The assignment of the order of happy and sad conditions was made randomly for each participant. The control condition was given in the second segment for all participants. 2.3. Results1 Selection patterns, response times, and emotion ratings. We first report overall selection patterns, response times, and 1 The entire data set and programs used for this study are available at https://www.dropbox.com/sh/hdglcwwuv999vky/AAC7vCf7C4oP5tugXfO2HCwa?dl=0

FIG. 5. Illustrations of targeting (deviation-away, deviation-toward) and controlling (Angle 1–4) features.

emotion rating scores. For overall selection patterns, we tabulated the proportion of trials in which participants selected globally similar shapes over locally similar shapes (e.g., in the first two columns of Figure 2, “selecting globally similar shapes” means selecting the choice figures placed on the right top corners). For response times, we measured the duration between the onset of the stimulus presentation and the point at which the participant pressed one of the choice buttons. For emotion ratings, we examined PANAS-X scores given to the positive affect, negative affect, joviality, and sadness categories (Table 2).

916

T. YAMAUCHI ET AL.

TABLE 2 Means (Standard Deviations) of Selection Patterns, Response Times, and Emotion Ratings Selection Patterns

Female Male

Response Times

Happy Music

No Music

Sad Music

0.78 (0.29) 0.74 (0.31)

0.77 (0.30) 0.75 (0.33)

0.78 (0.29) 0.77 (0.32)

Happy Music 1545 (319) 1524 (396)

Positive Affect


Female Male

2.45 (0.83) 2.54 (0.93)

1.72 (0.68) 1.83 (0.61)

∗

2.51 (1.02) 2.56 (1.10)

1.55 (0.69) 1.68 (0.71)

Sad Music

1414 (209) 1459 (617)

1560 (411) 1588 (459)

Negative Affect 1.99 (0.72) 2.10 (0.76)

1.29 (0.43) 1.38 (0.45)

Joviality Female Male

No Music

1.37 (0.48) 1.5 (0.53)

1.35 (0.47) 1.42 (0.52)

Sadness 1.82 (0.75) 1.88 (0.81)

1.24(0.43) 1.41 (0.58)∗

1.39 (0.61) 1.53 (0.86)

1.43 (0.61) 1.57 (0.62)

p < 0.05.

Our male and female participants were comparable in their overall selection patterns, response times, and emotion ratings except for sadness ratings. We found no gender differences in their selection patterns (F < 1.0) and response times (F < 1.0). There were no interaction effects between gender and music in selection patterns, F(2, 368) = 1.69, MSE = 0.01, p = .19, ïp 2 = 0.01, and response times (F < 1.0). Likewise, our male and female participants were generally equivalent in their emotion ratings of positive affect, F(1, 175) = 1.26, MSE = 1.13, p = 0.284, ïp 2 = 0.007; negative affect, F(1, 175) = 2.14, MSE = 0.544, p = 0.15, ïp 2 = 0.01; and joviality (F < 1.0). There were no interaction effects of music and gender in these measures (Fs < 1.0). The only exception was sadness ratings, in which we found a marginally significant main effect of gender (male, M = 1.50, SD = 0.56; female, M = 1.35, SD = 0.47), F(1, 175) = 3.64, MSE = 0.761, p = 0.058, ïp 2 = 0.02. Similarly, the order of trials (Parts 1–3 in Figure 3) did not interact with gender. There were no interaction effects between gender and trial order (Parts 1–3 in Figure 3): selection patterns (F < 1.0); response times (F < 1.0). These results suggest that gender differences were minimum in overt behavior, such as overall selection patterns, response times, and emotion ratings. Cursor motion analysis. Although we found virtually no gender differences in overall selection patterns, response times, and emotion ratings, clear and strong gender differences were evident in cursor motions. Significant gender differences were observed in the way cursors were targeted and controlled. Cursor trajectories in female participants deviated from the straight path toward the target location significantly to a larger degree than those in male participants; deviation-toward, F(1, 196) = 12.89, MSE = 627932.1, p = .0004, ïp 2 = 0.062 (Figure 6). Female participants showed significantly more

FIG. 6. Gender differences in the targeting features (deviation-toward, deviation-away). Note. The y-axis represents the number of pixels.

backward motions (Angle 3 length) than male subjects, F(1, 196) = 5.71, MSE = 3353.3, p = .02, ïp 2 = 0.03 (Figure 7). Female participants also displayed more forward motions (Angle 1 length) probably because they needed more directional adjustments, F(1, 196) = 6.51, MSE = 3768.6, p = .01, ïp 2 = 0.03. Male and female participants were statistically indistinguishable in their cursor motions deviating away from the target; deviation-away, Angle 2 length (F < 1.0). Impacts of music. Music impacted cursor motions differently in male and female participants. For female participants, music did not alter their motion patterns in all features. However, for male participants, music affected almost all features of cursor motions. One-way analyses of variance applied separately to female and male participants showed no main effects of music in female participants in all the motion features (Fs < 1.34, ps > .25). However, for male participants, music interacted with cursor motions in all the features except for Angle 3 length: deviation-away, F(2, 136) = 7.43, MSE = 3786949.8, p = .001, ïp 2 = 0.10; deviation toward, F(2, 136) = 3.79, MSE = 3786949.8, p = .03, ïp 2 = 0.05. Angle Length 1, F(2, 136) = 2.99, MSE = 475.8, p = .05, ïp 2 = 0.04; Angle Length 2, F(2, 136) = 4.59, MSE = 4759.8, p = .01,


917


FIG. 8. Gender differences in the targeting features (deviation-toward, deviation-away) relative to trial order. Note. The y-axis represents the number of pixels.

FIG. 7. Gender differences in the controlling features (Angle 1–4 lengths). Note. The y-axis represents the number of pixels.

ïp 2 = 0.06; Angle Length 3, F < 1.0; Angle Length 4, F(2, 136) = 3.44, MSE = 958.8, p = .04, ïp 2 = 0.05. Happy music made male participants move their cursors deviating from the ideal path toward the end position (deviationtoward), compared to sad music, t(68) = 2.15, p = .03, d = 0.32, 95% CId [–0.01, 0.66], whereas sad music made their cursors deviate from the ideal path away from the end position (deviation-away), compared to happy music, t(68) = 2.94, p = .004, d = 0.32, 95% CId [–0.01, 0.66]. These results suggest that emotional music influenced cursor motions of male participants; however, the influence of music was virtually nonexistent in female participants. Further analysis revealed that participants’ negative affect (as measured by the average values of the negative affect entry in the PANAS-X) correlated significantly with all the three cursor properties—Angle 1 length, Angle 3 length, and deviation-toward: rs = .20, .16, and .17, respectively, and all ps < .05. Analysis of covariance controlling participants’ negative affect as a covariant shows that gender differences in the aforementioned features remain substantial even after controlling participants’ negative emotional state. Our female participants’ cursor trajectories deviated toward the final location significantly more than male participants even controlling their emotional states: deviation-toward, F(1, 173) = 8.94, MSE = 150085, p = .003, ïp 2 = 0.05. Trajectories of female participants also displayed backward motions to a larger degree than those of male participants: Angle 3 length, F(1, 173) = 6.47, MSE = 900.9, p = .01, ïp 2 = 0.04, highlighting the fact that gender differences observed in cursor control and targeting are likely to be consistent over participants’ emotional states. Impacts of trial order. Trial order (e.g., Parts 1, 2, and 3 in Figure 3) did not influence gender differences overall selection patterns and response times. However, trial order made different impacts on cursor motions of male and female participants

FIG. 9. Gender differences in the controlling features (Angle 1–4 lengths) relative to trial order (Parts 1–3). Note. The y-axis represents the number of pixels.

(Figures 8 and 9). There were significant interactions between gender and trial order in targeting and controlling the cursor: deviation-away, F(2, 368) = 8.53, MSE = 2662894.6, p < .001, ïp 2 = 0.044; Angle 2 length, F(2, 368) = 10.1, MSE = 3207.6, p < .001, ïp 2 = 0.05; Angle 4 length, F(2, 368) = 2.96, MSE = 702.6, p = .05, ïp 2 = 0.02. Given the targeting feature, the amount of cursor motions deviating away from the straight line (deviation-away) declined gradually as trials proceeded from Part 1 to Part 3, but such an order effect was absent in female participants: male (trial order), F(2, 136) = 13.51, MSE = 3502909.4, p < .001, ïp 2 = 0.17; female (trial order), F(2, 232) = 1.23, MSE = 2170472.1, p = .28, ïp 2 = 0.01. Similarly, given the controlling feature (Angle 2 length and Angle 4 length), trial order influenced cursor motions in male participants considerably but to a much smaller extent in female participants: male (trial order), F(2, 136) = 9.80, MSE = 880.4, p < .001, ïp 2 = 0.13; female (trial order), F(2, 232) = 3.6, MSE = 598.3, p = .28, ïp 2 = 0.01. These results suggest that some sort of motor learning (i.e., a reduction of deviation in targeting and controlling) took place in male participants but in a much less degree in female participants. Taken together, these results show important trajectory features that diverge between male and female participants.

918

T. YAMAUCHI ET AL.

Consistent with H1, male and female participants were different in the degree that their cursor trajectories deviated from the straight path (deviation-toward); male and female participants were also different in the amount of backward cursor motions they displayed (Angle 2 length). Partly consistent with H2, these gender differences remained strong even after controlling emotion rating scores. However, cursor motions were also influenced by emotional music played during the choice-reaching task and the types of music interacted with gender. Our analysis further indicates that trial order (Parts 1, 2, 3 in Figure 3) also contributed to gender differences in cursor motions.

3.1. Summary In an attempt to elucidate gender differences in mouse and cursor activities, this study compared cursor trajectories of male and female participants in a simple choice-reaching task. In the experiment, participants were presented with a triad of geometric figures on a computer monitor and judged which choice figure, shown at the top-left or top-right corner of the monitor, was more similar to the base figure shown at the bottom. We found that deviations of cursor trajectories display robust gender differences in a choice-reaching task and that this gender difference interacts with the emotional content of music. Cursor trajectories obtained from female participants deviated from the straight line in a much higher degree than those from male participants; female participants also showed more backward motions than did male participants. The influence of music was virtually nonexistent in female participants, but the emotional music modified the cursor motions of male participants a great deal. Furthermore, a learning effect due to trial order (e.g., a reduction of motion deviation in later trials) was present primarily in male participants but in a much smaller degree in female participants. Taken together, these results support H1 and are partially consistent with H2. Results therefore indicate that, as in traditional “off-line” tasks (e.g., overall performance for a 3D mental rotation task), motor-spatial behavior associated with cursor movements displays gender disparity at a millisecond level.

First, the observed gender differences can be direct reflections of spatial-motor abilities that have been documented in “off-line” tasks (e.g., 3D mental rotation tasks and virtual maze navigation tasks). Navigating on the computer screen to reach a target (pressing a button) may require similar motor-spatial mechanisms as those required in off-line tasks. In this sense, gender differences in cursor movements can be viewed as a direct extension of the off-line tasks. In other words, it is likely that men and women were different in their abilities to accurately control cursors to reach a target on the screen. Second, it is also possible that men and women adopt different cognitive strategies in moving cursors. It is argued that sex differences pertaining to spatial activities are evolutionally grounded; males engaged primarily in hunting, whereas females were gatherers of plant food in the Pleistocene era. In males, a long-range global spatial ability had adaptive advantage, whereas in females, a short-range locally focused spatial ability was critical. To better utilize these different cognitive dispositions, men and women employed different cognitive strategies to cope with spatial tasks (Andreano & Cahill, 2009; Silverman et al., 2007). In reaching the end point, our male participants might have used global spatial cues, whereas women used local cues. In other words, the gender differences in cursor motions are expressions of cognitive strategies that men and women employ. Finally, it is also possible that the observed gender differences reflect different risk-taking behavior. Research in behavioral economics suggests that men and women are different in the perception of risk. Women tend to be more risk aversive in real and hypothetical gambling and in high-stake investment decision. Women are more reluctant to engage in competitive interactions such as tournaments, bargaining, and auctions (Croson & Gneezy, 2009). The choice-reaching task implemented in the current study might have led men and women to adopt different risk assessment. Cursor motions such as the increased deviations and backward motions may reflect women’s aversion of risk taking. At this stage we do not have clear explanations for the mechanisms that mediate gender differences in cursor motions; we suggest that at least these three factors interact and influence the gender disparity.

3.2. Sources of Gender Differences H1 predicted systematic differences in cursor motions in male and female participants. Our data support this hypothesis. The degree of deviation from the “ideal” straight line was far greater in female participants than male participants; female participants also exhibited the tendency to make more backward motions, suggesting that cursor control was probably an important factor that differentiates male and female participants. Although the exact origin of these motion-related gender differences should be scrutinized in future studies, we propose three possibilities.

3.3. Emotion and Gender Differences The impact of emotional music was far stronger on men than women. It is nterest that music did not influence overt performance—overall selection patterns and response times; yet the effect of music was unmistakable in cursor motions of male participants. We tentatively suggest that the diverging impacts of music can be understood in light of the gender disparity in emotion expressions and regulations. Behaviorally, men are more likely to report arousal when looking at pleasant stimuli, whereas women tend to report arousal given unpleasant stimuli (Bradley & Lang, 2007). The brain structure that processes


3. DISCUSSION



emotional stimuli is also different between men and women. Women have larger hippocampus than men when adjusted for total brain size, and sex hormones influence excitability of hippocampal cells. These hippocampal sex differences impact the reactions to chronic stress. For example, in rats and monkeys, chronic stress damages male hippocampus but far less in female hippocampus (Cahill, 2006). The amygdala is significantly larger in men than in women. There is also an important sex-related hemispheric lateralization in the amygdala and the prefrontal cortex (Cahill, 2006). We tentatively suggest that the diverging impacts of emotional music in male and female participants are probably related to sexual dimorphism pertinent to emotion processing.

3.4. Musculoskeletal Disorders, Gender Differences, and User Interface and Input Device Design Musculoskeletal disorders—pain or injuries in necks, shoulders, backs, wrists, or joints—are far more common in women than men (Cote, 2012; Jensen et al., 2002; Lassen et al., 2004; Saleh, Fuortes, Vaughn, & Bauer, 2001; Treaster & Burr, 2004; Wahlström, 2005). Undoubtedly, many socio-psychological factors contribute to this particular form of sexual dimorphism. For example, women in general bear more household work, child-care, and work-related stress than men do, making simple repetitive computer work even more cumbersome (Wahlström, 2005). However, it is difficult to ignore ergonomic risk factors. Continuous computer and mouse use not only increases symptoms of neck, shoulder, and joint pain but also delays conduction velocities of median and ulnar sensory nerves, which are often affected in an early stage of nerve entrapment (AlHashem & Khalid, 2008; Bamac et al., 2014). Studies show that even when men and women engage in identical repetitive tasks, female workers show far higher incidents of musculoskeletal disorders (Al-Hashem & Khalid, 2008; Bamac et al., 2014; Blatter & Bongers, 2002; Jensen et al., 2002; Lassen et al., 2004; Nordander et al., 2008). Male-centric interface and input device design might have been a factor contributing this inequality. Some sporting goods, such as tennis rackets, golf clubs, bicycles, and skis, fully incorporate gender differences. Given the finding that men and women move computer mice and cursors in a different manner, gender-specific computer interface and input devices may help ameliorate musculoskeletal disorders in women. For example, a mouse that coordinates its movement with saccades may help reduce unnecessary low-load repetitive muscle activities; sizes and locations of interactive buttons, type sets, icons, and menus can be made larger specifically for female users; different grip styles and motion sensitivities can be introduced for men and women (Chen & Leung, 2007). Given the different motor activities associated with men and women, it is possible that the interface design that is rated high in one gender group may not be equally positive to the other gender group (Passig & Levin, 1999).

919

3.5. Limitations The generality of the present findings should be tested in different interactive situations in which real online activities, such as browsing, comparing, and selecting take place (e.g., online shopping, gaming and survey sites). It is unknown that the features identified in the present experiments would be relevant in these real situations. It is possible that gender differences in cursor motions are subject to the age and expertise of computer users. Older men and young women may show similar cursor motions and cursor trajectories may not be informative in this case. Cursor motions could be also influenced by the level of computer literacy. In addition, the current study did not test gender differences in different screen sizes (e.g., tablets and smartphones), monitor resolutions, pointer speeds, and input devices (e.g., styles pens, gesture-based interface, 3D mice, touch screens, and/or input based on eye gaze; Zander, Gaertner, Kothe, & Vilimek, 2010). The generality of our findings should be tested further with these different devices. 3.6. Conclusion There are consistent and robust gender differences in the way men and women move computer cursors. We think that cursor motions reveal gender differences much in the same way that sex differences associated with facial features, gaits, and body types are shaped by sex hormones (e.g., testosterone). We suggest that it is critical to investigate the gender differences particularly related to motor-spatial activities for the development of context-aware interfaces. ORCID Takashi Yamauchi

http://orcid.org/0000-0002-6372-1118

REFERENCES Accot, J., & Zhai, S. (1997). Beyond Fitts’ law: Models for trajectory-based HCI tasks. Proceedings of the SIGCHI conference on Human Factors in Computing Systems, 295–302. Accot, J., & Zhai, S. (1999). Performance evaluation of input devices in trajectory-based tasks: An application of the steering law. Proceedings of the SIGCHI conference on Human Factors in Computing Systems, 466–472. Alexander, G. M., & Peterson, B. S. (2004). Testing the prenatal hormone hypothesis of tic-related disorders: Gender identity and gender role behavior. Development and Psychopathology, 16, 407–420. Alexander, G. M., Wilcox, T., & Farmer, M. E. (2009). Hormone–behavior associations in early infancy. Hormones and Behavior, 56, 498–502. Al-Hashem, F. H., & Khalid, M. (2008). The effect of long-term use of computer mouse devices on median nerve entrapment. Neurosciences, 13, 131–135. Andreano, J. M., & Cahill, L. (2009). Sex influences on the neurobiology of learning and memory. Learning & Memory, 16, 248–266. Azcarraga, J., & Suarez, M. T. (2012). Predicting academic emotions based on brainwaves, mouse behaviour and personality profile. Proceedings of the PRICAI 2012: Trends in Artificial Intelligence, 728–733. Bamac, B., Colak, S., Dundar, G., Selekler, H. M., Taskiran, Y., Colak, T., & Balci, E. (2014). Influence of the long term use of a computer on median, ulnar and radial sensory nerves in the wrist region. International Journal of Occupational Medicine and Environmental Health, 27, 1026–1035. doi:10.2478/s13382-014-0335-z


920

T. YAMAUCHI ET AL.

Blatter, B. M., & Bongers, P. M. (2002). Duration of computer use and mouse use in relation to musculoskeletal disorders of neck or upper limb. International Journal of Industrial Ergonomics, 30, 295–306. doi:10.1016/s0169-8141(02)00132-4 Bradley, M. M., & Lang, P. J. (2007). The International Affective Picture Systems (IAPS) in the study of emotion and attention. In J. A. Coan & J. J. B. Allen (Eds.), Handbook of emotion elicitation and assessment (pp. 29–46). New York, NY: Oxford University Press. Cahill, L. (2006). Why sex matters for neuroscience. Nature Reviews Neuroscience, 7, 477–484. Card, S. K., English, W. K., & Burr, B. J. (1978). Evaluation of mouse, ratecontrolled isometric joystick, step keys, and text keys for text selection on a CRT. Ergonomics, 21, 601–613. Castelli, L., Corazzini, L. L., & Geminiani, G. C. (2008). Spatial navigation in large-scale virtual environments: Gender differences in survey tasks. Computers in Human Behavior, 24, 1643–1667. doi:10.1016/j.chb.2007.06.005 Chen, H. M., & Leung, C. T. (2007). The effect on forearm and shoulder muscle activity in using different slanted computer mice. Clinical Biomechanics, 22, 518–523. doi:10.1016/j.clinbiomech.2007.01.006 Costa, P., Terracciano, A., & McCrae, R. R. (2001). Gender differences in personality traits across cultures: Robust and surprising findings. Journal of Personality and Social Psychology, 81, 322–331. Cote, J. N. (2012). A critical review on physical factors and functional characteristics that may explain a sex/gender difference in work-related neck/shoulder disorders. Ergonomics, 55, 173–182. doi:10.1080/00140139.2011.586061 Croson, R., & Gneezy, U. (2009). Gender differences in preferences. Journal of Economic Literature, 47, 448–474. Cyr, D., & Head, M. (2013). Website design in an international context: The role of gender in masculine versus feminine oriented countries. Computers in Human Behavior, 29, 1358–1367. http://dx.doi.org/10.1016/j.chb.2013. 01.050 Dale, R., Kehoe, C., & Spivey, M. (2007). Graded motor responses in the time course of categorizing atypical exemplars. Memory & Cognition, 35, 15–28. Eich, E., Ng, J. T. W., Macaulay, N. D., Percy, A. D., Grebneva, I., & Watson, D. (2007). Combing music with thought to change mood. In J. A. Coan & J. J. B. Allen (Eds.), Handbook of emotion elicitation and assessment (pp. 124–136). New York, NY: Oxford University Press. Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47, 381–391. Fitts, P. M., & Peterson, J. R. (1964). Information capacity of discrete motor responses. Journal of Experimental Psychology, 67, 103–112. Fitts, P. M., & Radford, B. K. (1966). Information capacity of discrete motor responses under different cognitive sets. Journal of Experimental Psychology, 71, 475–482. Freeman, J. B., Pauker, K., Apfelbaum, E. P., & Ambady, N. (2009). Continuous dynamics in the real-time perception of race. Journal of Experimental Social Psychology, 46, 179–185. Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems (TOIS), 14, 330–347. Guo, Q., & Agichtein, E. (2008). Exploring mouse movements for inferring query intent. Proceedings of the 31st annual international ACM SIGIR conference on Research and Development in Information Retrieval, 707–708. Hahn, U., & Ramscar, M. (2001). Similarity and categorization. Oxford, UK: Oxford University Press. Hertzum, M., & Hornbaek, K. (2010). How age affects pointing with mouse and touchpad: A comparison of young adult, and elderly users. International Journal of Human–Computer Interaction, 26, 703–734. doi:10.1080/10447318.2010.487198 Hines, M. (2011). Gender development and the human brain. Annual Review of Neuroscience, 34, 69–88. Hines, M., Fane, B. A., Pasterski, V. L., Mathews, G. A., Conway, G. S., & Brook, C. (2003). Spatial abilities following prenatal androgen abnormality: Targeting and mental rotations performance in individuals with congenital adrenal hyperplasia. Psychoneuroendocrinology, 28, 1010–1026.

Hyde, J. S. (2014). Gender similarities and differences. Annual Review of Psychology, 65, 373–398. Hyde, J. S., Lindberg, S. M., Linn, M. C., Ellis, A. B., & Williams, C. C. (2008). Gender similarities characterize math performance. Science, 321, 494–495. Imhof, M., Vollmeyer, R., & Beierlein, C. (2007). Computer use and the gender gap: The issue of access, use, motivation, and performance. Computers in Human Behavior, 23, 2823–2837. doi:10.1016/j.chb.2006.05.007 Jensen, C., Finsen, L., Sogaard, K., & Christensen, H. (2002). Musculoskeletal symptoms and duration of computer and mouse use. International Journal of Industrial Ergonomics, 30, 265–275. doi:10.1016/s0169-8141(02)00130-0 Kapoor, A., Burleson, W., & Picard, R. W. (2007). Automatic prediction of frustration. International Journal of Human–Computer Studies, 65, 724–736. Kesici, S., Sahin, I., & Akturk, A. O. (2009). Analysis of cognitive learning strategies and computer attitudes, according to college students’ gender and locus of control. Computers in Human Behavior, 25, 529–534. http://dx.doi. org/10.1016/j.chb.2008.11.004 Kimchi, R., & Palmer, S. E. (1982). Form and texture in hierarchically constructed patterns. Journal of Experimental Psychology: Human Perception and Performance, 8, 521–535. Kimura, D. (1999). Sex and cognition. Cambridge, MA: MIT Press. Kolb, B., & Whishaw, I. Q. (2009). Fundamentals of human neuropsychology. New York, NY: Worth. Lassen, C. F., Mikkelsen, S., Kryger, A. I., Brandt, L. P. A., Overgaard, E., Thomsen, J. F., . . . Andersen, J. H. (2004). Elbow and wrist/hand symptoms among 6,943 computer operators: A 1-year follow-up study (The NUDATA study). American Journal of Industrial Medicine, 46, 521–533. doi:10.1002/ajim.20081 MacKenzie, I. S. (1992). Fitts’ law as a research and design tool in human– computer interaction. Human–Computer Interaction, 7, 91–139. Martens, J., & Antonenko, P. D. (2012). Narrowing gender-based performance gaps in virtual environment navigation. Computers in Human Behavior, 28, 809–819. http://dx.doi.org/10.1016/j.chb.2012.01.008 Mueller, F., & Lockerd, A. (2001, 2001). Cheese: tracking mouse movement activity on websites, a tool for user modeling. Proceedings of the SIGCHI conference on Human Factors in Computing Systems, 279–280. Nordander, C., Ohlsson, K., Balogh, I., Hansson, G.-Å., Axmon, A., Persson, R., & Skerfving, S. (2008). Gender differences in workers with identical repetitive industrial tasks: Exposure and musculoskeletal disorders. International Archives of Occupational and Environmental Health, 81, 939–947. doi:10.1007/s00420-007-0286-9 Passig, D., & Levin, H. (1999). Gender interest differences with multimedia learning interfaces. Computers in Human Behavior, 15, 173–183. Puts, D. A., McDaniel, M. A., Jordan, C. L., & Breedlove, S. M. (2008). Spatial ability and prenatal androgens: Meta-analyses of congenital adrenal hyperplasia and digit ratio (2D: 4D) studies. Archives of Sexual Behavior, 37, 100–111. Saleh, S. S., Fuortes, L., Vaughn, T., & Bauer, E. P. (2001). Epidemiology of occupational injuries and illnesses in a university population: A focus on age and gender differences. American Journal of Industrial Medicine, 39, 581–586. Scheirer, J., Fernandez, R., Klein, J., & Picard, R. W. (2002). Frustrating the user on purpose: A step toward building an affective computer. Interacting with Computers, 14, 93–118. Schumacher, P., & Morahan-Martin, J. (2001). Gender, Internet and computer attitudes and experiences. Computers in Human Behavior, 17, 95–110. doi:10.1016/s0747-5632(00)00032-7 Sillanpaa, J., Nyberg, M., Uitti, J., Takala, E.-P., Kivi, P., Kilpikari, I., & Laippala, P. (2010). Muscular activity in relation to support of the upper extremity in work with a computer mouse. International Journal of Human– Computer Interaction, 15, 391–406. doi:10.1207/S15327590IJHC1503_05 Silverman, I., Choi, J., & Peters, M. (2007). The hunter–gatherer theory of sex differences in spatial abilities: Data from 40 countries. Archives of Sexual Behavior, 36, 261–268. Spivey, M. J., Grosjean, M., & Knoblich, G. (2005). Continuous attraction toward phonological competitors. Proceedings of National Academy of Sciences of the United States of America, 102, 10393–10398.


GENDER DIFFERENCES IN MOUSE AND CURSOR MOVEMENTS Sussman, N. M., & Tyson, D. H. (2000). Sex and power: Gender differences in computer-mediated interactions. Computers in Human Behavior, 16, 381–394. doi:10.1016/s0747-5632(00)00020-0 Tay, B., Jung, Y., & Park, T. (2014). When stereotypes meet robots: The doubleedge sword of robot gender and personality in human–robot interaction. Computers in Human Behavior, 38, 75–84. http://dx.doi.org/10.1016/j.chb. 2014.05.014 Todman, J., & Day, K. (2006). Computer anxiety: The role of psychological gender. Computers in Human Behavior, 22, 856–869. doi:10.1016/j.chb.2004.03.009 Treaster, D., & Burr, D. (2004). Gender differences in prevalence of upper extremity musculoskeletal disorders. Ergonomics, 47, 495–526. Wahlström, J. (2005). Ergonomics, musculoskeletal disorders and computer work. Occupational Medicine, 55, 168–176. Wahlstrom, J., Svensson, J., Hagberg, M., & Johnson, P. W. (2000). Differences between work methods and gender in computer mouse use. Scandinavian Journal of Work Environment & Health, 26, 390–397. Watson, D., & Clark, L. A. (1999). The PANAS-X: Manual for the positive and negative affect schedule–Expanded form. University of Iowa: Department of Psychology Publications. Xiao, K., & Yamauchi, T. (2014). Semantic priming revealed by mouse movement trajectories. Consciousness and Cognition, 27, 42–52. Yamauchi, T. (2013). Mouse trajectories and state anxiety: Feature selection with random forest. Proceedings of the Humaine Association Conference on Affective Computing and Intelligent Interaction, 399–404. Yamauchi, T., & Bowman, C. (2014). Mining cursor motions to find the gender, experience and feelings of computer users. Proceedings of the IEEE International Conference on Data Mining (ICDM 2014): Workshop on Domain Driven Data Mining, 221–230. Yamauchi, T., Kohn, N., & Yu, N. (2007). Tracking mouse movement in feature inference: Category labels are different from feature labels. Memory & Cognition, 35, 852–863. Yu, N., Yamauchi, T., Yang, H., Chen, Y., & Gutierrez-Osuna, R. (2010). Feature selection for inductive generalization. Cognitive Science, 34, 1574–1593. Zander, T. O., Gaertner, M., Kothe, C., & Vilimek, R. (2010). Combining eye gaze input with a brain–computer interface for touchless human–computer interaction. International Journal of Human–Computer Interaction, 27, 38–51. doi:10.1080/10447318.2011.535752 Zhai, S. (2004). Characterizing computer input with Fitts’ law parameters—The information and non-information aspects of pointing. International Journal of Human–Computer Studies, 6, 791–809.

921

Zhai, S., Kong, J., & Ren, X. (2004). Speed–accuracy tradeoff in Fitts’ law tasks—on the equivalency of actual and nominal pointing precision. International Journal of Human–Computer Studies, 61, 823–856.

ABOUT THE AUTHORS Takashi Yamauchi, PhD, is an associate professor at Texas A&M University. His research addresses a wide range of issues in cognitive science and human–computer interaction including affective computing, brain–computer interface, and knowledge representation and decision making. Jinsil Hwaryoung Seo, PhD, is an interactive artist/ researcher at Texas A&M University. Her research focuses on tangible and kinetic aesthetics of human experience. She integrates physical and digital experiences through soft materials and interactive technologies. Her primary aim is to engage diverse audiences by developing interactive designs in education and healthcare. Noel Jett is a PhD candidate at the University of North Texas studying educational psychology. Her research interests concern the social and emotional needs of profoundly gifted students, particularly in relation to addiction, mental illness, and therapy. Greg Parks has a bachelor’s degree of science in psychology and sociology with a minor in history from Texas A&M University. His research interests include perception, cognitive processing, consciousness, race and labor relationships, and economic paradigms throughout history. Casady Bowman is a PhD student and received her master’s degree in cognitive psychology from Texas A&M University. Her research interests include emotion perception in music and speech, sound processing, and acoustic components of sound. She is conducting psychophysical experiments regarding adaptation of vocal and musical sounds.