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Laterality, spatial abilities, and accident proneness a

a

Susan D. Voyer & Daniel Voyer a

Department of Psychology, University of New Brunswick, Fredericton, NB, Canada Published online: 06 Jan 2015.

Click for updates To cite this article: Susan D. Voyer & Daniel Voyer (2015) Laterality, spatial abilities, and accident proneness, Journal of Clinical and Experimental Neuropsychology, 37:1, 27-36, DOI: 10.1080/13803395.2014.985191 To link to this article: http://dx.doi.org/10.1080/13803395.2014.985191

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Journal of Clinical and Experimental Neuropsychology, 2015 Vol. 37, No. 1, 27–36, http://dx.doi.org/10.1080/13803395.2014.985191

Laterality, spatial abilities, and accident proneness Susan D. Voyer and Daniel Voyer Department of Psychology, University of New Brunswick, Fredericton, NB, Canada

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(Received 21 October 2014; accepted 4 November 2014) Although handedness as a measure of cerebral specialization has been linked to accident proneness, more direct measures of laterality are rarely considered. The present study aimed to fill that gap in the existing research. In addition, individual difference factors in accident proneness were further examined with the inclusion of mental rotation and navigation abilities measures. One hundred and forty participants were asked to complete the Mental Rotations Test, the Santa Barbara Sense of Direction scale, the Greyscales task, the Fused Dichotic Word Test, the Waterloo Handedness Questionnaire, and a grip strength task before answering questions related to number of accidents in five areas. Results indicated that handedness scores, absolute visual laterality score, absolute response time on the auditory laterality index, and navigation ability were significant predictors of the total number of accidents. Results are discussed with respect to cerebral hemispheric specialization and risk-taking attitudes and behavior. Keywords: Laterality; Accident proneness; Injury liability; Handedness; Spatial abilities.

Injury liability or the likelihood of having repetitive injuries has been known as clumsiness and injury/accident proneness (Visser, Pijl, Stolk, Neeleman, & Rosmalen, 2007). This definition implies that some individuals are more likely than others to have accidents. Therefore, it is not surprising that injury liability is believed to result from personal factors in addition to environmental determinants (Bernacki, 1976). For example, unsafe environments lead to more opportunities for injury. However, the relation is not causal because, given the same environment, one person may have more accidents than another person. Visser et al. (2007) reviewed the evidence in favor of the existence of accident proneness. In retrieving articles for their meta-analysis, they discovered that there was a wide variety of definitions and operationalizations of the concept accident proneness. In spite of this, they were able to calculate odds ratios on the distribution of accidents and found that the number of accident-prone people was higher than expected by chance. Thus, Visser

et al. concluded that accident proneness does exist. This reflects an individual difference component that is the focus of the present article. Specifically, we are examining the question pertaining to specific factors that increase the likelihood of accidents, regardless of the environment. Laterality is one factor that is often mentioned in the literature as a correlate of accident proneness (Coren & Halpern, 1991; Dutta & Mandal, 2005). Laterality refers to the specialization of each cerebral hemisphere for specific functions, and it can also manifest itself in preference for a specific hand for particular activities (see e.g., Bryden, 1982). In fact, evidence suggests that handedness may relate to varying degrees of interaction between the cerebral hemispheres (Fallow & Voyer, 2013). In their review of handedness and life expectancy data, Coren and Halpern (1991) found that left-handers died earlier and had more accidents. Similarly, Dutta and Mandal (2005) reviewed 20 studies on handedness and accidents and found that nonright-handers were more accident prone. In an

The authors are indebted to Marquelle Inman for her assistance with data collection and scoring. This research was made possible by a research grant awarded by the Natural Sciences and Engineering Research Council of Canada (NSERC) to D. Voyer. Address correspondence to: Susan D. Voyer, Department of Psychology, University of New Brunswick, PO Box 4400, Fredericton, NB E3B 5A3, Canada (E-mail: [email protected]).

© 2014 Taylor & Francis

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empirical study, Mandal, Suar, and Bhattacharya (2001) provided a more complete picture of research by examining more than just handedness. These authors considered side bias and the lifetime frequency of accidents. Side bias included usage preference of hand, foot, eye, and ear. Mandal et al. found that mixed handers had more accidents than either strongly right- or strongly left-handers. However, the number of accidents was unrelated to foot, eye, or ear usage preference. Clearly, although handedness was an important factor in the Mandal et al. study, it seems to be the degree, not direction, of hand preference that mattered. If we assume that stronger hand preference reflects more pronounced hemispheric specialization, then these findings imply that those with weaker hemispheric specialization should be more liable to have accidents. Data such as those above suggest that laterality appears to be involved in accident proneness but the degree/direction debate is ongoing. Bhushan and Khan (2006) also considered side bias and accident records with a focus on locomotive drivers. They found that left-handed and left-footed locomotive drivers had more accidents than other hand/foot preference groups. They concluded from their findings that it was clearly the direction of laterality that was determinant, not the degree. However, they also suggested that the design of the locomotives may have been problematic for left-handers as they might essentially be designed for right-handers. Handedness as a measure of asymmetry is clearly an important correlate of accident proneness but it remains an indirect measure of cerebral organization. Therefore, an examination of the relation between laterality and accident proneness also requires more direct measures of cerebral specialization. From this perspective, we should also consider visual and auditory perceptual asymmetry as they might reflect more directly the underlying cerebral laterality. However, it appears that little research has been done with such tasks in the context of accident proneness, with most research focusing on measures of hand preference (Mandal et al., 2001). Accordingly, one purpose of the present study was to add to the existing literature by considering not only hand preference, but also more direct measures of perceptual asymmetries presumed to measure right and left cerebral specialization, respectively. Potentially one of the most robust measures of right-hemisphere function can be found in what is known as the Greyscales task. This task was created to examine free-viewing perceptual asymmetries and spatial attention (Nicholls, Bradshaw, &

Mattingley, 1999). It requires participants to discriminate relative brightness between two horizontal bars. The bars are left/right reversals of each other and degrade from black to white. They are presented with one above the other, and viewing time is unlimited. Strong hemispheric specialization typically manifests itself as a strong bias to the left side of the stimuli (i.e., such that participants perceive as brighter those stimuli in which the bright part is on the left). This finding has been replicated many times and possibly reflects the largest magnitude of laterality effects obtained in a right-hemisphere task according to a recent meta-analysis (Voyer, Voyer, & Tramonte, 2012). In addition, the Greyscales task is easy to administer. Thus, this is a good measure of visual asymmetry but it has yet to be studied in the context of accident proneness. Accordingly, two outcomes are possible. Either, as Mandal et al. (2001) found, participants with weaker hemispheric specialization will be more liable to accidents or, as reported by Bhushan and Khan (2006), those with a reversed bias (right rather than left bias) will be more prone to accidents. As a measure of verbal asymmetry, dichotic listening has demonstrated validity and reliability (Voyer, 2003). In addition, strong and reliable right-ear (left-hemisphere) advantages have been found using the Fused Dichotic Word Test (Wexler & Halwes, 1983). In this task, pairs of monosyllabic rhyming words in which each word varies in the initial consonant are presented dichotically (one word to each ear). However, due to partial interaural fusion, people generally experience and report hearing only one word. Despite this fusion, strong hemispheric specialization is reflected in a preference to report the word presented to the right ear. As with visual asymmetry, two competing outcomes are possible: Either participants with weaker hemispheric specialization will be more liable to accidents or those with a reversed bias (left- rather than right-ear advantage bias) will be more prone to accidents. So far, we have focused on hemispheric specialization as an important factor in injury proneness; however, basic cognition and skills acquisition have also been shown to relate to accident proneness. For example, Deconinck, Spitaels, Fias, and Lenoir (2009) studied children diagnosed with a developmental coordination disorder. Essentially, these children demonstrated coordination and control problems with basic motor skills like running and jumping. Based on their view that motor imagery is an important skill that may be impaired for these children, Deconinck et al. examined this skill by means of a mental rotation paradigm. Their

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results indicated that children who displayed “clumsy” motor behavior had lower accuracy scores and slower response times in mental rotation. Deconinck et al. concluded that motor imagery may be less accessible or of a reduced quality in these children. This leads to the prediction for the current study that people who have more accidents will also have lower accuracy scores on a mental rotation test. Of course, a mental rotation test is also a measure of spatial abilities (Linn & Petersen, 1985), suggesting that there might be a connection between these abilities and developmental coordination problems. However, we hypothesize that one critical factor that might be linked to difficulties in accident-prone individuals is in assessing where their body is located in the environment and how they navigate the environment. Accordingly, we considered another measure of spatial ability relevant to spatial navigation, the Santa Barbara Sense of Direction Scale (Hegarty, Richardson, Montello, Lovelace, & Subbiah, 2002). This questionnaire offers most relevance as a measure of environmental awareness. In this case, we expect that people who have a better sense of their environment will report fewer accidents. Finally, after spending some time discussing spatial abilities, it would be inappropriate to ignore sex as a potential factor relevant to accident proneness. Sex differences (in favor of males) in spatial abilities are well documented (Voyer, Voyer, & Bryden, 1995). In addition, Visser et al. (2007) suggested that sex is an important predictor of accident proneness as males tend to be more accident prone than females. Sex of the participants will therefore have to be considered in all data analyses.

The current study After finding multiple definitions of injury liability, Visser et al. (2007) recommended that the operational definition of injury liability should be “accidents leading to injuries requiring medical care” (p. 562). They also recommended that researchers should consider separately the various contexts in which accidents take place (traffic, work, sports, and home) to allow further exploration of causes of accidents and exposure to risk. The present study examined the relations between laterality, spatial ability, and accident proneness. Although degree or direction of laterality could be influential, our working hypothesis is that it will be the degree, not the direction, of laterality that will be implicated in accident proneness. Furthermore, we expect that better mental rotation and navigation abilities will result in fewer accidents reported.

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METHOD Participants Initially 143 Introductory Psychology students were recruited and received bonus credit for their course. However, three participants did not complete all the questionnaires so this left a final sample of 140 participants (57 men and 83 women). The age ranged from 17 to 40 years (M = 19.7, SD = 2.8). All reported normal hearing and normal or corrected-to-normal vision. The experiment followed proper ethical guidelines and was approved by the institutional research ethics board.

Materials and procedure Participants were tested in groups of up to four people and were separated from their neighbors by cubicle screens. Each participant was seated at a Dell Optiplex computer with Sony dynamic stereo headphones. The participants were asked to complete the mental rotation test, the Santa Barbara Sense of Direction measure, the Greyscales task, the Fused Dichotic Word Test, the Waterloo Handedness Questionnaire, and the grip strength task (as a further measure of hand preference). The order of presentation of these tasks was random but the demographic questionnaire was always administered last. The Waterloo Handedness Questionnaire (Steenhuis & Bryden, 1989) asks 32 questions about the use of left and right hand to perform various activities. The score is totaled and can range from – 64 (left hand always) to +64 (right hand always). A physical measurement of grip strength in kilograms was obtained once for each hand. Participants were asked to stand with their arms at their sides and to grip the Lafayette Instrument grip meter (model 78010) with one hand only and squeeze. They were then asked to switch hands. Participants were not instructed which hand to measure first. The Santa Barbara Sense of Direction Scale (SBSOD; Hegarty et al., 2002) asks 15 questions about spatial and navigational abilities, preferences, and experiences such as giving directions and reading maps. Based on a 7-point Likert scale, responses range from strongly agree to strongly disagree. Total scores range from 15 to 105 with higher scores indicating better navigational abilities. The mental rotation test (MRT; modified to have an equal number of various item types, see Doyle & Voyer, 2013) is a spatial ability test requiring participants to mentally rotate 3D figures. The test is composed of 24 items and requires matching two of

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the four options with the target. A time limit of 6 min was given. Scoring of each item as correct required both correct options to be chosen. The Greyscales task computer program was retrieved from http://www.flinders.edu.au/sabs/ psychology/research/labs/brain-and-cognition-labora tory/the-greyscales-task.cfm (Nicholls & Mattingley, 2013). The default settings of interstimulus interval (1500 ms), exposure duration (2000 ms), darkness judgment, and luminance difference (0) were used but the number of trials was set at 72. Each trial required a forced two-choice brightness discrimination of two simultaneously presented images. The horizontal bars are left/right reversals of each other and degrade from black to white. Participants indicated which stimulus appeared darker overall by pressing the t or b key to indicate their choice of top or bottom stimulus. The index fingers of each hand used to press the keys were counterbalanced across participants. Note that our exclusive reliance on a darkness judgment was justified by findings that the type of judgment (darkness or brightness) does not affect the magnitude of the observed bias (Voyer, Saint-Aubin, & Cook, 2014). The Fused Dichotic Word Test (Wexler & Halwes, 1983) requires participants to listen to pairs of monosyllabic rhyming words in which the first letter of each word varies. A different word is presented to each ear, and participants indicate which word they heard clearest amongst four options. Participants completed four practice trials followed by 60 experimental trials. Headphone placement was randomized between participants. A program using E-prime Version 1.1 (Schneider, Eschman, & Zuccolotto, 2002a, 2002b) recorded responses and response times (in ms). The demographic questionnaire was designed for the current study and asked eight questions. In addition to age and sex, participants were asked “Please indicate the number of accidents during your lifetime that have required medical attention for yourself that were….” Five categories were specified: homerelated accidents, traffic-related accidents, workrelated accidents, sports-related accidents, and other. Finally, participants were asked to rate how clumsy they were on a 7-point Likert scale ranging from 1 not at all clumsy to 7 clumsy.

RESULTS Data analyses proceeded in three steps. First, the data were examined for missing data and outliers across the various measures of relevance. Only one participant had one missing answer on the SBSOD. As the score on this test is the total of

all items, it was totaled as is for that participant. Inclusion or exclusion of this participant did not change the results. Accordingly, this participant was preserved in the final sample. An outlier was defined as a score falling away from the sample mean by more than 3.29 standard deviations, as recommended by Tabachnick and Fidell (2007). Based on this criterion, nine outliers were identified across all measures. Data analyses with and without these outliers showed that it changed the results in meaningful ways. Accordingly, outliers were excluded from the final sample (final sample n = 131), and the reported results reflect their exclusion. Second, preliminary analyses were conducted to examine distribution of scores, observance of assumptions underlying the analytic methods, sex differences on the various measures, and that expected laterality effects were observed on the Greyscales and dichotic tasks. Finally, the primary analysis involving a multivariate multiple regression analysis was conducted with accident measures and clumsiness rating as dependent variables, and handedness score, greyscale bias, dichotic laterality effect on accuracy and response time, SBSOD score, MRT score, and grip strength difference between the hands as predictors, with sex partialed out. Laterality scores were computed as a simple right minus left difference for the dichotic task and hand grip measure. Positive scores therefore reflected a right advantage on accuracy and a leftear advantage for response time. For the Greyscales task, the laterality index was computed as recommended by Nicholls et al. (1999), with the following equation: [(36 – number of left choices)/ 36] × 100, reflecting the presentation of a total of 72 trials. A positive score therefore reflected a right bias. All laterality indices were used as predictors in their raw forms as well as in their absolute values to allow a comparison of direction and degree of laterality, respectively. Preliminary analyses Distribution of scores Table 1 shows summary data pertaining to the various measures of accident proneness, whereas Figure 1 illustrates the distribution of raw scores on the Waterloo Handedness Questionnaire. The most striking aspect of the data in Table 1 is that, in most cases, zero is by far the most frequent category. This renders individual analysis of these separate scores meaningless for the primary analysis. Accordingly, these values were totaled to reflect

ACCIDENT PRONENESS

Test of assumptions

TABLE 1 Descriptive statistics for accidents Accident type

Zero responses (%)

Range

M

SD

55.7 77.1 80.2 27.5 94.7 15.3

0–10 0–3 0–5 0–25 0–6 0–27

1.15 0.27 0.30 2.99 0.10 4.81

2.02 0.57 0.75 4.23 0.58 5.38

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Home Traffic Work Sports Other Total

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For the regression analysis, likely the most crucial assumption is that of normality. Accordingly, one-sample Kolmogorov–Smirnov tests of normality were conducted on all measures involved in the primary analysis (both dependent and predictors). This analysis showed significant deviation from normality (with p < .05) for the Waterloo Handedness Questionnaire, MRT, total number of accidents, absolute greyscale bias score, absolute grip differential, and absolute laterality index on response time for the dichotic task. A square root transformation achieved normality on all scores where such a transformation was possible (all except the handedness score). For the Waterloo Handedness Questionnaire, transformation as ranks achieved normality. Therefore, the primary analysis was conducted on these transformed values. Sex differences

Figure 1. Distribution of raw scores on the Waterloo Handedness Questionnaire.

the sum of all accidents for each participant. As also seen in Table 1, this approach produced a better range of scores to allow meaningful analyses. The data in Figure 1 show that there was a fairly broad distribution of scores, although few left-handers were identified (score below zero: n = 7).

Considering the violations of assumptions described above, sex differences in all measures were examined by means of Mann–Whitney U, in which this assumption is not relevant. Mean scores by sex relevant to this analysis are presented in Table 2. Results of the nonparametric test of significance showed significantly larger scores for males on the MRT, SBSOD, total number of accidents, and absolute laterality index on the dichotic task (all ps < .03), whereas females rated themselves as clumsier than did males (p < .001). The pervasiveness of sex differences underlines the need to consider this factor as a covariate in the primary analysis.

TABLE 2 Means and standard deviations for each measure as a function of sex Measures Total accidents* Clumsiness rating** Mental Rotation Test* Santa Barbara Sense of Direction* FDWT right bias Absolute bias in FDWT* RT bias (L–R) in FDWT Absolute RT bias (L–R) in FDWT Grip strength differential Absolute grip differential Waterloo handedness score (WHQ) Absolute WHQ Bias on Greyscales task Absolute bias on Greyscales task

Men 6.89 2.80 9.09 70.71 10.60 12.96 51.00 136.70 0.96 5.18 30.76 34.07 –19.65 33.99

(6.33) (1.38) (4.67) (13.47) (10.68) (7.57) (168.77) (110.00) (6.44) (3.88) (20.21) (13.77) (38.21) (26.00)

Women 3.30 3.93 5.37 62.09 7.29 9.66 63.33 112.49 1.75 3.78 34.71 36.84 –28.11 41.19

(3.98) (1.53) (2.84) (13.95) (10.07) (7.79) (131.03) (91.72) (4.30) (2.68) (17.53) (12.35) (40.03) (26.14)

Overall 4.81 3.46 6.93 65.71 8.68 11.05 58.15 122.65 1.42 4.37 33.05 35.68 –24.55 38.17

(5.38) (1.57) (4.14) (14.35) (10.42) (7.84) (147.56) (100.11) (5.30) (3.30) (18.73) (12.99) (39.35) (26.22)

Notes. FDWT = Fused Dichotic Word Test; RT = reaction time; L = left; R = right. N = 55 men and 76 women. Standard deviations in parentheses. *p < .05. **p < .001.

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Laterality effects A one-sample t test on the overall bias score on the Greyscales task showed a significant left bias (M = –24.55, SD = 39.35), t(130) = –7.14, p < .001. For the dichotic task, separate repeated measures analyses of variance for accuracy and response time with ear of presentation as the independent variable produced a significant effect of ear in both cases [accuracy: F(1, 130) = 19.71, p < .001, η2p = .132; response time: F(1, 130) = 4.50, p = .036, η2p = .033]. This reflected more accurate and faster performance for the right ear (accuracy: M = 29.47, SD = 6.74; response time: M = 810, SD = 280) than for the left ear (accuracy: M = 24.57, SD = 6.19; response time: M = 840, SD = 314). These findings establish the presence of laterality effects in the expected direction for these two tasks.

Primary analysis: Multivariate regression analysis As a starting point to the multivariate regression analysis, zero-order correlations among all variables involved are presented in Table 3. The multicollinearity that is visible among these variables was taken into account by means of simultaneous entry of all predictors. In this way, observed significant findings reflect the influence of a given variable over and above the influence of all other variables considered (Keith, 2006). Results of the regression analysis showed that for the total number of accidents, the Waterloo Handedness Questionnaire score (b = –0.013,

p = .039), absolute Greyscales task bias score (b = –0.010, p = .026), absolute response time laterality index on the dichotic task (b = 0.031, p = .014), and the SBSOD score (b = 0.021, p = .003) were significant predictors. For the clumsiness rating, only the SBSOD (b = –0.021, p = .025) was a significant predictor. No other predictors achieved significance with p < .05 (all ps > .09). DISCUSSION The purpose of the present study was to examine the relation among laterality, spatial abilities, and accident proneness. Specifically, we considered measures of handedness, hand strength laterality, auditory laterality, and visual laterality as potential predictors of injury proneness. Our working hypothesis was that degree, not direction of laterality, would be a significant predictor of accident proneness (Hypothesis 1). Spatial abilities were also examined as possible predictors, in the form of mental rotation and navigation measures. In this case, we expected that better spatial skills would result in fewer accidents (Hypothesis 2). Considering the important influence of sex on spatial abilities (Voyer et al., 1995), sex was also examined, but more as a variable that had to be used as a covariate. Interestingly, the first hypothesis was only partially confirmed. Specifically, for handedness, it was direction that mattered as strong right-handers were less accident prone than mixed-handers or left-handers. These findings are contrary to those reported by Mandal et al. (2001) but in agreement

TABLE 3 Zero-order correlations among measures Variables 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

2

3

4

5

6

7

8

9

10

11

12

13

14

–.10

–.18* .19*

–.11 .23** .92***

.09 .03 –.10 –.06

–.20* .06 .01 .03 –.58***

.07 –.01 .05 –.03 .14 –.16

.08 –.03 .05 –.02 .12 –.14 .76***

–.03 .11 –.03 –.06 .07 –.07 .17 .06

–.16 –.10 .01 –.04 .02 –.09 .20* .22* .47***

.02 .09 .30** .26** –.02 –.07 .18* .09 .11 –.02

.19* .02 .03 .03 .06 –.05 .16 .19* .05 .03 .27**

.36*** –.30** –.17* –.16 .11 –.10 .07 .06 –.04 –.01 –.05 .06

.16 –.20* –.03 .02 .18* –.11 .23** .24** –.01 .05 .07 .15 .13

Notes. 1 = total accidents; 2 = clumsiness rating; 3 = Waterloo Handedness score; 4 = absolute Waterloo Handedness score; 5 = Greyscales bias; 6 = absolute Greyscales bias; 7 = Fused Dichotic Word Test bias; 8 = absolute Fused Dichotic Word Test bias; 9 = reaction time bias in Fused Dichotic Word Test; 10 = absolute reaction time bias in Fused Dichotic Word Test; 11 = grip strength differential; 12 = absolute grip strength differential; 13 = Santa Barbara Sense of Direction scale; 14 = Mental Rotation Test. *p < .05. **p < .01. ***p < .001.

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with what Bhushan and Khan (2006) found. Bhushan and Khan suggested environmental design as a potential factor in their results with train drivers. As was mentioned in the introduction, Bhushan and Khan argued that the locomotive cab design advantaged right-handed participants. However, in the present study, this is not an obvious methodological problem as we relied on self-report and objective measures. Furthermore, cancellation of collinearity through the simultaneous entry of predictors as performed here cannot account for our finding as, in Table 3, the handedness score correlated significantly with the number of accidents, whereas the absolute score did not. Therefore, the intercorrelation between raw and absolute handedness scores is not a plausible reason for the observed relation. As mentioned earlier, our results are at variance with those reported by Mandal et al. (2001), who found that degree of handedness was more important than direction. However, the influence of handedness in a given study may depend upon how it is measured (Dutta & Mandal, 2005). For example, Mandal et al. asked 22 questions about hand use whereas we asked 32 questions. Furthermore, it is not only the quantity of questions but perhaps the type of questions that matters. When comparing the items in the handedness questionnaire that we used to the ones utilized by Mandal et al., there were only 11 similar questions. Therefore, although the predictive value of handedness when we disregard the degree/direction distinction suggests that it is an important and easily measured correlate of accident proneness, it may also be unduly affected by the way it is measured and the type of questions asked. In reality, handedness should not be considered in isolation as external factors such as living in an environment designed for right-handers could affect hand usage (Bhushan & Khan, 2006; Coren & Halpern, 1991). The novelty of the present study was that it examined more direct measures of cerebral specialization as we considered visual and auditory perceptual asymmetries. For these tasks, our results showed that it was not the direction but the degree of lateralization that was important. Smaller left visual bias and smaller auditory laterality index on reaction time were associated with more accidents. Thus, as predicted, people with weak rather than strong hemispheric specialization were more prone to accidents. These measures provide a much more direct estimate of laterality and likely reflect a more valid measure of the underlying cerebral organization than handedness scores. In fact, it is noteworthy that neither the raw handedness score nor the absolute handedness score correlated with

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any of the measures of perceptual asymmetries (see Table 3). This questions the hypothesized relation between hand preference and perceptual asymmetries (e.g., Bryden, 1982) or suggests the presence of nonlinearity in this relation. Regardless of this aspect, the present results provide stronger evidence for a degree relationship between laterality and injury proneness rather than one in terms of direction. There has been very little theorizing on the link between accident proneness and laterality as research has been mostly descriptive (e.g., Bhushan & Khan, 2006). However, in a meta-analysis on information processing deficits in children with developmental coordination disorder, Wilson and McKenzie (1998) suggested that a deficit in processing sensory information could result in poor motor coordination, resulting in a higher risk for accidents. From this perspective, our findings suggest that interhemispheric integration, as reflected in degree of laterality, might relate to the processing of sensory information, which would then affect accident proneness. As briefly mentioned earlier, handedness is believed to relate to varying degrees of interaction between the cerebral hemispheres (Fallow & Voyer, 2013). In particular, individuals with lower absolute handedness scores typically perform better at interhemispheric integration tasks than those with higher absolute scores (Christman, 2001; Christman & Propper, 2001; Propper, Christman, & Phaneuf, 2005). Therefore, this raises the possibility that our findings of more accidents in those with smaller laterality effects might reflect the integration of conflicting sensory information from the two hemispheres, resulting in accidents in the environment. This means that keeping sensory information relatively separate in the two hemispheres may result in fewer accidents. Of course, this is only a speculation, but one clear finding in our data is that greater lateralization relates to a smaller number of accidents. Structural and functional neuroimaging studies examining interhemispheric communication as a function of accident proneness would shed further light on the tentative account provided here. In another novel aspect of the present study, navigation skills were measured with the Santa Barbara Sense of Direction scale. Contrary to our prediction, this component produced paradoxical results as higher scores were associated with more accidents but lower clumsiness ratings even when sex was partialed out through simultaneous regression. Therefore, the influence of self-reported navigation skills as assessed with the Santa Barbara Sense of Direction scale accounts for variance over and above the influence of sex. This novel finding begs the question of whether good

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navigational skills could lead to more risk taking, resulting in a higher number of injuries. Kontos (2004) would probably view a positive answer to this question as plausible. Kontos studied adolescent soccer players and found a positive relationship between estimation of ability and risk taking. He also found an inverse relation between risktaking behaviors and self-ratings on the perceived likelihood of being injured. Unfortunately, risktaking attitudes or behaviors were not measured in the present study but examination of this factor as a possible third variable in the relation between navigation abilities and injury proneness represents an interesting avenue for future work. Not surprisingly, men had better mental rotation and navigation skills, replicating the well-known sex differences in such tasks (Voyer et al., 1995). Similarly, the larger absolute auditory laterality index in males fits with reports of small sex differences in degree of laterality (Voyer, 1996; Voyer & Doyle, 2012). More interestingly, men had more accidents but women rated themselves as clumsier. Why women would view themselves as clumsier when men have more accidents is a rather puzzling question. One possibility might relate to the previously mentioned issues relevant to risk-taking behavior. Essentially, sex differences are consistently reported in a variety of risk-taking activities, with males generally taking more risks (Byrnes, Miller, & Schafer, 1999). It is possible that those who take risk underestimate their likelihood of getting injured, as reported by Kontos (2004) in the aforementioned study of soccer players. This would account for the finding that men view themselves as less clumsy than women do. Essentially, individuals who view themselves as clumsy likely take fewer risks than those who have a lower clumsiness rating. This possibility could easily be examined in future work by assessing risk-taking behavior along with a simple self-rating of clumsiness as used here. One implication of males’ greater risk-taking behavior is that it might lead to them spending more time engaged in dangerous activities than do females, leading to more opportunity for accidents. This possibility finds support in Seccombe’s (1993) report that men are exposed to dangerous working conditions more often than women. However, this finding is probably a correlate of males’ greater propensity to take risks so that risk-taking likely remains the primary explanatory factor. For example, Harris, Jenkins, and Glaser (2006) reported that men are three times more likely than women to have a car accident. In accounting for this finding, Harris et al. suggested that, although average time spent driving was

probably a relevant factor, the overrepresentation of men in car accidents is more likely due to the fact that they engage in riskier behaviors like using their seatbelts less and running more yellow lights than women. In reality, it is important to remember that sex differences were not the focus of the present study, and this is the reason why sex was used as a covariate in the regression analysis. Nevertheless, any study focusing on sex differences should consider the amount of time spent engaged in risky behaviors to account more precisely for potential differences between the sexes in terms of accident proneness. When considering the limitations of the present study, the large percentage of zero responses for each type of accident must be kept in mind. This meant that we had to combine the number of accidents for an overall total, resulting in a loss of information. Essentially, we initially considered the location where the accident happens based on the notion that it would produce more fine-grained interpretations of the results. However, in practice, this proved impossible, suggesting that such a detailed analysis is simply not practical without broadening the definition of accidents to encompass those that result only in minor injuries (i.e., do not require medical assistance beyond what is available at the location of the accident). In a similar vein, the small number of left-handers in the sample can be seen as another limitation of the present study. Specifically, as previously mentioned in relation to Figure 1, there were only seven left-handers in the sample. This does reduce the power of the analysis that considered the direction of handedness as a relevant predictor. From this perspective, one has to wonder whether having more left-handers would affect the results in meaningful ways. However, the fact that we did find that direction of handedness, not degree, was a significant predictor of the total number of accidents suggests that the scarcity of left-handers in our sample likely had minimal effects on the observed results. Another limitation of the present study stems from a methodological choice that we had to make. The use of self-report measures of accident proneness and clumsiness may be influenced by social desirability or stereotypes. For example, left-handers may be aware of the clumsy left-hander stereotype (Grimshaw & Wilson, 2013). However, the positive correlation between the clumsiness rating and the direction of handedness (raw handedness score; see Table 3) actually argues against this possibility. In reality, this finding suggests that right-handers typically rated themselves as clumsier. It is therefore unlikely that social

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desirability or stereotypes account for the present findings relevant to clumsiness ratings. Multicollinearity is another limitation of the present study. Essentially, as seen in Table 3, many of the predictors considered in the regression analysis were correlated with each other. We used simultaneous entry of predictors as a way to control to some extent for this collinearity as this approach produces results that reflect the predictive value of each variable as if it was entered last in the regression equation (Keith, 2006). Essentially, this means that the influence of each variable was estimated when all other factors were partialed out. This seemed like a reasonable approach considering that no strong theoretical model was available to justify hierarchical entry of predictors. In contrast, stepwise regression, often presented as useful in an exploratory context (Keith, 2006), has been criticized for its lack of replicability and validity (Thompson, 1995). Therefore, the present results provide a solid idea of what factors are most important among the ones we examined. However, we need to keep in mind that intercorrelation among measures might have limited the number of significant predictors in the primary analysis. In conclusion, the present study extended findings from previous research that relied exclusively on measures of handedness to direct measures of perceptual asymmetry as well as measures of spatial abilities. Absolute visual and auditory perceptual asymmetries were found to relate to the number of accidents experienced in one’s lifetime. Specifically, participants with stronger hemispheric specialization had fewer accidents. As a potential factor underlying these findings, we suggested that differences in interhemispheric integration as a function of degree of laterality could account for accident proneness. Much more work is required to elucidate the structural and neurofunctional correlates of injury proneness as well as the role of risk-taking attitudes and behavior.

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