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Visual search strategies of children with and without autism spectrum disorders during an embedded figures task
Research in Autism Spectrum Disorders 8 (2014) 463–471
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Research in Autism Spectrum Disorders Journal homepage: http://ees.elsevier.com/RASD/default.asp
Visual search strategies of children with and without autism spectrum disorders during an embedded figures task Chiara Horlin a, Matthew A. Albrecht b, Marita Falkmer a,c, Denise Leung a, Anna Ordqvist d, Tele Tan e, Wee Lih Lee f, Torbjorn Falkmer a,d,g,* a
School of Occupational Therapy & Social Work, CHIRI, Curtin University, GPO Box U1987, Perth, WA 6845, Australia School of Psychology & Speech Pathology, CHIRI, Curtin University, GPO Box U1987, Perth, WA 6845, Australia School of Education and Communication, CHILD Programme, Institute of Disability Research, Jo¨nko¨ping University, Sweden d Rehabilitation Medicine, Department of Medicine and Health Sciences (IMH), Faculty of Health Sciences, Linko¨ping University & Pain and Rehabilitation Centre, SE-581 85 Linko¨ping, Sweden e Department of Mechanical Engineering, Curtin University, GPO Box U1987, Perth, WA 6845, Australia f Department of Electrical and Computer Engineering, Curtin University, GPO Box U1987, Perth, WA 6845, Australia g School of Occupational Therapy, La Trobe University, Melbourne, VIC 3086, Australia b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 11 October 2013 Received in revised form 14 January 2014 Accepted 21 January 2014
Individuals with ASD often demonstrate superior performance on embedded figures tasks (EFTs). We investigated visual scanning behaviour in children with ASD during an EFT in an attempt replicating a previous study examining differences in visual search behaviour. Twenty-three children with, and 31 children without an ASD were shown 16 items from the Figure-Ground subtest of the TVPS-3 while wearing an eye tracker. Children with ASD exhibited fewer fixations, and less time per fixation, on the target figure. Accuracy was similar between the two groups. There were no other noteworthy differences between children with and without ASD. Differences in visual scanning patterns in the presence of typical behavioural performance suggest that any purported differences in processing style may not be detrimental to cognitive performance and further refinement of the current methodology may lead to support for a purported advantageous cognitive style. ß 2014 Elsevier Ltd. All rights reserved.
Keywords: ASD Eye tracking Embedded figures test Visual search
1. Introduction Although not currently included in the formal behavioural criteria for a diagnosis of autism spectrum disorders (ASD), there is increasing support for sensory atypicalities as a primary or secondary contributing factor to ASD symptomatology. Reports of atypical sensory experiences include the enhanced processing of low-level sensory information or local stimulus features of viewed objects (Iarocci & McDonald, 2006). This processing enhancement, or preference/bias, for low level features was earlier suggested to come at the expense of integrating individual features, e.g., the Weak Central Coherence model (WCC; Happe´, 1996). However, the evidence for deficits in global processing is not as robust as the evidence for enhanced, or at least spared, low level processing (Happe´ & Frith, 2006).
* Corresponding author at: School of Occupational Therapy and Social Work, Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University of Technology, GPO Box U1987, Perth, WA 6845, Australia. Tel.: +61 8 9266 9051; fax: +61 8 9266 3636. E-mail address: [email protected] (T. Falkmer). 1750-9467/$ – see front matter ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.rasd.2014.01.006
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Nevertheless, enhanced/preferential processing of local features has often been suggested to play a significant role in the enhanced performance, or preferential bias towards, select domains in people with ASD. For example, children with autism display a preference for visuo-spatial, rather than temporal, processing and this preference may impart superior performance on tasks that necessitate spatial cues or codes (O’Connor & Hermelin, 1978). This is consistent with clinical and experimental observations whereby individuals with ASD notice and react to minute details in the environment that are not noticed by others and react with surprising speed (Shah & Frith, 1983). In more controlled settings/tasks, enhanced (or at least comparable) performance has been observed in a range of visuospatial tasks including Block Design (Shah & Frith, 1993), copying of impossible figures (Mottron, Belleville, & Menard, 1999) and tasks involving visual search and target detection (Joseph, Keehn, Connolly, Wolfe, & Horowitz, 2009; O’riordan, 2004; O’Riordan, Plaisted, Driver, & Baron-Cohen, 2001). Individuals with ASD have also been found to be less susceptible to the Ebbinghaus illusion (Happe´, 1996) and tolerate higher levels of luminance noise while still being able to detect the orientation of a target figure (Bertone, Mottron, Jelenic, & Faubert, 2005). Resistance to these illusions may be especially strong in individuals with high levels of systematising type behaviours, rather than a trait that is related to ASD per se (Walter, Dassonville, & Bochsler, 2009). However, the most oft-cited task to demonstrate enhanced performance in individuals with ASD is the embedded figures task (EFT; Jolliffe & Baron-Cohen, 1997; Ring et al., 1999; Shah & Frith, 1983). EFTs involve the detection of a smaller stimulus embedded within a larger pattern or image, sometimes within an array of distractor images. EFTs are typically held as the prototypical example of preserved cognitive skill in many individuals with autism. Superior, or at least comparable, performance on EFTs has been observed in various stages of development and across the autism spectrum when individuals with ASD are compared to chronological aged, mental-age, verbal ability, and IQ matched controls (Jolliffe & Baron-Cohen, 1997; Ring et al., 1999; Shah & Frith, 1983). The most dramatic differences in performance between individuals with ASD and other clinical groups, or typically developed controls, are usually seen in the response times. Individuals with ASD typically perform the EFT at usually comparable (and sometimes superior) levels of ˜ a, 2011). Similarly, strategies used to perform the EFT are also accuracy and often with faster response times (White & Saldan known to differ between individuals with ASD and other groups (Shah & Frith, 1983). However, little is known about whether visual scan strategies are ASD specific or not. Analyses of visual scanning patterns via eye-tracking data can give insight into both the top-down and bottom-up processing that takes place during experimental tasks. To date, only one study has used eye trackers during an EFT in children with ASD (Keehn et al., 2009). It found no overall performance differences between children with (n = 12) and without (n = 11) ASD, and no differences in the number of fixations made per trial (Keehn et al., 2009). However, children with ASD made fixations of shorter duration. Further analyses of the location of fixations found that the shorter fixations were located on the figure containing the embedded target. Thus, children with ASD exhibited shorter fixations when attempting to locate the target within a complex array. The authors also found that children with ASD were faster at initially encoding stimulus features, as indicated by shorter fixation durations during the first fixation on each item. In an attempt to replicate these findings, the current study examined the visual scanning behaviour of children with and without ASD while disembedding figures. To further elucidate the visual scanning patterns of children with and without ASD, a larger sample size than that of the previous study was recruited. The Figure-Ground subtest from the Tests of Visual Perception Skills – 3rd edition (TVPS-3, Martin, 2006) was used as a more complex measurement of embedded figure performance, with multiple response options per item and an increasing degree of difficulty throughout the task. We were particularly interested in differences in accuracy and search strategy between the two groups, thus, presentation of the stimuli was not constrained in order to avoid any impedance on performance due to time pressure. 2. Methods 2.1. Participants Recruitment of children with and without ASD was conducted through the Telethon Institute of Child Health Research, personal contacts, a number of local primary schools, as well as radio and newspaper advertisements throughout the Perth metropolitan area in Western Australia. Inclusion criteria specified the absence of comorbid cognitive conditions in children with ASD, and all participants were required to read and understand written and verbal instructions in English. Medical records were sighted to confirm ASD diagnosis. In total 23 (4 female) children with ASD participated in the study, with 21 (4 female) contributing full datasets, and 32 (7 female) control children. One female control was excluded to improve the sex and age balance across groups. The removal of this participant did not influence the results reported. All children were aged 8–13 years (ASD’s mean age = 10.7 years, SD = 1.2; controls’ mean age = 10.6 years, SD = 1.2). There was a significant difference in age between boys (mean age = 10.8 years, SD = 1.2, median = 11.0 years) and girls (mean age = 10.0 years, SD = 1.0, median = 9.7 years) (t(15.3) = 2.2, p = 0.045), however no interaction between sex and diagnostic group for age (F(1,50) = 2.4, p = 0.13), thus there are no significant age differences across groups within genders. 2.2. Materials/apparatus The number of fixations and fixation durations were recorded with a head-mounted Arrington ViewPointTM eye tracker operating at 60 Hz. The eye tracker was worn over participants’ glasses if necessary. After a screening procedure to verify that
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each participant’s eyes moved smoothly and congruently, the eye tracker was set to record movements of one eye only, usually the right. In order to reduce systematic error and increase the accuracy of the results, the participant’s head was stabilised using an Arrington Ultra Precision Head PositionerTM. Participants were seated one metre from a 4200 flat screen television, which displayed the stimuli. A 16-point calibration of the eye tracker was conducted for each participant prior to commencing the primary experimental task. To ensure that calibration was maintained throughout the trial, participants were instructed to fix their gaze to a central fixation point (represented by a black dot on a white background) between stimuli items. If calibration was not maintained, a re-calibration took place. 2.2.1. The stimuli The participants were shown two practice items and 16 individual test items, each taken from the Tests of Visual Perception Skills Figure-Ground subtest (TVPS-3, Martin, 2006) in the original order of the test. Each slide consisted of a target figure displayed in the centre of the top half, as shown in Fig. 1. The bottom half of each slide displayed four alternative response choices labelled between 1 and 4. The target figure could be found embedded in only one of these response options. A distinguishing feature of this subtest is an increasing degree of difficulty across items, as demonstrated in the difference in complexity of the response options between items 1 (top) and 16 (bottom) shown in Fig. 1.
Fig. 1. Test items 1(above) and 16 (below) from the Figure-Ground subtest of TVPS-3 (Martin, 2006).
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2.3. Procedure Before the onset of the task, participants were reminded of the task instructions by the primary experimenter. Instructions were delivered verbally and pictorially and participants were reminded they could withdraw at any point without explanation. After a screening procedure to assess visual acuity (Albrecht et al., 2014; Falkmer, Bja¨llmark, Larsson, & Falkmer, 2011; Falkmer, Stuart, et al., 2011) and verify that each participant’s eyes moved smoothly and congruently, participants were seated comfortably in the chin-rest and the eye-tracker was calibrated. Following successful calibration, and prior to administration of the 16 test items, two items were administered as practice trials to ensure full understanding of the task requirements. Immediately before the presentation of each item, a fixation point was presented in the middle of the screen to cue the participant to the upcoming item. This fixation point also confirmed the calibration of the eye-tracker, in accordance with previous studies (Falkmer, Bja¨llmark, et al., 2011; Leung, Ordqvist, Falkmer, Parsons, & Falkmer, 2013). Eye-tracking data were continuously recorded throughout the task; however, the onset and close of recordings for each item were locked to the time point at which the stimuli were first presented until a response was made. Participants responded by pointing at, or by verbally indicating which of the four response options (labelled 1, 2, 3 and 4 in Fig. 1) they thought was correct. As soon as a response was made, the participant progressed to the next item. 2.3.1. Classification of fixation areas As shown in Fig. 1, each item was classified into five different areas of interest, i.e., target figure, correct response and incorrect left, middle or right alternatives. Fixations on none of the aforementioned areas of interest were classified as ‘‘other’’. The location of the participants’ fixations was determined by a fixation generation programme that uses a centroid mode algorithm used in a similar study with adults with ASD (Falkmer, Dahlman, Dukic, Bja¨llmark, & Larsson, 2008). The number of fixations and fixation durations were manually coded according to these areas by two raters. Inter-rater reliability was 94.5% (Fig. 2). 2.3.2. Statistical analyses Data were analysed using a Bayesian repeated measures models (similar to repeated measures ANOVA models) in R version 3.0.0 (R Development Core Team, 2012) using the ‘‘rjags’’ package to link with the Gibbs sampler ‘‘JAGS’’ (Plummer, 2003, 2011). The number of fixations on the four areas of interest was analysed by incorporating a logistic link function for the binomial distribution into the repeated measures model. Each of the four areas of interest was analysed separately by classifying each fixation on a specified area as a ‘‘Yes’’, and fixations on other areas as a ‘‘No’’. The posterior of the binomial repeated measures model yielded the distribution of credible odds ratios (to extract the mean + 95% highest density interval) for fixations on the areas of interest. For the fixation durations, a t-distribution was used to model the error distribution, with the degrees of freedom parameter estimated from the data. The t-distribution was used because it provides more robust estimates compared to those obtained from normal distributions (Kruschke, 2013). The analysis scripts were adapted from the split-plot scripts by (Kruschke, 2011). Hierarchical priors were used for each of the parameters of interest (the main effect of participant group, i.e., with or without ASD, the main effect of item, the interaction between item and group, and the participant level ‘random’ effects) and were described by a series of normal distributions centred on 0 with a standard deviation (SD) described by a half-Cauchy distribution (Gelman & Hill, 2007). The scale parameter for all half-Cauchy SD priors was estimated from the data with the prior on the scale parameter described as a uniform distribution between 0 and 1000. The prior on the normality parameter (degrees of freedom parameter for the tdistribution) for the durations model was described by an exponential distribution centred on 30.
Fig. 2. A screenshot of the image captured by the scene-camera showing the first practice item being fixated upon with a green dot representing the participant’s point of fixation on the correct response at that instance in time.
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In total, 10,000 adaptation steps were used to tune the samplers, 50,000 burn-in steps were discarded before taking 200,000 samples from the posterior spread across three separate chains. All chains showed good convergence of the final parameters, and there was sufficiently few autocorrelations in the chains yielding a high number of effective samples. The 95% highest density intervals (HDI) of the posterior distributions were used to describe the credible interval for each of the parameter estimates and contrasts. 2.3.3. Ethical considerations This study was approved by the Human Research Ethical Committee of Curtin University (approval numbers OTSW-032011 and OTSW-10-2011) in Western Australia. Each participant and their parent/guardian were provided with information sheets detailing the objectives, procedures and ethical considerations of the study. Participation was completely voluntary, and participants were free to withdraw at any time without explanation. Informed assent from the participants and written informed consent from their parents/guardians were obtained prior to commencing the trial. Participants were given two cinema tickets as tokens of appreciation for participating in the study. 3. Results 3.1. Fixation frequency and duration During the initial classification of fixations, all fixations were defined as being located on either the target figure, the correct figure, one of three incorrect figures, or none of the above (‘other’). For the purposes of subsequent analyses these areas of interest were simplified to focus on visual scanning behaviour as it related to the ‘target figure’, the ‘correct figure’ or the figure chosen by the participant as correct (‘chosen figure’). In total, 12,275 fixations were generated by control participants and 10,035 by participants with ASD as shown in Table 1. Fig. 3 presents the odds ratios obtained from the Bayesian repeated measures binomial model for children with and without ASD across each embedded figures item (left) along with the contrasts between children with and without ASD for each individual item and the combined contrast across all items (right). Overall, children with ASD were less likely to fixate on the target object, with items 5, 7, 9, 10, 11, 12 and 14 showing the strongest reductions in fixation counts. However, none of these items could be considered to be substantially different from the remaining items that had 95% credible intervals that overlapped with 0. By contrast, there were no systematic differences between children with and without ASD for either the number of fixations on the correct figure or their chosen figure. There were, however, individual contrasts between children with and without ASD that indicated a credible difference between the groups on specific items. For fixations on chosen figures, children with ASD made more and less fixations proportionally on items 6 and 10, respectively. For fixations on the correct figures, children with ASD made fewer fixations proportionally on the correct figure for items 7 and 12, whereas they were more likely to fixate on the correct object for items 9 and 10. Fig. 4 presents the mean fixation durations (+95% HDIs) obtained from the Bayesian repeated measures model. In addition to being less likely to fixate on the target figure, children with ASD also spent less time per fixation on the target figure across items. There were no other credible differences between children with and without ASD regarding fixation durations. 3.2. EFT performance Table 2 shows the total number of participants who chose the correct and incorrect choices for each embedded figures item. Fig. 5 presents the odds of correct choice for each embedded figures item. There were no systematic differences between children with and without ASD on embedded figures performance. 3.3. Performance by participant Given the wide range of variability in ASD, the proportion of correct responses for each individual as a function of the major eye tracking measures were also examined and are provided in a supplementary figure. There does not appear to be a cluster of individuals with ASD or a cluster of controls that appear markedly different to the respective main groups. Table 1 Number of fixations in areas of interest (AOIs). Percentages below do not sum to 100% as the fixations on ‘incorrect’ figures/AOIs were not included in subsequent analyses. Controls n Total fixations Target Chosen figure Correct figure
12,275 2024 3706 3041
ASD %
n
%
16.5 30.2 24.8
10,035 1386 3037 2522
13.8 30.3 25.1
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Fig. 3. Fixation odds ratios (Means + 95% HDIs). Open circles = controls. Filled circles = ASDs. The left side illustrates the likelihood of fixating on a particular object for controls (open circles) and ASDs (closed circles). The right side presents the contrast in odds ratios between children with ASD and without. Negative contrast deflections indicate less proportional fixations in children with ASD compared to those without.
Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.rasd.2014.01.006. 4. Discussion The current study showed a reduction in fixations on the target figure of each test item observed in children with ASD. Similarly, children with ASD were found to spend less time per fixation on the target figure compared to control children. Although children with ASD spent less time, and looked less frequently, at the target figure, this did not translate to differences in performance between the two groups. There were no other substantial differences between groups in visual search behaviour. These results have some inconsistencies with those of Keehn et al. (2009). Firstly, they did not find a reduction of target fixations in children with ASD. However, the analysis conducted by Keehn et al. (2009) was based on the mean number of fixations per figure while our analysis was a proportional analysis, i.e., the number of fixations on the target proportional to the total number of all fixations for that item. When we analysed our data in a similar way to Keehn et al. (2009) we also found no difference in mean number of target fixations across conditions (children with ASD mean = 4.0, SD = 2.0; Controls mean = 4.3, SD = 1.8). Secondly, Keehn et al. (2009) found that children with ASD made significantly shorter fixations to the figure but not to the target. However, the fixation durations to the target stimuli in children with ASD in their study were still shorter than the fixation durations of the controls, so this may simply be a lack of power in the Keehn et al. study. Furthermore, despite the credible interval overlapping 0 for the overall contrast between individuals with ASD and controls
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Fig. 4. Fixation durations (Means + 95% HDIs). Open circles = controls. Filled circles = ASDs. Contrast is ASD–controls. The right side presents the contrast in fixation durations between children with and without ASD. Negative contrast deflections indicate less time spent per fixation by children with ASD compared to those without. Table 2 Number of correct and incorrect responses per item for children with and without ASD. ASD (n = 23)*
Controls (n = 32) Item
n correct
n incorrect
% correct
n correct
n incorrect
% correct
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
31 30 31 28 24 22 20 22 18 15 19 24 17 10 17 14
0 1 0 3 7 9 11 9 13 16 12 7 14 21 14 17
100 97 100 90 77 71 65 71 58 48 61 77 55 32 55 45
22 23 19 21 17 19 13 16 17 12 11 9 12 7 10 7
1 0 4 2 6 4 10 7 5 10 11 13 10 15 11 14
96 100 83 91 74 83 56 70 77 55 50 41 55 32 48 33
* For items 8–13 inclusive – n (ASD) = 22; and for items 14, 15 n (ASD) = 21 due to incomplete datasets.
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Fig. 5. Behavioural results for ASDs (filled circles) and Controls (open circles). The graph on the left depicts the odds of correct choice for each embedded figures item. The graph on the right depicts contrast in odds ratios between children with ASD and controls; contrasts estimates above the dashed line indicate that children with ASD were more likely to get that item correct compared to controls.
on the chosen figure fixation duration (which could be considered the most similar to Keehn et al.’s ‘‘Figure’’), the majority of the credible parameter contrast estimates for the chosen figure indicated faster fixation durations in children with ASD. The current study found a select number of differences in visual search behaviour between children with and without ASD. Evidence of only specific differences in visual scanning patterns in the presence of equivocal performance suggest that if processing differences do exist in children with ASD, these differences need not be deleterious to some aspects of cognition. Indeed, evidence of children with ASD requiring fewer fixations (and fewer overall fixations; see Table 1) and less time per fixation on stimuli but exhibiting comparable accuracy to controls suggests a processing advantage, if not an accuracy advantage in this instance. Embedded figures performance is usually assessed using the EFT, not the Figure-Ground subtest used here. Thus, the present study included necessary methodological differences that make direct comparison between findings problematic. For instance, the stimuli used by Keehn et al. (2009) consisted of eight items of the original EFT with only the target figure and the response array. In this instance participants simply indicated the presence or absence of the target within this array, which gives a 50% chance of guessing the correct answer. By contrast, the Figure-Ground subtest used in the current study possessed four response arrays in addition to the target figure. As a result, behavioural responses involve greater cognitive elaboration as the participant must look at multiple possible response options and compare them to confirm or deny the presence of the target. Nevertheless, the Figure-Ground subtest of the TVPS-3, like the original EFT, relies on ‘orientation ability’ and not ‘visualisation ability’. Shah and Frith (1983) describe the embedded feature to be identified in EFT items as being unaltered relative to the target and not subject to manipulation, rotation, twisting or inverting within the complex array. In that sense the Figure-Ground subtest used in this study is a robust measure of figure disembedding. Neuroimaging studies provide insight into the neurobiological underpinnings of these differences in both EFT efficiency and strategy, even in the presence of comparable behavioural performance between individuals with and without ASD. Individuals with ASD with comparable accuracy to neurotypical controls have been shown to exhibit reduced dorsolateral prefrontal and inferior parietal activation, as well increased activity in other visuospatial regions (Damarla et al., 2010). Similarly, individuals with ASD also exhibit reduced functional connectivity between higher-order working memory/ executive function centres and visuospatial areas. These observed differences in activation not only provide insight into the processing of EFT stimuli, but also the strategies used to complete the task itself. Even in the presence of some similar patterns of activation between controls and individuals with ASD, those differences in activity may indicate that individuals with ASD approach the EFT in such a way that it reduces the load on working memory (possibly leading to a need for less fixations and shorter durations) but increases activity in regions associated with object perception (Ring et al., 1999). 5. Conclusion Differences in visual scanning patterns in the presence of typical behavioural performance suggest that any purported differences in processing style may not be detrimental to cognitive performance and further refinement of the current methodology may lead to support for a purported advantageous cognitive style. References Albrecht, M. A., Stuart, G. W., Falkmer, M., Ordqvist, A., Leung, D., Foster, J. K., et al. (2014). Visual acuity in autism spectrum disorders. Journal of Autism and Developmental Disorders. Bertone, A., Mottron, L., Jelenic, P., & Faubert, J. (2005). Enhanced and diminished visuo-spatial information processing in autism depends on stimulus complexity. Brain, 128(10), 2430–2441. Damarla, S. R., Keller, T. A., Kana, R. K., Cherkassky, V. L., Williams, D. L., Minshew, N. J., et al. (2010). Cortical underconnectivity coupled with preserved visuospatial cognition in autism: Evidence from an fMRI study of an embedded figures task. Autism Research, 3(5), 273–279. Falkmer, M., Bja¨llmark, A., Larsson, M., & Falkmer, T. (2011). The influences of static and interactive dynamic facial stimuli on visual strategies in persons with Asperger syndrome. Research in Autism Spectrum Disorders, 5(2), 935–940.
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Visual search strategies of children with and without autism spectrum disorders during an embedded figures task Chiara Horlin a, Matthew A. Albrecht b, Marita Falkmer a,c, Denise Leung a, Anna Ordqvist d, Tele Tan e, Wee Lih Lee f, Torbjorn Falkmer a,d,g,* a
School of Occupational Therapy & Social Work, CHIRI, Curtin University, GPO Box U1987, Perth, WA 6845, Australia School of Psychology & Speech Pathology, CHIRI, Curtin University, GPO Box U1987, Perth, WA 6845, Australia School of Education and Communication, CHILD Programme, Institute of Disability Research, Jo¨nko¨ping University, Sweden d Rehabilitation Medicine, Department of Medicine and Health Sciences (IMH), Faculty of Health Sciences, Linko¨ping University & Pain and Rehabilitation Centre, SE-581 85 Linko¨ping, Sweden e Department of Mechanical Engineering, Curtin University, GPO Box U1987, Perth, WA 6845, Australia f Department of Electrical and Computer Engineering, Curtin University, GPO Box U1987, Perth, WA 6845, Australia g School of Occupational Therapy, La Trobe University, Melbourne, VIC 3086, Australia b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 11 October 2013 Received in revised form 14 January 2014 Accepted 21 January 2014
Individuals with ASD often demonstrate superior performance on embedded figures tasks (EFTs). We investigated visual scanning behaviour in children with ASD during an EFT in an attempt replicating a previous study examining differences in visual search behaviour. Twenty-three children with, and 31 children without an ASD were shown 16 items from the Figure-Ground subtest of the TVPS-3 while wearing an eye tracker. Children with ASD exhibited fewer fixations, and less time per fixation, on the target figure. Accuracy was similar between the two groups. There were no other noteworthy differences between children with and without ASD. Differences in visual scanning patterns in the presence of typical behavioural performance suggest that any purported differences in processing style may not be detrimental to cognitive performance and further refinement of the current methodology may lead to support for a purported advantageous cognitive style. ß 2014 Elsevier Ltd. All rights reserved.
Keywords: ASD Eye tracking Embedded figures test Visual search
1. Introduction Although not currently included in the formal behavioural criteria for a diagnosis of autism spectrum disorders (ASD), there is increasing support for sensory atypicalities as a primary or secondary contributing factor to ASD symptomatology. Reports of atypical sensory experiences include the enhanced processing of low-level sensory information or local stimulus features of viewed objects (Iarocci & McDonald, 2006). This processing enhancement, or preference/bias, for low level features was earlier suggested to come at the expense of integrating individual features, e.g., the Weak Central Coherence model (WCC; Happe´, 1996). However, the evidence for deficits in global processing is not as robust as the evidence for enhanced, or at least spared, low level processing (Happe´ & Frith, 2006).
* Corresponding author at: School of Occupational Therapy and Social Work, Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University of Technology, GPO Box U1987, Perth, WA 6845, Australia. Tel.: +61 8 9266 9051; fax: +61 8 9266 3636. E-mail address: [email protected] (T. Falkmer). 1750-9467/$ – see front matter ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.rasd.2014.01.006
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Nevertheless, enhanced/preferential processing of local features has often been suggested to play a significant role in the enhanced performance, or preferential bias towards, select domains in people with ASD. For example, children with autism display a preference for visuo-spatial, rather than temporal, processing and this preference may impart superior performance on tasks that necessitate spatial cues or codes (O’Connor & Hermelin, 1978). This is consistent with clinical and experimental observations whereby individuals with ASD notice and react to minute details in the environment that are not noticed by others and react with surprising speed (Shah & Frith, 1983). In more controlled settings/tasks, enhanced (or at least comparable) performance has been observed in a range of visuospatial tasks including Block Design (Shah & Frith, 1993), copying of impossible figures (Mottron, Belleville, & Menard, 1999) and tasks involving visual search and target detection (Joseph, Keehn, Connolly, Wolfe, & Horowitz, 2009; O’riordan, 2004; O’Riordan, Plaisted, Driver, & Baron-Cohen, 2001). Individuals with ASD have also been found to be less susceptible to the Ebbinghaus illusion (Happe´, 1996) and tolerate higher levels of luminance noise while still being able to detect the orientation of a target figure (Bertone, Mottron, Jelenic, & Faubert, 2005). Resistance to these illusions may be especially strong in individuals with high levels of systematising type behaviours, rather than a trait that is related to ASD per se (Walter, Dassonville, & Bochsler, 2009). However, the most oft-cited task to demonstrate enhanced performance in individuals with ASD is the embedded figures task (EFT; Jolliffe & Baron-Cohen, 1997; Ring et al., 1999; Shah & Frith, 1983). EFTs involve the detection of a smaller stimulus embedded within a larger pattern or image, sometimes within an array of distractor images. EFTs are typically held as the prototypical example of preserved cognitive skill in many individuals with autism. Superior, or at least comparable, performance on EFTs has been observed in various stages of development and across the autism spectrum when individuals with ASD are compared to chronological aged, mental-age, verbal ability, and IQ matched controls (Jolliffe & Baron-Cohen, 1997; Ring et al., 1999; Shah & Frith, 1983). The most dramatic differences in performance between individuals with ASD and other clinical groups, or typically developed controls, are usually seen in the response times. Individuals with ASD typically perform the EFT at usually comparable (and sometimes superior) levels of ˜ a, 2011). Similarly, strategies used to perform the EFT are also accuracy and often with faster response times (White & Saldan known to differ between individuals with ASD and other groups (Shah & Frith, 1983). However, little is known about whether visual scan strategies are ASD specific or not. Analyses of visual scanning patterns via eye-tracking data can give insight into both the top-down and bottom-up processing that takes place during experimental tasks. To date, only one study has used eye trackers during an EFT in children with ASD (Keehn et al., 2009). It found no overall performance differences between children with (n = 12) and without (n = 11) ASD, and no differences in the number of fixations made per trial (Keehn et al., 2009). However, children with ASD made fixations of shorter duration. Further analyses of the location of fixations found that the shorter fixations were located on the figure containing the embedded target. Thus, children with ASD exhibited shorter fixations when attempting to locate the target within a complex array. The authors also found that children with ASD were faster at initially encoding stimulus features, as indicated by shorter fixation durations during the first fixation on each item. In an attempt to replicate these findings, the current study examined the visual scanning behaviour of children with and without ASD while disembedding figures. To further elucidate the visual scanning patterns of children with and without ASD, a larger sample size than that of the previous study was recruited. The Figure-Ground subtest from the Tests of Visual Perception Skills – 3rd edition (TVPS-3, Martin, 2006) was used as a more complex measurement of embedded figure performance, with multiple response options per item and an increasing degree of difficulty throughout the task. We were particularly interested in differences in accuracy and search strategy between the two groups, thus, presentation of the stimuli was not constrained in order to avoid any impedance on performance due to time pressure. 2. Methods 2.1. Participants Recruitment of children with and without ASD was conducted through the Telethon Institute of Child Health Research, personal contacts, a number of local primary schools, as well as radio and newspaper advertisements throughout the Perth metropolitan area in Western Australia. Inclusion criteria specified the absence of comorbid cognitive conditions in children with ASD, and all participants were required to read and understand written and verbal instructions in English. Medical records were sighted to confirm ASD diagnosis. In total 23 (4 female) children with ASD participated in the study, with 21 (4 female) contributing full datasets, and 32 (7 female) control children. One female control was excluded to improve the sex and age balance across groups. The removal of this participant did not influence the results reported. All children were aged 8–13 years (ASD’s mean age = 10.7 years, SD = 1.2; controls’ mean age = 10.6 years, SD = 1.2). There was a significant difference in age between boys (mean age = 10.8 years, SD = 1.2, median = 11.0 years) and girls (mean age = 10.0 years, SD = 1.0, median = 9.7 years) (t(15.3) = 2.2, p = 0.045), however no interaction between sex and diagnostic group for age (F(1,50) = 2.4, p = 0.13), thus there are no significant age differences across groups within genders. 2.2. Materials/apparatus The number of fixations and fixation durations were recorded with a head-mounted Arrington ViewPointTM eye tracker operating at 60 Hz. The eye tracker was worn over participants’ glasses if necessary. After a screening procedure to verify that
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each participant’s eyes moved smoothly and congruently, the eye tracker was set to record movements of one eye only, usually the right. In order to reduce systematic error and increase the accuracy of the results, the participant’s head was stabilised using an Arrington Ultra Precision Head PositionerTM. Participants were seated one metre from a 4200 flat screen television, which displayed the stimuli. A 16-point calibration of the eye tracker was conducted for each participant prior to commencing the primary experimental task. To ensure that calibration was maintained throughout the trial, participants were instructed to fix their gaze to a central fixation point (represented by a black dot on a white background) between stimuli items. If calibration was not maintained, a re-calibration took place. 2.2.1. The stimuli The participants were shown two practice items and 16 individual test items, each taken from the Tests of Visual Perception Skills Figure-Ground subtest (TVPS-3, Martin, 2006) in the original order of the test. Each slide consisted of a target figure displayed in the centre of the top half, as shown in Fig. 1. The bottom half of each slide displayed four alternative response choices labelled between 1 and 4. The target figure could be found embedded in only one of these response options. A distinguishing feature of this subtest is an increasing degree of difficulty across items, as demonstrated in the difference in complexity of the response options between items 1 (top) and 16 (bottom) shown in Fig. 1.
Fig. 1. Test items 1(above) and 16 (below) from the Figure-Ground subtest of TVPS-3 (Martin, 2006).
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2.3. Procedure Before the onset of the task, participants were reminded of the task instructions by the primary experimenter. Instructions were delivered verbally and pictorially and participants were reminded they could withdraw at any point without explanation. After a screening procedure to assess visual acuity (Albrecht et al., 2014; Falkmer, Bja¨llmark, Larsson, & Falkmer, 2011; Falkmer, Stuart, et al., 2011) and verify that each participant’s eyes moved smoothly and congruently, participants were seated comfortably in the chin-rest and the eye-tracker was calibrated. Following successful calibration, and prior to administration of the 16 test items, two items were administered as practice trials to ensure full understanding of the task requirements. Immediately before the presentation of each item, a fixation point was presented in the middle of the screen to cue the participant to the upcoming item. This fixation point also confirmed the calibration of the eye-tracker, in accordance with previous studies (Falkmer, Bja¨llmark, et al., 2011; Leung, Ordqvist, Falkmer, Parsons, & Falkmer, 2013). Eye-tracking data were continuously recorded throughout the task; however, the onset and close of recordings for each item were locked to the time point at which the stimuli were first presented until a response was made. Participants responded by pointing at, or by verbally indicating which of the four response options (labelled 1, 2, 3 and 4 in Fig. 1) they thought was correct. As soon as a response was made, the participant progressed to the next item. 2.3.1. Classification of fixation areas As shown in Fig. 1, each item was classified into five different areas of interest, i.e., target figure, correct response and incorrect left, middle or right alternatives. Fixations on none of the aforementioned areas of interest were classified as ‘‘other’’. The location of the participants’ fixations was determined by a fixation generation programme that uses a centroid mode algorithm used in a similar study with adults with ASD (Falkmer, Dahlman, Dukic, Bja¨llmark, & Larsson, 2008). The number of fixations and fixation durations were manually coded according to these areas by two raters. Inter-rater reliability was 94.5% (Fig. 2). 2.3.2. Statistical analyses Data were analysed using a Bayesian repeated measures models (similar to repeated measures ANOVA models) in R version 3.0.0 (R Development Core Team, 2012) using the ‘‘rjags’’ package to link with the Gibbs sampler ‘‘JAGS’’ (Plummer, 2003, 2011). The number of fixations on the four areas of interest was analysed by incorporating a logistic link function for the binomial distribution into the repeated measures model. Each of the four areas of interest was analysed separately by classifying each fixation on a specified area as a ‘‘Yes’’, and fixations on other areas as a ‘‘No’’. The posterior of the binomial repeated measures model yielded the distribution of credible odds ratios (to extract the mean + 95% highest density interval) for fixations on the areas of interest. For the fixation durations, a t-distribution was used to model the error distribution, with the degrees of freedom parameter estimated from the data. The t-distribution was used because it provides more robust estimates compared to those obtained from normal distributions (Kruschke, 2013). The analysis scripts were adapted from the split-plot scripts by (Kruschke, 2011). Hierarchical priors were used for each of the parameters of interest (the main effect of participant group, i.e., with or without ASD, the main effect of item, the interaction between item and group, and the participant level ‘random’ effects) and were described by a series of normal distributions centred on 0 with a standard deviation (SD) described by a half-Cauchy distribution (Gelman & Hill, 2007). The scale parameter for all half-Cauchy SD priors was estimated from the data with the prior on the scale parameter described as a uniform distribution between 0 and 1000. The prior on the normality parameter (degrees of freedom parameter for the tdistribution) for the durations model was described by an exponential distribution centred on 30.
Fig. 2. A screenshot of the image captured by the scene-camera showing the first practice item being fixated upon with a green dot representing the participant’s point of fixation on the correct response at that instance in time.
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In total, 10,000 adaptation steps were used to tune the samplers, 50,000 burn-in steps were discarded before taking 200,000 samples from the posterior spread across three separate chains. All chains showed good convergence of the final parameters, and there was sufficiently few autocorrelations in the chains yielding a high number of effective samples. The 95% highest density intervals (HDI) of the posterior distributions were used to describe the credible interval for each of the parameter estimates and contrasts. 2.3.3. Ethical considerations This study was approved by the Human Research Ethical Committee of Curtin University (approval numbers OTSW-032011 and OTSW-10-2011) in Western Australia. Each participant and their parent/guardian were provided with information sheets detailing the objectives, procedures and ethical considerations of the study. Participation was completely voluntary, and participants were free to withdraw at any time without explanation. Informed assent from the participants and written informed consent from their parents/guardians were obtained prior to commencing the trial. Participants were given two cinema tickets as tokens of appreciation for participating in the study. 3. Results 3.1. Fixation frequency and duration During the initial classification of fixations, all fixations were defined as being located on either the target figure, the correct figure, one of three incorrect figures, or none of the above (‘other’). For the purposes of subsequent analyses these areas of interest were simplified to focus on visual scanning behaviour as it related to the ‘target figure’, the ‘correct figure’ or the figure chosen by the participant as correct (‘chosen figure’). In total, 12,275 fixations were generated by control participants and 10,035 by participants with ASD as shown in Table 1. Fig. 3 presents the odds ratios obtained from the Bayesian repeated measures binomial model for children with and without ASD across each embedded figures item (left) along with the contrasts between children with and without ASD for each individual item and the combined contrast across all items (right). Overall, children with ASD were less likely to fixate on the target object, with items 5, 7, 9, 10, 11, 12 and 14 showing the strongest reductions in fixation counts. However, none of these items could be considered to be substantially different from the remaining items that had 95% credible intervals that overlapped with 0. By contrast, there were no systematic differences between children with and without ASD for either the number of fixations on the correct figure or their chosen figure. There were, however, individual contrasts between children with and without ASD that indicated a credible difference between the groups on specific items. For fixations on chosen figures, children with ASD made more and less fixations proportionally on items 6 and 10, respectively. For fixations on the correct figures, children with ASD made fewer fixations proportionally on the correct figure for items 7 and 12, whereas they were more likely to fixate on the correct object for items 9 and 10. Fig. 4 presents the mean fixation durations (+95% HDIs) obtained from the Bayesian repeated measures model. In addition to being less likely to fixate on the target figure, children with ASD also spent less time per fixation on the target figure across items. There were no other credible differences between children with and without ASD regarding fixation durations. 3.2. EFT performance Table 2 shows the total number of participants who chose the correct and incorrect choices for each embedded figures item. Fig. 5 presents the odds of correct choice for each embedded figures item. There were no systematic differences between children with and without ASD on embedded figures performance. 3.3. Performance by participant Given the wide range of variability in ASD, the proportion of correct responses for each individual as a function of the major eye tracking measures were also examined and are provided in a supplementary figure. There does not appear to be a cluster of individuals with ASD or a cluster of controls that appear markedly different to the respective main groups. Table 1 Number of fixations in areas of interest (AOIs). Percentages below do not sum to 100% as the fixations on ‘incorrect’ figures/AOIs were not included in subsequent analyses. Controls n Total fixations Target Chosen figure Correct figure
12,275 2024 3706 3041
ASD %
n
%
16.5 30.2 24.8
10,035 1386 3037 2522
13.8 30.3 25.1
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Fig. 3. Fixation odds ratios (Means + 95% HDIs). Open circles = controls. Filled circles = ASDs. The left side illustrates the likelihood of fixating on a particular object for controls (open circles) and ASDs (closed circles). The right side presents the contrast in odds ratios between children with ASD and without. Negative contrast deflections indicate less proportional fixations in children with ASD compared to those without.
Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.rasd.2014.01.006. 4. Discussion The current study showed a reduction in fixations on the target figure of each test item observed in children with ASD. Similarly, children with ASD were found to spend less time per fixation on the target figure compared to control children. Although children with ASD spent less time, and looked less frequently, at the target figure, this did not translate to differences in performance between the two groups. There were no other substantial differences between groups in visual search behaviour. These results have some inconsistencies with those of Keehn et al. (2009). Firstly, they did not find a reduction of target fixations in children with ASD. However, the analysis conducted by Keehn et al. (2009) was based on the mean number of fixations per figure while our analysis was a proportional analysis, i.e., the number of fixations on the target proportional to the total number of all fixations for that item. When we analysed our data in a similar way to Keehn et al. (2009) we also found no difference in mean number of target fixations across conditions (children with ASD mean = 4.0, SD = 2.0; Controls mean = 4.3, SD = 1.8). Secondly, Keehn et al. (2009) found that children with ASD made significantly shorter fixations to the figure but not to the target. However, the fixation durations to the target stimuli in children with ASD in their study were still shorter than the fixation durations of the controls, so this may simply be a lack of power in the Keehn et al. study. Furthermore, despite the credible interval overlapping 0 for the overall contrast between individuals with ASD and controls
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Fig. 4. Fixation durations (Means + 95% HDIs). Open circles = controls. Filled circles = ASDs. Contrast is ASD–controls. The right side presents the contrast in fixation durations between children with and without ASD. Negative contrast deflections indicate less time spent per fixation by children with ASD compared to those without. Table 2 Number of correct and incorrect responses per item for children with and without ASD. ASD (n = 23)*
Controls (n = 32) Item
n correct
n incorrect
% correct
n correct
n incorrect
% correct
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
31 30 31 28 24 22 20 22 18 15 19 24 17 10 17 14
0 1 0 3 7 9 11 9 13 16 12 7 14 21 14 17
100 97 100 90 77 71 65 71 58 48 61 77 55 32 55 45
22 23 19 21 17 19 13 16 17 12 11 9 12 7 10 7
1 0 4 2 6 4 10 7 5 10 11 13 10 15 11 14
96 100 83 91 74 83 56 70 77 55 50 41 55 32 48 33
* For items 8–13 inclusive – n (ASD) = 22; and for items 14, 15 n (ASD) = 21 due to incomplete datasets.
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Fig. 5. Behavioural results for ASDs (filled circles) and Controls (open circles). The graph on the left depicts the odds of correct choice for each embedded figures item. The graph on the right depicts contrast in odds ratios between children with ASD and controls; contrasts estimates above the dashed line indicate that children with ASD were more likely to get that item correct compared to controls.
on the chosen figure fixation duration (which could be considered the most similar to Keehn et al.’s ‘‘Figure’’), the majority of the credible parameter contrast estimates for the chosen figure indicated faster fixation durations in children with ASD. The current study found a select number of differences in visual search behaviour between children with and without ASD. Evidence of only specific differences in visual scanning patterns in the presence of equivocal performance suggest that if processing differences do exist in children with ASD, these differences need not be deleterious to some aspects of cognition. Indeed, evidence of children with ASD requiring fewer fixations (and fewer overall fixations; see Table 1) and less time per fixation on stimuli but exhibiting comparable accuracy to controls suggests a processing advantage, if not an accuracy advantage in this instance. Embedded figures performance is usually assessed using the EFT, not the Figure-Ground subtest used here. Thus, the present study included necessary methodological differences that make direct comparison between findings problematic. For instance, the stimuli used by Keehn et al. (2009) consisted of eight items of the original EFT with only the target figure and the response array. In this instance participants simply indicated the presence or absence of the target within this array, which gives a 50% chance of guessing the correct answer. By contrast, the Figure-Ground subtest used in the current study possessed four response arrays in addition to the target figure. As a result, behavioural responses involve greater cognitive elaboration as the participant must look at multiple possible response options and compare them to confirm or deny the presence of the target. Nevertheless, the Figure-Ground subtest of the TVPS-3, like the original EFT, relies on ‘orientation ability’ and not ‘visualisation ability’. Shah and Frith (1983) describe the embedded feature to be identified in EFT items as being unaltered relative to the target and not subject to manipulation, rotation, twisting or inverting within the complex array. In that sense the Figure-Ground subtest used in this study is a robust measure of figure disembedding. Neuroimaging studies provide insight into the neurobiological underpinnings of these differences in both EFT efficiency and strategy, even in the presence of comparable behavioural performance between individuals with and without ASD. Individuals with ASD with comparable accuracy to neurotypical controls have been shown to exhibit reduced dorsolateral prefrontal and inferior parietal activation, as well increased activity in other visuospatial regions (Damarla et al., 2010). Similarly, individuals with ASD also exhibit reduced functional connectivity between higher-order working memory/ executive function centres and visuospatial areas. These observed differences in activation not only provide insight into the processing of EFT stimuli, but also the strategies used to complete the task itself. Even in the presence of some similar patterns of activation between controls and individuals with ASD, those differences in activity may indicate that individuals with ASD approach the EFT in such a way that it reduces the load on working memory (possibly leading to a need for less fixations and shorter durations) but increases activity in regions associated with object perception (Ring et al., 1999). 5. Conclusion Differences in visual scanning patterns in the presence of typical behavioural performance suggest that any purported differences in processing style may not be detrimental to cognitive performance and further refinement of the current methodology may lead to support for a purported advantageous cognitive style. References Albrecht, M. A., Stuart, G. W., Falkmer, M., Ordqvist, A., Leung, D., Foster, J. K., et al. (2014). Visual acuity in autism spectrum disorders. Journal of Autism and Developmental Disorders. Bertone, A., Mottron, L., Jelenic, P., & Faubert, J. (2005). Enhanced and diminished visuo-spatial information processing in autism depends on stimulus complexity. Brain, 128(10), 2430–2441. Damarla, S. R., Keller, T. A., Kana, R. K., Cherkassky, V. L., Williams, D. L., Minshew, N. J., et al. (2010). Cortical underconnectivity coupled with preserved visuospatial cognition in autism: Evidence from an fMRI study of an embedded figures task. Autism Research, 3(5), 273–279. Falkmer, M., Bja¨llmark, A., Larsson, M., & Falkmer, T. (2011). The influences of static and interactive dynamic facial stimuli on visual strategies in persons with Asperger syndrome. Research in Autism Spectrum Disorders, 5(2), 935–940.
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