Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2019 Oct 1.
Published in final edited form as: J Autism Dev Disord. 2018 Oct;48(10):3449–3459. doi: 10.1007/s10803-018-3612-z

Circumscribed interests and attention in autism: The role of biological sex

Clare Harrop 1, Desiree Jones 1, Shuting Zheng 2, Sallie Nowell 1, Brian A Boyd 3, Noah Sasson 4
PMCID: PMC6267775  NIHMSID: NIHMS969291  PMID: 29777472

Abstract

Recent studies suggest that circumscribed interests (CI) in females with Autism Spectrum Disorder (ASD) may align more closely with interests reported in typical female development than those typically reported for ASD males. We used eye-tracking to quantify attention to arrays containing combinations of male, female and neutral images in elementary-aged males and females with and without ASD. A number of condition*sex effects emerged, with both groups attending to images that corresponded with interests typically associated with their biological sex. Diagnostic effects reported in similar studies were not replicated in our modified design. Our findings of more typical attention patterns to gender-typical images in ASD females is consistent with evidence of sex differences in CI and inconsistent with the “Extreme Male Brain” theory of ASD.

Keywords: Eye-tracking, Circumscribed Interests, Sex Differences, Females, Extreme Male Brain Theory

Introduction

Sex differences in the prevalence of Autism Spectrum Disorder (ASD) have remained consistent despite changes in diagnostic criteria and early screening. Based on national prevalence estimates, four boys to one girl receive a diagnosis of ASD (Christensen et al., 2016; Loomes et al., 2017). As a result, much less is known about the development and clinical profile of females on the autism spectrum. Research has indicated differences between males and females with ASD in a number of areas, including social motivation (Dean et al., 2017; Hiller et al., 2014; Sedgewick et al., 2016) and fewer and/or different restricted and repetitive behaviors (RRBs; Frazier et al., 2014; Van Wijngaarden-Cremers et al., 2014). This study focuses on a subset of RRBs – circumscribed interests (CI)—and adapts a validated eye-tracking paradigm to understand whether patterns of attention to images differ between males and females with ASD.

Circumscribed Interests in ASD

Differences in CI have emerged as a potential area of distinction between males and females with ASD. CI are defined as an intense and focused interest in a narrow range of subjects. The content and focus of CI often overlap with interests observed in typical development, particularly in males (DeLoache et al., 2007). Examples include Legos®, trains, cars and computers (South et al., 2005). However, CI in ASD are often idiosyncratic, such as interests in watches and clocks, road signs, historical facts and timetables (Mercier et al., 2000; South et al., 2005), and can be less functional and less age-appropriate (Turner-Brown et al., 2011).

Reports suggest that over 75% of individuals with ASD have at least one CI, with a high proportion of individuals having multiple CI (Klin et al., 2007; Turner-Brown et al., 2011). Individuals with ASD frequently organize their activities around their CI (Klin et al., 2007; South et al., 2005) and these interests can have a negative effect on social activities, learning and adaptive behaviors (Koegel& Covert, 1972; Koegel, et al., 1974; Pierce & Courchesne, 2001). Children with ASD often engage with their CI in inflexible ways, resist when interrupted and require accommodation around their interests (Turner-Brown et al., 2011), supporting the clinical importance of these behaviors. Further, CI seem to be specific to ASD compared to other RRBs that are frequently observed in other neurodevelopmental disorders. As such, researchers have sought to understand the mechanisms that underlie CI and their potential impact on learning and social opportunities.

Importantly, however, CI in ASD can also confer significant benefit for individuals with ASD. Not only can they be a great source of pleasure (Sasson et al., 2012), but they also can serve as an area of strength and expertise (Mercier et al., 2000) that is observable at the neurobiological level (Grelotti et al., 2005; Foss-Feig et al., 2016) and in some cases lead to specialized skills and abilities (Koenig & Hough, 2017). CI also can provide opportunities for individuals with ASD to socially engage with others (Mercier et al., 2000), a phenomenon which may be particularly relevant to females with ASD, as they often report their CI as a defining feature of their identity (Bargiela, Steward & Mandy, 2016). CI in such cases may serve as a protective effect (Bargiela, Steward & Mandy, 2016).

Eye-Tracking Studies of Circumscribed Interests

Eye-tracking studies have been used to understand how CI relate to attention and motivation in ASD. In two studies using a paired-preference task, young children with ASD fixated on images of common CI at the expense of attending to faces to a greater degree than did TD children (Sasson & Touchstone, 2014; Unruh et al., 2016). Using arrays of images varying in content, Sasson and colleagues found that when presented with images of common CI that are of high salience to individuals with ASD (termed high autism interest objects, e.g. Lego®, trains, cars), attention to social stimuli and other images (termed low autism interest objects) is reduced (Sasson et al., 2008; Sasson et al., 2011). When presented with common CI, individuals with ASD had a tendency to explore fewer items within the arrays, perseverate on CI images, and explore them in a more detail-orientated manner. These studies demonstrated that relative to typically developing (TD) controls, attention in individuals with ASD is reduced to social stimuli when paired with items of high salience, like CI objects. However, these studies were underpowered to examine sex differences and recent findings suggest that CI content and intensity may differ between males and females with ASD; therefore, it is unclear whether these patterns of attention extend to females.

Circumscribed Interests in ASD Females

A number of recent studies have indicated lower incidences of CI in females with ASD and differences in their content. For example, in a large study examining behavioral and cognitive characteristics of ASD females (N = 304, mean age = 9 years), Frazier and colleagues (2014) reported lower CI scores on standardized measures of RRBs in females compared to males, and that these differences were not mediated by cognitive abilities. This study raised questions as to whether these types of higher order RRBs (RRBs considered to be more cognitively complex) are as prevalent in females, or perhaps that CI observed in females are not as readily observed using existing measurement tools which have been predominately developed using male samples (Lai et al., 2011).

A handful of studies implementing parent report and direct observation have suggested that the content of these interests may differ with more focus on interests that are commonly observed in typical development. In two studies, Hiller and colleagues reported that parents and teachers described both lower levels of RRBs but also differences in the content of CI (Hiller et al., 2014, 2016). Namely, girls were more likely to have “seemingly random” interests and less likely to be interested in wheeled toys and gaming. Girls were also more likely to share their interests with others, suggestive of lower levels of social impairment stemming from these intense interests.

Using observations of parent-child play, Harrop, Hudry and Green (2017) reported that while preschool-aged ASD females played to a similar level of complexity as ASD males, they played with different toys. Specifically, both groups played with toys commonly associated with sex differences in typical development. For example, females with ASD were more likely to play with such toys as tea sets, dolls and cooking items; whereas ASD males tended to play with toys associated with male typical– building toys, computers and cars. These results suggest that sex differences observed in typical development may extend to ASD.

These findings, together with reports of heightened social motivation in females with ASD (Sedgewick et al., 2016), do not align with the predictions of the Extreme Male Brain (EMB) theory of ASD (Baron-Cohen, 2002). The EMB theory of ASD postulates that autism represents an extension of typical sex differences in the domains of empathizing and systemizing (Baron-Cohen, 2009) and thus would predict more male-typical interests among females with ASD rather than female-typical interests, as has been shown in prior studies (Harrop et al., 2017; Hiller et al., 2014, 2016). Based on this theory, females and males with ASD should demonstrate similar attention patterns to similar images – those reflecting common CI that are male-typical. This, however, has not yet been empirically investigated.

Unfortunately, given findings that CI in ASD appear to fall along traditional gender lines (Hiller et al., 2014, 2016; Sutherland et al., 2017), the applicability of previously validated eye-tracking paradigms may not truly reveal differences between males and females with ASD because of an over-reliance on more male-specific CI (e.g. Lego®, trains, computers) using predominantly male samples. To this end, we adapted the visual search arrays that Sasson and colleagues (Sasson et al., 2008; Sasson et al., 2011) developed to include images of interests that that reflect commonly reported gender differences. The inclusion of more gender typical images may provide a more sensitive measure of attention in ASD as these stimuli may be more likely to capture attention than typical CI images that have not considered the role of gender. The goal of this study was to understand patterns of visual attention to CI-related stimuli in school-aged ASD females. Based on previous literature and clinical descriptions of ASD females, we predicted that overall attention in females with ASD would be comparable to males with ASD (i.e. more circumscribed and perseverative), but to different types of images. Specifically, females with ASD would demonstrate these visual attention patterns to images representative of interests reported in typically developing (TD) females but not to the male-typical CI images used in prior research (Sasson et al., 2008; 2011).

Methods

Participants

Four groups of participants were recruited to participate based on diagnosis and biological sex: (1) 27 ASD males; (2) 27 ASD females; (3) 16 TD males; and (4) 17 TD females. All participants met the following general inclusion criteria: between 6 and 10 years of age; absence of seizure disorder, acute medical or genetic condition; and absence of any visual impairment uncorrectable with eyewear. The age range was 6 to 10 years based on the findings of a recent meta-analysis suggesting that, prior to the age of six, very few differences in core symptoms were observed between males and females, however at age 6, higher rates of RRBs were reported in males (Van Wijngaarden-Cremers et al., 2014). We also included a narrower age range than Sasson and colleagues (2008) to ensure the developmental appropriateness of the images selected for our paradigm.

We did not exclude participation based on IQ or level of functioning. However, we collected data on child cognitive ability using the Differential Ability Scales (DAS-II; Elliot, 2007). We did not exclude any children based on severity or functioning due to the inherent difficulty of recruiting an adequate sample size of ASD females and the fact they often fall at the lower end of the spectrum and require additional behavioral problems and/or co-occurring intellectual disability to warrant a diagnosis of ASD (Dworzynski et al., 2012). However, any cognitive or functional differences were co-varied in all analyses – an approach common in ASD research and eye tracking studies (Chevallier et al., 2015; Fletcher-Watson et al., 2009; Sasson & Touchstone, 2014).

Participants with ASD were recruited via the (insert name of university following review) Autism Research Registry in conjunction with regional diagnostic and treatment centers. Inclusion in the registry required a previous Diagnostic and Statistical Manual of Mental Disorders diagnosis of ASD made by a licensed clinician experienced in the assessment and diagnosis of autism, and based on parent interview and direct observation for the completion of one or more standardized autism diagnostic assessment instruments (Autism Diagnostic Interview-Revised (ADI-R; Rutter et al., 2003b), Social Communication Questionnaire (SCQ; Rutter et al., 2003a), Autism Diagnostic Observation Schedule (ADOS; Lord et al., 2000), Childhood Autism Rating Scale (CARS-2; Schloper et al., 2010)). Study staff verified the diagnosis via a phone screen with families, and parents/caregivers also completed the Social Communication Questionnaire (SCQ; Rutter et al., 2003a) at their first study visit.

TD children were recruited via an email sent to the (insert name of university following review) Child Development Research Registry, advertisements on social media and word of mouth. TD children were excluded if they had a history of neurodevelopmental disorders or had an immediate relative with a diagnosis of ASD. Parents completed the SCQ to rule out elevated behaviors indicative of ASD. We did not purposefully match on developmental age due to the low task demands of the eye-tracking task; however, any differences in mental age (MA) were controlled for in the analysis.

All subjects were reimbursed with a gift card for their study participation. Parents provided informed written consent and, when developmentally appropriate, children provided written assent to participate. The research protocol was approved by the Institutional Review Board at (insert name of university following review).

All children completed the DAS-II (Elliot, 2007). The DAS-II is an established measure of cognitive abilities from 30 months to 17 years, 11 months. It has been implemented in several studies of children with ASD (e.g. Bishop et al., 20111; Joseph et al., 2002). In this study, we administered six scales that comprise the Core Battery to derive nonverbal, verbal, and spatial ability scores and age equivalents. These core subscales are comparable across the Early and School Years protocols, and both protocols allow for out-of-level testing, allowing for administration based on ability level. The majority completed the School Years Form (ages 7 to 17); however, four children with ASD and one control completed the Early Years protocol.

To measure the degree of repetitive behaviors in our sample, parents completed the Repetitive Behavior Scales-Revised (RBS-R; Bodfish et al., 1999). The RBS-R is a 43-item parent-report rating scale that rates the occurrence of restricted and repetitive behaviors on a 4 point Likert scale from (0) does not occur to (3) occurs frequently and/or is severe. Ratings are based on a) the frequency of the behavior, b) how difficult the behavior is to interrupt, and c) the degree of interference caused by the behavior. The RBS-R generates six subscale scores, which were summed to create a cumulative score for analysis.

Parents also completed the Interests Scale (Bodfish et al., 2003) to measure the presence and severity of circumscribed interests. The scale consists of a checklist of interests that are summed to provide a score of current and past interests in their child. Seven additional questions ask the parent to select the child’s primary interest and rate the severity of this interest (frequency, social involvement, interference and accommodation). Higher scores indicate more interference/greater severity, with scores ranging from 0 to 23.

Of the 87 subjects recruited for the study, two ASD participants (both male) did not complete the eye-tracking task due to behavioral or attention issues during the testing procedure. Of the 85 subjects who completed the eye tracking paradigm, two children (1 ASD female, 1 TD female) did not have sufficient data (criteria set at greater than 20% overall fixation time to the screen). The final sample included 25 ASD males, 26 ASD females, 16 TD males and 16 TD females. Sample characteristics are reported in Table 1.

Table 1:

Sample Characteristics

Boys Girls Diagnosis Effects
(ASD vs. TD)
ASD
(n = 25)		 TD Controls
(n = 16)		 ASD
(n = 26)		 TD Controls
(n = 16)		 F P
Chronological Age
(months)		 113.12 (10.09) 93.50 (17.85) 102.00 (17.45) 95.75 (17.50) 13.37 <.001**
Mental Age
(months)		 115.23 (31.69) 124.30 (40.88) 96.75 (32.48) 115.31 (24.31) 3.51 .06
SCQ Score 14.80 (5.80) 3.50 (2.58) 13.88 (4.92) 2.19 (2.88) 125.43 <.001**
RBS-R Score 28.80 (16.91) 3.12 (2.87) 34.38 (23.03) 5.25 (8.85) 54.45 <.001**
Current Interests 12.92 (5.62) 11.87 (6.15) 14.23 (3.53) 13.87 (4.91) .27 .60
CI Intensity 14.36 (3.29) 9.94 (2.23) 14.81 (3.53) 9.19 (3.58) 46.66 <.001**
Open in a new tab

Mean (SD) unless otherwise noted

SCQ = Social Communication Questionnaire; RBS-R = Repetitive Behavior Scales-Revised; CI = Circumscribed Interests

**

significant to p = .01 level

Despite recruitment efforts, the ASD and TD groups differed in chronological age (CA; F = 13.37, p ≤.01) with the ASD group being older. There also was a diagnosis by sex effect, with ASD males being older than both TD males and females (F=3.57, p ≤.01). There was a marginal difference in MA between the diagnostic groups (F=3.51, p = .06) and sexes (F=3.47, p = .06). As a result, CA and MA were entered as co-variates in the main analysis adopting a similar approach to other eye tracking studies (Chevallier et al., 2015; Fletcher-Watson et al., 2009; Sasson & Touchstone, 2014). There were no differences between the ASD males and females in SCQ and other parent reported variables.

Eye-Tracking Stimuli

Visual Exploration Task.

The visual exploration task used in this study was based on the paradigm Sasson and colleagues developed (2008, 2011). However, rather than use the original visual search arrays, these were modified to include either male, female or neutral images. Participants viewed 18 static, high-quality color picture arrays consisting of 24 images each (for examples, see Figure 1). Six of the arrays were comprised of “male vs. female” arrays. These arrays contained images of toys, objects and common interests frequently reported as male or female typical. Six of the arrays were comprised of “male vs. neutral” arrays, containing images of either common male or neutral toys, objects or interests. Six of the arrays contained “female vs. neutral” images.

Figure 1:

Figure 1:

Open in a new tab

Examples of Visual Search Arrays

All images were public domain photographs obtained via the internet and were selected because of their similarity in size. Images were determined as common male, female or neutral based on previous literature (Caldera, Huston & O’Brien., 1989; Cherney & London, 2006; DeLoache et al., 2007; Robinson & Morris, 1986) and searching categories of male and female toys within our age range on various online stores. Our research group also met to discuss the gender of the images selected. Prior to data collection, we conducted a small (n = 20) online survey distributed via social media and email to parents of TD children (ages 6 to 10). This survey was designed to ensure that the images selected for each category (male, female, neutral) were representative of those categories. Parents were asked to rate whether they thought images were “male, female or neutral” toys or interests. Results confirmed the assigned categories (male, female or neutral) of images.

Male images included common characters (Star Wars®), building toys (Lego®) and game consoles. Female images included dress up toys, make up, dolls, tea sets and popular characters (Shopkins®, My Little Pony®). Neutral images included playground equipment, board games and gender neutral characters (Mickey Mouse®). As female toys are often more socially orientated than males (i.e. more dolls and characters), we ensured that the number of social images (images of characters/faces) in each category were balanced according to the ratio of the visual arrays.

Each of the picture arrays contained 24 total images. The mixture of categories within each array was determined by a set of image-type ratios designed to counter-balance the image contents of the arrays. Within each pairing of categories (i.e. male vs. female, male vs. neutral, female vs. neutral) the image ratios were as follows; 12:12, 12:12, 16:8, 8:16, 20:4, 4:20. This counter-balancing of ratios of image categories (male, female, neutral) across arrays was designed to minimize expectancy effects.

Eye-Tracking Procedure

Testing occurred in a single session at the (insert name of University following review). Eye-tracking data were collected using a Tobii T60XL eye tracker, which uses the Pupil Center Correction Reflection method to record eye movements from both eyes at a sampling rate of 60 Hz with spatial accuracy of approximately 0.5°. Children were tested individually, and sat either by themselves on a chair or on a cushion/booster seat to ensure a distance of approximately 60 cm from a 24” widescreen computer monitor. Raw eye tracking data were aggregated into fixations by Tobii Studio software using a fixation criterion of gaze remaining within a radius of 30 pixels for a minimum of 100 ms, as is consistent with previous research on using visual search arrays (Sasson et al., 2008; 2011). A five-point calibration procedure was completed prior to testing and repeated until quality was high.

Children were simply told that they would see lots of pictures on the screen and could look at them however they wanted. Visual search arrays were displayed one at a time for 10s each in a random order. Prior to each trial, a crosshair appeared at the center of the screen to reorient attention.

Data Analysis

We analyzed the same dependent variables as Sasson and colleagues (2008; 2011) to measure visual attention: (a) exploration (the number of unique images viewed) was quantified by tabulating the total number of different images on which the participant recorded at least one fixation; (b) perseveration (how long individual images were explored) was quantified by tabulating the total fixation time per image explored, and (c) detail orientation (the amount of detail each image was inspected) was quantified by tabulating the number of individual discrete fixations per image explored.

Analysis of visual attention between groups and sex was conducted using separate repeated measures ANOVAs on each of the three dependent variables, with image type (male, female or neutral) as the within-subject variable and group (ASD, TD) and sex (male, female) as the between group variables. Effects sizes were calculated using partial eta squared (small = 0.01; medium = 0.09; large = 0.25). All analyses controlled for CA and MA.

Results

Preliminary analysis revealed there was a main effect of diagnosis on the total percentage of time spent attending to the screen (F= 4.38, p = .03, ŋ2= .06). Children in the ASD group spent less time attending to the screen overall than TD controls (90.49% vs. 95.90%) and there was considerably more variability in total attention percentage (ASD = 36 – 99%; TD = 74 – 99%.). There were no effects of sex (F = .09, p = .76) or a diagnosis*sex interaction (F = .12, p = .73) on total attention to the screen. Total attention to the screen was entered as a covariate for Exploration, but not for Perseveration or Detail Orientation. Perseveration controls for attention to the screen by only examining images where a fixation has been registered. Detail Orientation is calculated considering total attention.

Exploration

Controlling for MA, CA and total time attending to the screen, there were no main effects of condition (image type), diagnosis or sex on the number of unique images explored (all p’s >.05; Figure 2). Despite no main sex and condition effects, there was a significant condition* sex interaction (F=20.41, p ≤.001, ŋ2= .21; Figure 2). Post hoc analyses revealed that females across diagnoses looked at more female images than males (t = −3.50, p ≤ .001).

Figure 2:

Figure 2:

Open in a new tab

Exploration by Image Type

Perseveration

There were no main effects of condition, diagnosis or sex on perseveration after controlling for CA and MA (all p’s >.05; Figure 3). As with exploration, there was a significant condition*sex interaction with a large effect size (F = 26.53, p ≤.001, ŋ2= .25). Females perseverated more to female images (t = −5.60, p ≤ .001) and males perseverated more to male images (t = 3.65, p ≤.001). There was no difference for perseveration to neutral images (t = .89, p = .38).

Figure 3:

Figure 3:

Open in a new tab

Perservation by Image Type

There was also an interaction between condition, diagnosis and sex (F = 4.70, p ≤ .01, ŋ2 = .05). The four groups differed in their perseveration to female images (F = 15.51, p ≤.001). ASD females perseverated more to female images than both ASD males (p = .03) and TD males (p = .01), but less than TD females (p = .008). TD females also perseverated more to female images than both ASD males (p ≤001) and TD males (p ≤.001). The groups further differed in their perseveration to male images (F = 4.39, p = .007). ASD males perseverated more to male images than ASD females (p = .05). TD males also perseverated more to male images than ASD females (p = .04). There were no differences in perseveration between ASD males and TD males to all image categories.

Detail Orientation

There were no main effects for diagnosis for detail orientation (p >.05); however, there were trends toward condition (F = 2.66, p = .07, ŋ2 = .03) and sex effects (F = 3.33, p = .07, ŋ2 = .04). Males trended to be more detail oriented than females (Figure 4) and overall subjects were more detail oriented to both male and neutral images. There was a significant condition*sex interaction with a large effect size (F = 27.53, p ≤.001, ŋ2 = .26). Males were more detail oriented to both male (t = 4.10, p ≤.001) and neutral images (t = 1.97, p = .05) than females.

Figure 4:

Figure 4:

Open in a new tab

Detail Orientation by Image Group

There was also a condition*diagnosis*sex interaction (F = 4.66, p ≤ .01, ŋ2 = .06). Post hoc analyses revealed differences in detail orientation to male images (F= 5.83, p ≤ .001) in both the ASD and TD males. ASD males were more detail oriented than both ASD females (p = .04) and TD females (p= .009) to male images. TD males were also more detail oriented than TD females (p ≤ .01). There were no differences between ASD and TD males.

Discussion

Previous studies have shown that children with ASD demonstrate restricted visual exploration and perseverative attention while viewing arrays containing images depicting common circumscribed interests (CI; Sasson et al., 2008; 2011). However, these studies were conducted with predominantly male samples using CI-related images of objects that are more typical of male than female interests (DeLoache et al., 2007). Given the emerging literature suggesting that differences in CI between males and females fall along traditional gender lines (Sutherland et al., 2017), the goal of the current study was to understand whether attention to gender-typical images differs between male and female children with and without ASD. Here, for the first time, a large sample of female children with ASD was included in a study of visual attention to CI in ASD. Across both ASD and TD groups, males and females explored a similar number of images overall and spent a similar amount of time fixating individual images. However, a number of condition by sex effects emerged, with females and males of both groups attending to a greater degree to images that corresponded with interests typically associated with their biological sex. The findings from the current study suggest that what captures the attention of ASD males and females differs and more closely aligns with gender differences observed in typical development (Caldera et al., 1989; Cherney & London, 2006; DeLoache et al., 2007; Robinson & Morris, 1986). Our findings of more typical attention patterns to more gender-typical images in ASD females is inconsistent with the EMB Theory of ASD, but consistent with recent evidence of sex differences in CI in ASD (Sutherland et al., 2017; Hiller et al., 2015).

Evidence for gender-typical visual interest in ASD males and females was found across each dependent measure. First, individuals with ASD explored more images that aligned with interests common to their biological sex in typical development. Whereas for males this aligns with the EMB predictions of ASD as male toys are more typically associated with greater systematizing (a key construct of the EMB theory), for ASD females it suggests a deviation from ASD male peers and more alignment with TD female peers. With greater exploration and time spent attending to female images, attention in ASD females mirrored what was observed for TD girls in this study. This confirms the importance of considering biological sex (and gender) when studying ASD females, and calls into question the applicability of EMB theory to explain visual attention to common CI by females on the spectrum. Importantly, these effects did not extend to neutral images. Thus, the eye-tracking evidence presented here converges with findings of gender typical preferences observed for ASD observationally (Harrop et al., 2017) and from parent report (Hiller et al., 2016; Knickmeyer et al., 2008; Sutherland et al., 2017).

Sex typical effects extended to perseveration, with males maintaining attention to male images relative to female images, with female participants demonstrating the opposite patterns. However, descriptively ASD females demonstrated similar rates of perseveration across image categories (Figure 3), suggesting that they potentially did not modulate their attention across image types to the same extent as other groups. This could reflect an underlying cognitive profile in ASD females that merits further study.

While preservation and exploration aligned with images associated with biological sex in typical development, detail orientation appeared to be specific to males in our sample. Males were more detail oriented than females to male and neutral images. This heightened detail orientation did not extend to female images, suggesting that males modulated their detail orientation dependent on the image type and they did not inspect female images with the same level of detail as they did male images. This aspect of attention also differentiated ASD males and females, with ASD males demonstrating greater detail orientation on male images than both TD and ASD females. ASD females, however, never demonstrated greater detail orientation than ASD males, even on female images. This finding suggests a greater detail orientation attentional profile in ASD males than females. This is consistent with a tendency to greater systemizing tendencies posited by the EMB theory of ASD, but again this finding only applied to males and not females with ASD.

Collectively, our findings suggest that the content of experimental paradigms (eye-tracking, ERP, behavioral) needs to be considered in ASD and cannot simply assume what has produced large group effects in largely male samples will apply to ASD females. As with reports of play and CI, females with ASD attended to images most aligned with their biological sex, and biological sex interacted with condition (image type) for all indices of attention. Our data also confirm the strong existence of differences in typical development in what captures and maintains the attention of males and females, which in turn has implications for learning practices. Overall, regardless of diagnosis, images that aligned most with biological sex produced the largest differences. This may reflect sexual dimorphism in what captures the attention of males and females (irrespective of diagnosis) or a potential socialization effect. We did not replicate the diagnostic effects observed in previous studies using a similar paradigm but with different image categories (Sasson et al., 2008, Sasson et al., 2011). ASD males in our study were comparable to TD males on indices of attention and to similar image categories (mostly male images). This may have occurred for several reasons. First, the male images included, while overlapping somewhat with common CI, were different from those in previous studies and may have not been sufficiently salient to drive similar effects. That is, the images captured our ASD males’ attention to the same degree as TD males, but the inclusion of high autism interest objects may have differentiated our ASD males further, replicating previous findings of circumscribed attention in (mostly male) ASD samples. Second, the power to detect effects between the ASD and TD groups may have been reduced by our inclusion of biological sex as a between-groups variable in addition to clinical diagnosis, as previous studies using a similar paradigm collapsed across sex and only included diagnosis as a between-group variable examined. Finally, differences in sample characteristics and methodology may have affected comparability between studies.

These findings also have implications for the appropriateness of gold-standard diagnostic measures and how we approach interventions with females on the autism spectrum. A number of diagnostic tools and screening instruments utilize items such as dolls and ask questions about engagement with make believe toys. Such questions may mask the appearance of CI in females as they may not align with preconceived assumptions of ASD (Lai et al., 2015) and contribute to the possibility of under diagnosing females with autism (Dean et al., 2017; Dworzynski et al., 2012; Frazier et al., 2014). Further, perseverative play is a diagnostic symptom often observed in ASD and might be less discernable in females during the diagnostic process using toys included within assessments that may more sensitively elicit this behavior in ASD males. Therefore, diagnostic and screening processes that utilize toys within assessments and infer symptoms should consider using sex-neutral, or sex-specific toys to minimize the chances that males and females will perform differently based upon motivational factors related to items used. Further, as CI have been utilized within ASD intervention to encourage motivation and have led to a number of positive outcomes (Koegel, et al., 2012; Legoff & Sherman, 2006; Vismara & Lyons, 2016), CI selected should not be general (e.g. trains and Lego) as the focus of male and female interests may produce differential outcomes within these intervention models.

While our findings suggest greater motivation in ASD females to images aligning with their own gender, a recent study of ASD adults reported elevated affective responses to images of CI by adults with ASD (Sasson et al., 2012). The greatest effects were found for ASD females, despite the more male focus of the images. Thus, the content and engagement with CI may change with development in ASD females and may reflect more commonly observed CI with time and experience pointing to the need for longitudinal studies of ASD females.

Overall, our condition*sex effects were stronger for female images (and therefore ASD and TD females) than those for male and neutral objects. Females with ASD explored and perseverated more on female images than males with ASD. While the study design ensured that male and female images included the same number of faces/characters to minimize the chances that social content of the images drove these effects, it remains a possibility that female images represented more socially-relevant interests and activities than male images. For instance, although containing no explicit social content, tea sets are typically interacted with socially (with a real or imagined playmate). Thus, greater preference for female images by ASD females may reflect greater social motivation relative to their male counterparts that has been reflected in other studies (Sedgewick et al., 2016) and contribute to greater social camouflage reported in ASD females (Dean et al., 2017; Head et al., 2014).

Limitations

As subjects in our study did not view the original arrays including common CI-related images (Sasson et al., 2008; Sasson et al., 2011), we cannot conclude that ASD females would not attend similarly to what has been reported previously for majority ASD male samples (i.e. more focused attention to images such as road signs, trains and electronics). Based on parent report data (Sutherland et al., 2017; Hiller et al., 2015), we designed our paradigm as a test of typical sex differences. However, future research should embed CI-related images and personalized images within gendered arrays to see whether the effects of CI or gender are stronger and whether previous diagnostic effects emerge particularly for ASD males (Sasson et al., 2008; Sasson et al., 2011).

Prior to developing our paradigm, we surveyed parents of TD children to confirm the group membership (male, female or neutral) of a random selection of our images. However, our survey was small and there is potential that some biases could exist. For example, parents from different racial/ethnic, cultural and SES backgrounds may rate images differently. We also did not gather information about the familiarity with toys included in our arrays. Further, there were a number of images that could represent trends at the time of data collection, such as Pokemon Go® and Frozen®.

As the eye-tracking task was a brief passive-viewing task, this study required minimal cognitive demands therefore we did not specifically match on MA. This decision was made due to the difficulty in recruiting ASD females and wanting to recruit a larger than typical sample of ASD females. Specifically, males with ASD in our study had a higher MA than females with ASD. This difference was expected given reports of higher-functioning females being diagnosed later and the presence of co-occurring intellectual disability being a factor in ASD diagnoses for females (Dworzynski et al., 2012). While we co-varied MA and CA to control for differences in functioning, a strategy consistent with previous eye-tracking studies of ASD (Chevallier et al., 2015; Fletcher-Watson et al., 2009; Sasson & Touchstone, 2014), it is possible that both higher functioning males and females may be more successful and motivated at engaging with more typical interests, such as those aligning with their biological sex. These effects may not be observed to the same degree in lower functioning children. Future studies are encouraged to explore whether patterns reported here vary a function of MA in males and females with ASD and more tightly match samples. Finally, although the sample size of females with ASD is a notable strength of our study, our sample of TD controls was relatively small. The control groups were identical in size to one another (N = 16), but smaller than our ASD groups, and this discrepancy may have affected the power to detect effects of clinical status.

Conclusions

Across multiple measures of visual attention, ASD and TD females in this study explored more female images and spent more time attending to these, whereas ASD and TD males explored more male images, spent more time attending to these and were also more detail oriented to male and neutral images. Collectively, results suggest sex differences in visual attention in ASD that align with sex-typical patterns. These findings are inconsistent with the EMB theory of autism that would predict a more male visual profile of attention in females with ASD, and highlight the importance of considering what is typical when researching, diagnosing, and treating ASD females.

Acknowledgments

Funding: Research reported in this publication was supported by a North Carolina Translational and Clinical Sciences (NC TraCS) Pilot Grant (2KR691506) and Autism Science Foundation Accelerator Grant (16-003A) awarded to Clare Harrop. The research reported was also supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development (R01HD082127 and P30-HD03110) and the National Center for Advancing Translational Sciences (UL1TR001111) of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of NC TraCS, the Autism Science Foundation or the National Institutes of Health.

Portions of this data has been presented at the International Meeting For Autism Research 2017 (San Francisco, CA), the NIH Future Research Leaders Conference (Bethesda, MD) and the Triangle Neuroscience Conference (Raleigh, NC). We thank the families whose participation made this study possible.

Footnotes

Conflict of Interest: CH has received research grants from North Carolina Translational and Clinical Sciences (NC TraCS) and Autism Science Foundation. BB has received research grants from the Eunice Kennedy Shriver National Institute of Child Health & Human Development. All authors declare no conflicts of interests.

At the time the study was conducted, Dr. Brian Boyd was at the University of North Carolina at Chapel Hill.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Baron-Cohen S (2002). The extreme male brain theory of autism. Trends in Cognitive Sciences, 6, 248–254. doi: 10.1016/s1364-6613(02)01904-6 [DOI] [PubMed] [Google Scholar]
  2. Baron-Cohen S (2009). Autism: the empathizing-systemizing (E-S) theory. Ann N Y Acad Sci, 1156, 68–80. doi: 10.1111/j.1749-6632.2009.04467.x [DOI] [PubMed] [Google Scholar]
  3. Bargiela S, Steward R, & Mandy W (2016). The experiences of late-diagnosed women with autism spectrum conditions: An investigation of the female autism phenotype. Journal of Autism and Developmental Disorders, 46, 3281–3291 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bishop SL, Guthrie W, Coffing M, & Lord C (2011). Convergent validity of the Mullen Scales of Early Learning and the differential ability scales in children with autism spectrum disorders. Am J Intellect Dev Disabil, 116, 331–343. doi: 10.1352/1944-7558-116.5.331 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bodfish J (2003). Interests Scale. Chapel Hill, NC. [Google Scholar]
  6. Bodfish J, Symons F, & Lewis M (1999). The Repetitive Behavior Scale–Revised. Western Carolina Center Research Reports. [Google Scholar]
  7. Caldera YM, Huston AC and O’Brien M (1989) Social interactions and play patterns of parents and toddlers with feminine, masculine, and neutral toys. Child Development, 60(1): 70–76. [DOI] [PubMed] [Google Scholar]
  8. Cherney ID, & London K (2006). Gender-linked differences in the toys, television shows, computer games, and outdoor activities of 5-to 13-year-old children. Sex Roles, 54(9-10), 717 [Google Scholar]
  9. Chevallier C, Parish-Morris J, McVey A, Rump KM, Sasson NJ, Herrington JD, & Schultz RT (2015). Measuring social attention and motivation in autism spectrum disorder using eye-tracking: Stimulus type matters. Autism Res, 8, 620–628. doi: 10.1002/aur.1479 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Christensen DL, Baio J, Van Naarden Braun K, Bilder D, Charles J, Constantino JN, … Yeargin-Allsopp M (2016). Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years--Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. MMWR Surveill Summ, 65, 1–23. doi 10.15585/mmwr.ss6503a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Dean M, Harwood R, & Kasari C (2017). The art of camouflage: Gender differences in the social behaviors of girls and boys with autism spectrum disorder. Autism, 21, 678–689. doi: 10.1177/1362361316671845 [DOI] [PubMed] [Google Scholar]
  12. DeLoache JS, Simcock G, & Macari S (2007). Planes, trains, automobiles--and tea sets: extremely intense interests in very young children. Dev Psychol, 43, 1579–1586. doi: 10.1037/0012-1649.43.6.1579 [DOI] [PubMed] [Google Scholar]
  13. Dworzynski K, Ronald A, Bolton P, & Happe F (2012). How different are girls and boys above and below the diagnostic threshold for autism spectrum disorders? J Am Acad Child Adolesc Psychiatry, 51, 788–797. doi: 10.1016/j.jaac.2012.05.018 [DOI] [PubMed] [Google Scholar]
  14. Elliot CD (2007). Differential Ability Scales–Second edition (DAS-II). San Antonio, TX: Pearson. [Google Scholar]
  15. Fletcher-Watson S, Leekam SR, Benson V, Frank MC, & Findlay JM (2009). Eye-movements reveal attention to social information in autism spectrum disorder. Neuropsychologia, 47, 248–257. doi: 10.1016/j.neuropsychologia.2008.07.016 [DOI] [PubMed] [Google Scholar]
  16. Foss-Feig JH, McGugin RW, Gauthier I, Mash LE, Ventola P, & Cascio CJ (2016). A functional neuroimaging study of fusiform response to restricted interests in children and adolescents with autism spectrum disorder. Journal of neurodevelopmental disorders, 8(1), 15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Frazier TW, Georgiades S, Bishop SL, & Hardan AY (2014). Behavioral and cognitive characteristics of females and males with autism in the Simons Simplex Collection. J Am Acad Child Adolesc Psychiatry, 53, 329–340 e321-323. doi: 10.1016/j.jaac.2013.12.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Grelotti DJ, Klin AJ, Gauthier I, Skudlarski P, Cohen DJ, Gore JC, Volkmar FR, & Schultz RT (2005). fMRI activation of the fusiform gyrus and amygdala to cartoon characters but not to faces in a boy with autism. Neuropsychologia, 43(3), 373–85. [DOI] [PubMed] [Google Scholar]
  19. Harrop C, Green J, & Hudry K (2017). Play complexity and toy engagement in preschoolers with autism spectrum disorder: Do girls and boys differ? Autism, 21, 37–50. doi: 10.1177/1362361315622410 [DOI] [PubMed] [Google Scholar]
  20. Head AM, McGillivray JA, & Stokes MA (2014). Gender differences in emotionality and sociability in children with autism spectrum disorders. Mol Autism, 5, 19. doi: 10.1186/2040-2392-5-19 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hiller RM, Young RL, & Weber N (2014). Sex differences in autism spectrum disorder based on DSM-5 criteria: evidence from clinician and teacher reporting. J Abnorm Child Psychol, 42, 1381–1393. doi: 10.1007/s10802-014-9881-x [DOI] [PubMed] [Google Scholar]
  22. Hiller RM, Young RL, & Weber N (2016). Sex differences in pre-diagnosis concerns for children later diagnosed with autism spectrum disorder. Autism, 20, 75–84. doi: 10.1177/1362361314568899 [DOI] [PubMed] [Google Scholar]
  23. Joseph RM, Tager-Flusberg H, & Lord C (2002). Cognitive profiles and social-communicative functioning in children with autism spectrum disorder. Journal of Child Psychology and Psychiatry, 43, 807–821. doi: 10.1111/1469-7610.00092 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Klin A, Danovitch JH, Merz AB, & Volkmar FR (2007). Circumscribed interests in higher functioning individuals with autism spectrum disorders: An exploratory study. Research and Practice for Persons with Severe Disabilities, 32, 89–100. [Google Scholar]
  25. Knickmeyer RC, Wheelwright S, & Baron-Cohen SB (2008). Sex-typical play: masculinization/defeminization in girls with an autism spectrum condition. J Autism Dev Disord, 38, 1028–1035. doi: 10.1007/s10803-007-0475-0 [DOI] [PubMed] [Google Scholar]
  26. Koegel RL, & Covert A (1972). The relationship of self-stimulation to learning in autistic children. Journal of Applied Behavior Analysis, 5, 381–387. doi: 10.1901/jaba.1972.5-381 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Koegel RL, Firestone PB, Kramme KW, & Dunlap G (1974). Increasing spontaneous play by suppressing self-stimulation in autistic children. J Appl Behav Anal, 7, 521–528. doi: 10.1901/jaba.1974.7-521 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Koegel RL, Fredeen R, Kim S, Danial J, Rubinstein D, & Koegel L (2012). Using Perseverative Interests to Improve Interactions Between Adolescents with Autism and their Typical Peers in School Settings. J Posit Behav Interv, 14, 133–141. doi: 10.1177/1098300712437043 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Koenig KP, & Hough LW (2017). Characterization and Utilization of Preferred Interests: A Survey of Adults on the Autism Spectrum. Occupational Therapy in Mental Health, 33(2), 1–12. doi: 10.1080/0164212x.2016.1248877 [Google Scholar]
  30. Lai MC, Lombardo MV, Pasco G, Ruigrok AN, Wheelwright SJ, Sadek SA, … Baron-Cohen S (2011). A behavioral comparison of male and female adults with high functioning autism spectrum conditions. PLoS One, 6, e20835. doi:10.1371/journal.pone.0020835 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lai MC, Lombardo MV, Auyeung B, Chakrabarti B, & Baron-Cohen S (2015). Sex/gender differences and autism: setting the scene for future research. J Am Acad Child Adolesc Psychiatry, 54, 11–24. doi: 10.1016/j.jaac.2014.10.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Legoff DB, & Sherman M (2006). Long-term outcome of social skills intervention based on interactive LEGO play. Autism, 10, 317–329. doi: 10.1177/1362361306064403 [DOI] [PubMed] [Google Scholar]
  33. Loomes R, Hull L, & Mandy WPL (2017). What Is the Male-to-Female Ratio in Autism Spectrum Disorder? A Systematic Review and Meta-Analysis. Journal of the American Academy of Child & Adolescent Psychiatry, 56, 466–474. doi: 10.1016/j.jaac.2017 [DOI] [PubMed] [Google Scholar]
  34. Lord C, Risi S, Lambrecht L, Cook JEH, Leventhal BL, DiLavore PC, … Rutter M (2000). The Autism Diagnostic Observation Schedule—Generic: A Standard Measure of Social and Communication Deficits Associated with the Spectrum of Autism. J Autism Dev Disord, 30, 205–223. doi: 10.1023/a:1005592401947 [PubMed] [Google Scholar]
  35. Mercier C, Mottron L, & Belleville S (2000). A Psychosocial Study on Restricted Interests in High Functioning Persons with Pervasive Developmental Disorders. Autism, 4, 406–425. doi: 10.1177/1362361300004004006 [Google Scholar]
  36. Pierce K, & Courchesne E (2001). Evidence for a cerebellar role in reduced exploration and stereotyped behavior in autism. Biological Psychiatry, 49, 655–664. doi: 10.1016/s0006-3223(00)01008-8 [DOI] [PubMed] [Google Scholar]
  37. Robinson CC, & Morris JT (1986). The gender-stereotyped nature of Christmas toys received by 36-, 48-, and 60-month-old children: A comparison between nonrequested vs requested toys. Sex Roles, 15(1-2), 21–32. [Google Scholar]
  38. Rutter M, Bailey A, & Lord C (2003a). The Social Communication Questionnaire. United States of America: Western Psychological Services. [Google Scholar]
  39. Rutter M, Le Couteur A, & Lord C (2003b). ADI-R. Autism Diagnostic Interview Revised. Los Angeles: Western Psychological Services. [Google Scholar]
  40. Sasson NJ, Turner-Brown LM, Holtzclaw TN, Lam KS, & Bodfish JW (2008). Children with autism demonstrate circumscribed attention during passive viewing of complex social and nonsocial picture arrays. Autism Res, 1, 31–42. doi: 10.1002/aur.4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Sasson NJ, Dichter GS, Bodfish JW (2012). Affective responses by adults with autism are reduced to social images but elevated to images related to circumscribed interests. PLoS ONE, 7, e42457 10.1371/journal.pone.0042457 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Sasson NJ, Elison JT, Turner-Brown LM, Dichter GS, & Bodfish JW (2011). Brief report: Circumscribed attention in young children with autism. J Autism Dev Disord, 41, 242–247. doi: 10.1007/s10803-010-1038-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Sasson NJ, & Touchstone EW (2014). Visual attention to competing social and object images by preschool children with autism spectrum disorder. J Autism Dev Disord, 44, 584–592. doi: 10.1007/s10803-013-1910-z [DOI] [PubMed] [Google Scholar]
  44. Schopler E, Van Bourgondien ME, Wellman GJ, Love SR (2010). Childhood Autism Rating Scale – 2nd Edition Los Angeles: Western Psychological Services. [Google Scholar]
  45. Sedgewick F, Hill V, Yates R, Pickering L, & Pellicano E (2016). Gender Differences in the Social Motivation and Friendship Experiences of Autistic and Non-autistic Adolescents. J Autism Dev Disord, 46, 1297–1306. doi: 10.1007/s10803-015-2669-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. South M, Ozonoff S, & McMahon WM (2005). Repetitive Behavior Profiles in Asperger Syndrome and High-Functioning Autism. J Autism Dev Disord, 35, 145–158. doi: 10.1007/s10803-004-1992-8 [DOI] [PubMed] [Google Scholar]
  47. Sutherland R, Hodge A, Bruck S, Costley D, & Klieve H (2017). Parent-reported differences between school-aged girls and boys on the autism spectrum. Autism 1–10. doi: 10.1177/1362361316668653 [DOI] [PubMed] [Google Scholar]
  48. Turner-Brown LM, Lam KS, Holtzclaw TN, Dichter GS, & Bodfish JW (2011). Phenomenology and measurement of circumscribed interests in autism spectrum disorders. Autism, 15, 437–456. doi: 10.1177/1362361310386507 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Unruh KE, Sasson NJ, Shafer RL, Whitten A, Miller SJ, Turner-Brown L, & Bodfish JW (2016). Social Orienting and Attention Is Influenced by the Presence of Competing Nonsocial Information in Adolescents with Autism. Front Neurosci, 10, 586. doi: 10.3389/fnins.2016.00586 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Van Wijngaarden-Cremers PJ, van Eeten E, Groen WB, Van Deurzen PA, Oosterling IJ, & Van der Gaag RJ (2014). Gender and age differences in the core triad of impairments in autism spectrum disorders: a systematic review and meta-analysis. J Autism Dev Disord, 44, 627–635. doi: 10.1007/s10803-013-1913-9 [DOI] [PubMed] [Google Scholar]
  51. Vismara LA, & Lyons GL (2016). Using Perseverative Interests to Elicit Joint Attention Behaviors in Young Children With Autism. J Posit Behav Interv, 9, 214–228. doi: 10.1177/10983007070090040401 [Google Scholar]

RESOURCES