The use of eye-tracking technology as a tool to evaluate social cognition in people with an intellectual disability: a systematic review and meta-analysis

Abstract

Background

Relatively little is known about social cognition in people with intellectual disability (ID), and how this may support understanding of co-occurring autism. A limitation of previous research is that traditional social-cognitive tasks place a demand on domain-general cognition and language abilities. These tasks are not suitable for people with ID and lack the sensitivity to detect subtle social-cognitive processes. In autism research, eye-tracking technology has offered an effective method of evaluating social cognition—indicating associations between visual social attention and autism characteristics. The present systematic review synthesised research which has used eye-tracking technology to study social cognition in ID. A meta-analysis was used to explore whether visual attention on socially salient regions (SSRs) of stimuli during these tasks correlated with degree of autism characteristics presented on clinical assessment tools.

Method

Searches were conducted using four databases, research mailing lists, and citation tracking. Following in-depth screening and exclusion of studies with low methodological quality, 49 articles were included in the review. A correlational meta-analysis was run on Pearson’s r values obtained from twelve studies, reporting the relationship between visual attention on SSRs and autism characteristics.

Results and conclusions

Eye-tracking technology was used to measure different social-cognitive abilities across a range of syndromic and non-syndromic ID groups. Restricted scan paths and eye-region avoidance appeared to impact people’s ability to make explicit inferences about mental states and social cues. Readiness to attend to social stimuli also varied depending on social content and degree of familiarity. A meta-analysis using a random effects model revealed a significant negative correlation (r = −.28, [95% CI −.47, −.08]) between visual attention on SSRs and autism characteristics across ID groups. Together, these findings highlight how eye-tracking can be used as an accessible tool to measure more subtle social-cognitive processes, which appear to reflect variability in observable behaviour. Further research is needed to be able to explore additional covariates (e.g. ID severity, ADHD, anxiety) which may be related to visual attention on SSRs, to different degrees within syndromic and non-syndromic ID groups, in order to determine the specificity of the association with autism characteristics.

Social cognition refers to the ability to spontaneously read and interpret social and emotional cues [1]. Social-cognitive abilities are conceptualised hierarchically, with visual social attention viewed as a necessary precursor for effective appraisal of mental states [2,3,4]. Eye-tracking technology has been used to detect early emerging differences in visual social attention in autistic people and their infant siblings. Examples include reduced gaze-following and inattention to social cues [5,6,7,8,9]. It is thought that these differences in visual social attention contribute to challenges with higher-level appraisal abilities (e.g. misinterpretation of facial expressions, mentalising difficulties) that are evident across the lifespan of some autistic people [10,11,12]. Social-cognitive differences have been shown to predict social difficulties in autistic children and adults without intellectual disability (ID) [13, 14]. Unfortunately, people with ID are often excluded from autism research despite high co-occurrence [15], and studies of social cognition are no exception. In this article, we begin by highlighting how eye-tracking technology could advance social-cognitive research for people with ID. We emphasise the importance of improved accessibility and sensitivity, with reference to the autism literature. A systematic review is then used to synthesise social-cognitive research which has used eye-tracking technology in ID. A meta-analysis was conducted to explore the relationship between visual social attention during these tasks and autism characteristics across ID groups.

Social cognition and intellectual disability

Social functioning is inherent in the conceptualisation of ID, with evaluation of day-to-day social abilities being one of several core components used to determine a person’s global adaptive functioning, alongside IQ [16]. Autism¹frequently co-occurs with ID (> 40% [16, 21]) and a number of genetic syndromes in which ID is central to the phenotype (e.g. fragile X, Cornelia de Lange, Prader-Willi syndrome), present with an increased prevalence of clinically significant autism characteristics [22,23,24]. However, relatively little is known about the development and profile of social-cognitive abilities among people with ID, particularly with regard to co-occurring autism.

Traditional measures of social cognition are typically demanding on language and domain-general cognitive abilities. The participant is shown a stimulus or vignette and asked to verbally identify a character’s thoughts, feelings and/or intentions. During these tasks, the participant is required to hold the stimuli and/or scenario in mind, understand a test question and provide a response. In autistic adults without ID, performance on such measures has been related to IQ [25]. It is therefore unsurprising that people with genetic syndromes associated with ID score relatively poorly when traditional social-cognitive measures are used [26]. Furthermore, performance on social-cognitive tasks in people with ID has been related to executive function (e.g. [26, 27]) and language (e.g. [28]) difficulties. Even in genetic syndromes (i.e. Williams syndrome) where social cognition has been thought to be a relative strength [29], social-cognitive strengths are primarily evident among those with a milder severity of ID [30, 31]. Together, this highlights the challenge of disentangling social-cognitive abilities from the language and domain-general cognitive difficulties which are central to ID when traditional measures are used.

Though social difficulties may be characteristic of ID [16], the nature of these difficulties and the degree to which they manifest in each genetic syndrome is highly variable. For instance, people with Down syndrome and Rubinstein-Taybi syndrome present with high levels of social motivation [32, 33], whereas Cornelia de Lange syndrome and fragile X syndrome are characterised by social anxiety and extreme shyness [34]. Notably, profiles of autism characteristics are highly heterogeneous and appear qualitatively different, often in very subtle ways, across genetic syndromes and when compared to non-syndromic² autism [35]. This heterogeneity cannot be accounted for by degree of ID severity [22] and appears to reflect the broader behavioural phenotypes presented in specific genetic syndromes [36]. For instance, people with Down syndrome who score above threshold on autism screening tools are less withdrawn from their surroundings than those with non-syndromic autism—representing their high levels of social motivation [37]. Given the association between social cognition and social behaviour in autism [13, 14], it is possible that variable profiles of social-cognitive strengths and difficulties may also underly these heterogeneous profiles of autism characteristics in genetic syndromes associated with ID.

To further delineate autism profiles, Ellis and colleagues [38] measured the developmental sequence of early social-cognitive skills (i.e. intention reading) by using behavioural responses to basic goal-directed actions—suitable for children with ID and limited language. Relative to neurotypical children, children with Rubinstein-Taybi, Cornelia de Lange, and fragile X syndrome demonstrated similarly delayed acquisition of early social-cognitive skills as autistic children. However, children with these genetic syndromes did not pass tasks in the same order as autistic and neurotypical children. Performance was not related to general cognitive delay, pointing to an alternative mechanism which may be disrupting the sequence in which social-cognitive abilities are acquired. This study demonstrates that in genetic syndromes, behavioural phenotypes and related profiles of autism characteristics may be underpinned by divergent trajectories of social-cognitive development. However, conclusions are limited as behavioural observation lacks sensitivity to detect more subtle mechanisms underlying these social-cognitive processes within and across ID groups.

Eye-tracking as a tool to evaluate social cognition in autism

In autism research, eye-tracking technology has become an increasingly popular method of studying early emerging differences in visual social attention [5, 7], which differentiate autistic and neurotypical people [6, 9]. Studies on autistic toddlers have found that reduced gaze towards people within social scenes [39], the eye region of faces [40] and increased preference for non-social (versus social) stimuli [41] is significantly correlated with greater severity scores on the Autism Diagnostic Observational Schedule (ADOS; [41]). These findings have also been evidenced among autistic children [42], adults [43] and in the broader autism phenotype [44, 45]. Significant correlations between visual social attention and autism characteristics have also been evidenced using screening questionnaires [46, 47], and changes in visual social attention have been associated with behavioural change over time [48].

A key benefit of eye-tracking technology is that paradigms can be devised which present participants with stimuli in a passive, free-viewing manner, without the need for explicit responses or verbal demands. Not only has this supported research on ‘markers’ of autism in infancy [8, 49], but has provided a more sensitive method of studying higher-level social-cognitive abilities. For example, anticipatory gaze has been used as a non-verbal measure of false-belief reasoning [50]. Similar to traditional false-belief measures (e.g. the Sally-Anne task; [51]), participants are shown a change-location scenario, where the location of an object is moved when the actor is not looking. Autistic adults are less likely to show anticipatory gaze towards where the actor last saw the object when they return, appearing to not anticipate the actor’s false-belief [50, 52, 53]. Interestingly, these adults were able to pass traditional false-belief measures which required a verbal response, suggesting their language ability and possibly other strategies (e.g. learning the ‘rules’) were able to compensate for underlying social-cognitive difficulties. These findings illustrate how eye-tracking can reduce the confound of language and domain-general cognition, even when measuring higher-level social-cognitive abilities—highlighting potential as an inclusive and accessible tool to evaluate social cognition in autistic people with few or no words [54, 55].

The systematic review and meta-analysis

Eye-tracking technology is a sensitive and direct method of measuring social-cognitive abilities, independent of language and with reduced domain-general cognitive demands. Furthermore, there is evidence of an association between visual social attention and autism characteristics in autistic people and the broader autism phenotype. Despite extensive work in autism research, no review to our knowledge has explored how eye-tracking technology has been used to evaluate social cognition among people with ID. The aim of the systematic review was to provide an account of research which has used eye-tracking paradigms to study social-cognitive abilities in ID. A meta-analysis was used to explore whether visual social attention during these tasks correlated with degree of autism characteristics presented on clinical assessment tools. Synthesis of current research in this way is a necessary step to begin to evaluate the utility and feasibility of eye-tracking as a methodology to study social cognition and autism in ID.

Methods

Literature search

Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA [56]) Statement, a systematic review was conducted. The key components for the search query were (1) intellectual disability and (2) eye-tracking. The intellectual disability component included terms for both syndromic (e.g. ‘genetic syndrome*’, ‘fragile X syndrome*’) and non-syndromic (e.g. ‘intellectual disab*’) groups. Where databases allowed, controlled vocabulary (e.g. Medical Subject Headings [MeSH]) was also included. Search terms were determined from an initial scoping of literature, followed by investigation of controlled vocabulary. Social cognition was not included as a separate component, as some eye-tracking terms describe social-cognitive abilities (e.g. ‘face scan*’). Peer review of the search strategy was conducted to improve the quality, using the PRESS guidelines [57]. The full systematic review search strategy and search queries were pre-registered and are available to access: https://osf.io/ktp2r/.

Searches were conducted in PsycINFO, MEDLINE, Embase and Web of Science. Filters for the databases were used where possible to include the following: (a) English language, (b) peer-reviewed and grey literature (c) published between 2000 and 2022 and (d) human participants. Only literature available in English was included to ensure consistency in definitions related to intellectual disability, eye-tracking and social cognition. Searches were also conducted through relevant ID research mailing lists, as well as forwards/backwards citation tracking.

Inclusion and exclusion criteria

All identified records were pooled, and duplicates were removed (see Fig. 1). Titles and abstracts from identified records were screened using the following exclusion criteria: (1) studies that code eye gaze from observation or use a neuroimaging technique, rather than using eye-tracking technology, and (2) papers available only in a language other than English. To be included, papers needed to report empirical research. The title or abstract had to indicate that the method of data collection involved an eye-tracking paradigm which measured responses to social stimuli (e.g. emotional expressions, social scenes) or a social-cognitive task (e.g. false-belief reasoning). Studies which focused only on response to threat/anxiety (e.g. fearful faces) were not included, given the known interplay between anxiety and social functioning (e.g. in Williams syndrome [58]).

Fig. 1

PRISMA (2020) flow diagram for systematic reviews

The dependent variable of interest was visual attention. Examples of variables include proportion of fixations towards areas of interest, overall dwell time and/or direction of first saccade. Studies included had at least one group of participants with syndromic or non-syndromic ID. Groups where associated ID and adaptive functioning is highly variable (e.g. autism, Klinefelter syndrome) were excluded if either clinical diagnosis of ID or an appropriate metric indicating ID (e.g. IQ < 70) was not reported. These ID-specific descriptors were not required for inclusion of groups where ID is core to the behavioural phenotype (e.g. fragile X syndrome). Participants could be of any age. Study design was not specified. Two independent reviewers screened the studies’ titles (κ = .84) and abstracts (κ = .87), indicating excellent reliability. In cases of disagreement, a third party was consulted.

Quality rating

A quality criteria checklist from Cross and Hare [59] was used, which was originally created based on reported best practice for behavioural phenotype methodology. Criteria have been adapted to ensure they are applicable to samples with non-syndromic ID (see Table 1).

Table 1 Quality appraisal checklist based on Cross and Hare (2013)

For each of the criteria, the study was allocated a score of 0, 1 or 2 according to the degree to which the criterion was met. A score of 0 was also used when information was not stated or could not be assessed. The ‘developmental trajectory’ item included in the original Cross and Hare [59] checklist was removed due to it not being appropriate for the review aims, as is the case for other systematic reviews which have used the checklist [60, 61]. A total score of 0-12 can be achieved, with higher scores indicating greater quality. A quality rating in the upper tertial is recommended for study inclusion [59]. For the amended criteria used in this review, a rating in the upper tertial is indicated by a score of eight or more. One study was omitted [62] due to a methodological quality score below seven. Quality ratings were repeated for studies included in the meta-analysis (see Table 2), following the removal of criteria which were accounted for within the meta-analysis (i.e. sample size, appropriate statistics) or no longer relevant (i.e. comparison groups). In this instance, the maximum score was six.

Table 2 Overview of the studies included in the meta-analysis

Data extraction

Studies which met eligibility criteria were examined to extract data regarding the ID sample characteristics (i.e. ID aetiology, N, chronological age, general ability), exclusion criteria, comparison groups, social-cognitive domain measured, eye-tracking paradigm used and principal findings. Where possible, Pearson’s r value reporting the relationship between visual social attention and autism characteristics was also extracted (see Table 2). If a study measured autism characteristics, but this relationship was not explored, then a request was made to obtain Pearson’s r value from the authors via correspondence.

Results

Systematic review

The majority of paradigms measured expression discrimination (N = 16 [65, 66, 70, 78,79,80,81,82,83,84,85,86,87,88,89,90]; 31.37%) and social preference (N = 10 [64, 67, 71, 91,92,93,94,95,96,97]; 19.61%), whereas fewer investigated face recognition (N = 6 [73, 98,99,100,101,102]; 11.76%), social scene scanning (N = 8 [68, 80, 103,104,105,106,107,108]; 15.69%), gaze-following (N = 3 [63, 97, 109]; 5.88%), face scanning (N = 2 [69, 110]; 3.92%), attention to the eye region (N = 2 [72, 111]; 3.92%), overimitation (N = 2 [112, 113]; 3.92%), and false-belief reasoning (N = 1 [114]; 1.96%). Characteristics of the ID sample/s, comparison group/s, the eye-tracking paradigm and principal findings from each study are summarised in Table 3. Studies which used different eye-tracking paradigms to measure multiple social-cognitive domains are described separately.

Table 3 Overview of the reviewed studies which used eye-tracking to measure social cognition in intellectual disability

Data from these 49 studies were qualitatively synthesised to provide an account of (1) the ID sample characteristics and exclusion criteria, and (2) atypical visual social attention as an indicator of social-cognitive differences. These are presented in narrative form, to provide discussion regarding the inclusivity, accessibility, and sensitivity of eye-tracking technology as a measure of social cognition in ID.

Sample characteristics and exclusion criteria

Samples included those with Williams syndrome (N = 17; 29.31%), fragile X syndrome (N = 14; 24.14%), 22q11.2 deletion syndrome (N = 6; 10.34%), non-syndromic ID (N = 6; 10.34%), Rett syndrome (N = 3; 5.17%), Down syndrome (N = 3; 5.17%), Phelan-McDermid syndrome (N = 3; 5.17%), Cornelia de Lange syndrome (N = 2; 3.45%), Rubinstein-Taybi syndrome (N = 2; 3.45%), Angelman syndrome (N = 1; 1.72%) and Prader-Willi syndrome (N = 1; 1.72%). People with Williams syndrome were included in studies evaluating several social-cognitive domains, whilst the focus of social-cognitive research was much narrower for other populations. Sample size varied across studies, ranging from 3 to 75 participants (M = 20, SD = 11.25), reflecting the rarity of the genetic syndromes studied. Thus, a common caveat of the data presented going forward is small sample sizes (see Table 3). To attain a larger sample, most studies included a wide age range of both children and adults. Few studies focused on children under six years old [97, 112, 113], or toddlers and infants [110] specifically.

Studies in which full-scale IQ and adaptive functioning were measured reported samples characterised predominantly by those with a mild-moderate degree of ID (see Table 3). The mean full-scale IQ reported for ID samples ranged from 39.4 (± 5.82) to 73.8 (± 13.6), and adaptive behaviour composite scores ranged from 44.2 (± 10.1) to 69.9 (± 10.1). Hong and colleagues [93] focused on participants with Angelman syndrome, a genetic syndrome characterised by severe to profound ID, and reported that over half of their sample (N = 9) were unable to complete the eye-tracking task. In this study, adaptive functioning did not distinguish participants who engaged in the eye-tracking task from those who did not. Rather, unsuccessful eye-tracking was significantly associated with higher levels of hyperactivity and higher scores on the social motivation subscale of the SRS, indicating greater social motivation difficulties. The authors suggest measurement of these traits could be used as screening criteria to determine participant eligibility.

Challenges obtaining sufficient calibration (5- or 9-point) were commonly reported, leading to the exclusion of participants in both ID and comparison groups [68, 93, 95, 100, 105, 106, 109]. Inadequate number of fixations (e.g. on more than 40% of trials [68]) due to difficulties sustaining attention also led to the exclusion of a small number of participants [68, 70, 71, 79, 80, 95, 100, 105]. In addition, visual impairments (e.g. strabismus) [65, 70, 79, 86, 95, 109] and physical disability (e.g. scoliosis; [100]) were common reasons for exclusion. None of the studies provided metrics to describe the quality of the eye movement data obtained from the included (or excluded) participants.

Atypical visual social attention as an indicator of social-cognitive differences

Compared to neurotypical groups with similar chronological age and/or developmental level³, people with ID often had more difficulty spontaneously discriminating different emotional expressions (e.g. fragile X syndrome [65], Cornelia de Lange and Rubinstein-Taybi syndromes [66], Williams syndrome [85, 89]) and recognising novel faces (e.g. Rett syndrome, [100], 22q11.2 deletion syndrome [102]). People with ID also had more difficulty with following gaze (e.g. Williams syndrome [109]), fragile X syndrome [63]) and implicit anticipation of other people’s beliefs and mental states (e.g. Williams syndrome [70, 114]) than neurotypical children with similar chronological age and/or developmental level. The visual attention data which indicate these social-cognitive differences are discussed in further detail below, according to three key themes which were prominent within the reviewed literature: (a) limited exploration of social stimuli, (b) eye region avoidance and (c) response to familiarity and social content.

• (a) Limited exploration of social stimuli. Exaggerated fixations towards the eyes and face were reported in Down syndrome [104, 107, 110] and Williams syndrome [95, 97, 105, 106, 108, 112, 114] with an opposite looking pattern described in autistic comparison groups and those with fragile X syndrome with similar chronological age and/or developmental level. However, people with Down syndrome [110] and Williams syndrome [86] spent less time fixating on salient facial features when compared to neurotypical comparison groups with similar chronological age and/or developmental level; even when prompted to identify the expression viewed (in Williams syndrome [70]).

In Williams syndrome, reduced gaze towards facial features has been attributed to longer time taken to first fixate on the face [92, 108] and eyes [89, 111]. Once attended, people with Williams syndrome were less likely to disengage from these regions than neurotypical comparison groups with similar chronological age and/or developmental level. These ‘sticky fixations’ [114] had implications for recognition and interpretation of social cues. For example, children with Williams syndrome performed similarly to chronological age matched autistic children on an implicit false-belief reasoning task, as they remained fixated on the actor, rather than anticipating the object would be retrieved from where the actor saw it last, as was demonstrated in neurotypical children [114]. Children with Williams syndrome also had difficulty gaze-following, as they did not disengage their fixation from the face to follow the cued object, only doing so once prompted verbally [109]. When shown trustworthy and untrustworthy faces side-by-side, people with Williams syndrome spent longer fixating on one face in the pair, and reduced transitions between faces—showing no preference for either face type (unlike neurotypical groups matched on chronological age who prefer trustworthy faces [85]).

When compared to neurotypical groups matched on chronological age, people with 22q11.2 deletion syndrome also demonstrated shorter scan paths and fewer fixations to salient features of the face [78, 84, 88]. However, restricted scan paths were not face-specific in 22q11.2 deletion syndrome [87]. During facial recognition tasks, people with 22q11.2 deletion syndrome look longer at one face in the pair, and evidence reduced transitions between faces than chronological age matched neurotypical groups [102]; however, this was also evident for pairs of nonsocial stimuli [87]. Similar findings were also described in Rett syndrome [81, 100].

• (b) Eye-region avoidance. In fragile X syndrome, people demonstrated shorter initial [72] and overall [71, 82, 83] fixations to the eye region of faces when compared to chronological age matched neurotypical groups, appearing similar to autistic people [65, 79] and those with non-syndromic ID [73, 101]. Even when prompted to maintain eye contact, people with fragile X syndrome more frequently avoided fixating on the eye region than those with non-syndromic ID [69]. Interestingly, people with fragile X syndrome showed reduced fixations to the eye region across conditions in which gaze direction (averted/directed) was manipulated [72]. This persistent avoidance of the eye region may be why children with fragile X syndrome remained fixated on the face during gaze-following trials (unlike autistic and neurotypical children matched on verbal ability, who followed gaze towards the target object). Instead, pointing increased saccades towards a target object in fragile X syndrome [63].

In a number of studies, reduced looking at the eye region of faces was related to less accurate emotional discrimination and/or facial recognition. These findings were evident in Williams syndrome [70], fragile X syndrome [90], 22q11.2 deletion syndrome [98] and non-syndromic ID [73, 101]. An exception was identified in people with Prader-Willi syndrome, where people with the maternal uniparental disomy variant demonstrated overall reduced proportions of fixations to the eye region compared to those with paternal deletion variant, yet both groups showed similarly poor recognition accuracy for faces and emotional expressions [80].

• (c) Familiarity and social content. Syndrome-specific differences in perceptual capture and engagement whilst viewing social scenes appeared to be driven by degree of familiarity and the nature of the social content depicted. For instance, proportion of fixations across trials on actors in social scenes was similar in fragile X syndrome and neurotypical children comparable on receptive language [68]. However, when earlier and later trials were compared, those with fragile X syndrome were initially hesitant to fixate on an actor within a social scene [103]. Likewise, those with fragile X syndrome fixated less on an actor presented centrally in a scene, at least initially; this difference was not evident when the actor in the stimuli was located peripherally (in contrast to Williams syndrome;[108]). When viewing dynamic stimuli, the direction in which an actor was moving (towards/past) did not change the latency of fixation or overall dwell time in either fragile X syndrome or Rubinstein-Taybi syndrome, unlike autistic children and those with Cornelia de Lange syndrome, who were slower to fixate, and fixated less, on the actor moving towards them [67].

In Prader-Willi syndrome, exploration of social scenes became more atypical as the social content increased [80]. In contrast, children with Down syndrome were quicker to fixate on actors within a social scene than those with non-syndromic ID and autistic children [107], particularly when there were three actors depicted (compared to two) and sharing was occurring in the scene [104]. Similarly, people with Williams syndrome looked longer at an actor who was socially engaging (versus neutral) whilst demonstrating an action [112].

Autism-related similarities and differences in visual social attention

Though studies on expression discrimination (e.g. fragile X syndrome [79], 22q11.2 deletion syndrome [88]), social preference (e.g. Angelman syndrome [93], Phelan-McDermid syndrome [64]), gaze-following (e.g. Williams syndrome [109]) false-belief reasoning (i.e. Williams syndrome [114]) highlighted similarities between people with ID and autistic comparison groups comparable on chronological age and/or developmental, few studies considered how visual social attention may vary within ID groups by comparing those with co-occurring autism (non-syndromic ID [73, 101], Phelan-McDermid syndrome [94]). In addition, studies rarely analysed how visual social attention may be associated with clinical variables, such as autism characteristics (e.g. in Phelan-McDermid syndrome [99]), despite frequent discussion of how social-cognitive differences may underly social behaviour in ID groups.

Meta-analysis

An exploratory meta-analysis was conducted to see whether visual social attention during studies of social cognition in ID correlated with degree of autism characteristics presented on clinical assessment tools. As no previous meta-analyses have explored this relationship, and there were limited data available within the reviewed literature (k = 16), effect sizes from a variety of eye-tracking studies measuring different social-cognitive abilities were included. Across studies, the visual social attention variable captured allocation of gaze upon pre-defined areas of interest that were considered to be ‘socially salient’ regions (SSRs) of the stimuli (see Table 2). Larger scores indicate increased visual attention on SSRs. The dependent variable for autism characteristics was total score on either a standardised screening questionnaire (i.e. Social Responsiveness Scale [SRS; [75]], Social Communication Questionnaire [SCQ; [76]], Gilliam Autism Rating Scale [GARS; [77]) or direct observational assessment (ADOS; [74]). Higher scores on these measures suggest a greater frequency and/or severity of autism characteristics.

Data were included for studies on fragile X (FXS; k = 7 [63, 65, 67,68,69, 71, 72]; 43.75%), Cornelia de Lange (CdLS; k = 2 [66, 67], 12.5%), Rubinstein-Taybi (RTS; k = 2 [66, 67]; 12.5%), Williams (WS; k = 1 [70]; 6.25%), Phelan-McDermid (PMS; k = 1 [64]; 6.25%) and Angelman (AS; k = 1 [93]; 6.25%) syndromes, as well as non-syndromic ID (nsID; k = 2 [69, 73]; 12.5%). Three articles (63,79,103) included subgroups of people with ID of different aetiology (e.g. CdLS & FXS) within the same study; hence, to allow consideration of ID aetiology in the analysis, effect sizes for each group are included separately. Only effect sizes from the ID groups were analysed, as there was not sufficient data to perform the same analysis in comparison groups (e.g. autism, neurotypical) to compare effect sizes.

Data analysis strategy

Data were analysed in R, using the Metafor package, version 3.6.2. A random effects model and quality effects model was used, due to the likelihood of uncontrolled factors including methodological heterogeneity across studies. The random effects model weights each study based on the number of participants and the variation from findings across the full set of studies. The DerSimonian and Laird [116] method of random effects modelling was used to calculate between studies variation (tau), as there was no indication that the distribution of effects was not normally distributed. An additional quality effects model [117] was also used to explore variation due to methodological factors; this model weighted studies according to their quality ratings (see Table 2), in addition to number of participants. It can be interpreted as the meta-analytic effect that would have been obtained had all the studies been of the same methodological quality as the highest quality in the review. Pearson’s r values were transformed to Fisher’s Z scores for analysis and converted back to r for interpretation.

Methodological variation

Estimates of heterogeneity which can result from methodological variation in the studies were calculated using the Q statistic and I² statistic. The degree of heterogeneity was classified as ‘low’ (25%), ‘medium’ (50%) and ‘large’ (75%) [118]. Given the diverse methodologies included, variation was expected in the reported effects to reflect the methodological differences between studies. Therefore, I² < 75% was deemed acceptable for interpretation of a summary effect [119].

Planned contrasts

Subgroup analysis was applied from the outset to account for the different ID groups, to support ease of interpretation of the forest plot (see Fig. 2.). However, given that the number of effect sizes within each subgroup is ≤ four, there was not sufficient statistical power to conclude meaningful differences between each of the ID groups [120]. Instead, subgroup analyses were conducted on the following categorical moderator variables:

• (1) A group moderator variable was used to distinguish (a) FXS (k = 7) from (b) other ID groups (AS, CdLS, nsID, PMS, RTS, WS) (k = 9), given a high proportion of the effect sizes included were from people with FXS. Therefore, it was important to compare effect sizes from FXS to other ID groups, to assess these groups’ independent contributions to the overall effect.

• (2) A methodological moderator variable for measure of autism characteristics, categorised as (a) screening questionnaires (SCQ, SRS, GARS; k = 11) and (b) direct observational assessment (ADOS; k = 5) was used. Screening questionnaires are considered a less sensitive measure of autism characteristics than the ADOS (120) in ID. It was speculated this could result in a weaker effect.

Fig. 2

Forest plot of the relationship between visual attention on socially salient regions and autism characteristics

Summary effects and associated heterogeneity measures were calculated for each of the subgroup analyses. It was not possible to control for other clinical variables such as IQ, adaptive functioning, social functioning, or other behavioural outcomes which frequently co-occur with autism (e.g. anxiety, ADHD) within the analyses, due to data availability and variability in methodology.

Overall effect size

A total of 16 effect sizes were included, to inform a pooled effect size with data from a total of 283 participants. Results of the random effects model indicated that there was a negative correlation between visual attention on SSRs of the stimuli and autism characteristics, r = −.28, (95% confidence interval [CI −.47, −.08]), which was significantly different from zero (z = −2.65; p < .001). A significant level of heterogeneity (medium) was observed, (Q = 39.21, df = 15, p < .001, I² = 61.7%). This was expected, given the various methodologies included, and was deemed reasonable as it fell below the cut-off of 75%. Results of the quality effects model returned a slightly smaller estimate of the correlation, r = −.25 (95% CI [−.47, −.03]), in which a significant level of heterogeneity (medium) was also observed (Q = 39.20, df = 15, p < .001, I² = 61.7%). Visual inspection of the forest plot (see Fig. 2.) revealed preliminary evidence that in specific ID groups the direction of the effect was reversed, although confidence intervals spanned zero. For instance, in CdLS (k = 2) the pooled effect size was r = .27 (95% CI [−.14, .60]) and in RTS (k = 2) the pooled effect size was r = .32 (95% CI [−.04, .61]). Due to the small number of effect sizes available for these groups, the significance of these subgroup differences cannot be determined. Overall, estimates indicate a significant association between reduced visual attention on SSRs of the stimuli and higher autism characteristics across most ID groups.

Subgroup analyses

There was no significant difference between the pooled effect size for FXS and other ID groups (Q = .11, df = 1, p = .756). However, in FXS there was a trend towards a greater negative correlation between visual attention on SSRs and autism characteristics (r = −.31 (95% CI [−.47, −.14], k = 7) with smaller heterogeneity (I² = 0% [p = .878]), in comparison to other ID groups where the pooled effect was slightly smaller (r = −.25 [95% CI (−.57, .14), k = 9]) and there was much larger heterogeneity (I² = 78% [p < .001]). There was no significant difference between the pooled effect size from studies which used screening questionnaires compared to direct observational assessment (Q = 1.16, df = 1, p = .282). However, there was a trend towards a smaller negative correlation between visual attention on SSRs and autism characteristics on screening questionnaires (r = −.23 (95% CI [−.49, .07], k = 11), with larger heterogeneity (I² = 72% [p < .001]) than for direct observational assessment where the correlation was greater (r = −.42 (95% CI [−.60, −.19], k = 5) and heterogeneity was smaller (I² = 0% [p = .941]). Notably, many of the studies in FXS used direct assessment to measure autism characteristics (k = 4), and in most of the other ID groups, screening questionnaires were used. Therefore, it is not currently possible to account for possible influences of these moderating factors by estimating their contribution individually.

Publication bias

Publication bias was explored through inspection of funnel plots and the use of a trim and fill procedure which estimates the number of missing studies due to publication bias and calculates an adjusted effect size for the analysis. The funnel plot of the correlation between standard error by Fisher’s Z for the overall effect size is presented in Fig. 3. Visual inspection of the funnel plot demonstrated little evidence of publication bias, as the plot resembled a somewhat symmetrical (inverted) funnel with much of the study level effect within the boundaries. This conclusion was backed by Egger and colleagues’ [121] linear regression test of funnel plot asymmetry (bias = −.76, t(14) = −.50, p = .627). Using Duval and Tweedie’s [122] ‘Trim and Fill’ method no imputed studies were added. The uncorrected estimate of the effect size is −.29 (95% CI [ −.51, −.08). As there is little evidence of publication bias, the overall effect size value describing the relationship between visual attention on SSRs of the stimuli and autism characteristics can be seen to be reasonably robust.

Fig. 3

Funnel plot indicating the symmetry of the data in relation to publication bias

Discussion

To date, relatively little is known about social cognition in people with ID, particularly regarding whether these abilities are associated with autism characteristics. A limitation has been that traditional social-cognitive tasks place demands on domain-general cognition and language [26]. In autism research, eye-tracking technology has offered an effective method of evaluating social-cognitive abilities, independent of language ability (e.g.[7,8,9]), and indicated an association between visual social attention and autism characteristics (e.g. [41]). Here, we provided an account of research which has used eye-tracking paradigms to study social cognition in people with ID. An exploratory meta-analysis was used to estimate the degree to which visual attention to SSRs of the stimuli during these tasks may be related to degree of autism characteristics presented on clinical assessment tools.

Summary of findings

Eye-tracking technology was used to measure different social-cognitive abilities across syndromic and non-syndromic ID groups. A range of infants, children and adults were studied. Samples were predominantly characterised by individuals with a mild to moderate degree of ID, although the range of IQ and adaptive behaviour scores reported across the studies indicate that samples were inclusive of individuals across a range of ability levels. There was also an example here of successful inclusion of those with severe to profound ID [91]. These findings speak to the way in which eye-tracking technology can support inclusion of people with ID of different ages and ability in social-cognitive research. Although there is preliminary evidence (N = 8) to suggest that those with high levels of hyperactivity and greater social motivation difficulties (as defined by higher scores on the SRS) may find it challenging to sustain their attention throughout the task [91]. Methods of supporting engagement should be considered during experimental design, as an attempt to minimise exclusion and improve sample validity. Examples include using minimal (e.g. [2,3,4,5]) calibration points, short (< five minutes) task length, attention grabbers, and mobile eye-trackers tolerant to head movements. Notably, many studies required participants to provide explicit responses (e.g. verbally identify emotional expressions) alongside completion of the eye-tracking task [70, 78,79,80,81,82,83,84,85,86,87,88,89,90]. Such demands are likely to limit who can participate—particularly those with severe to profound ID. Therefore, passive-viewing paradigms (e.g. [65, 79]), used alongside tasks with minimal (if any) explicit demands, may improve accessibility. Co-occurrence of visual impairment and/or physical disability (e.g. scoliosis) can also limit participation, as is the case for eye-tracking research more broadly [123], and therefore should be expected.

Studies highlighted differences in spontaneous expression discrimination and facial recognition across ID groups. This may be partly due to shorter scan paths and longer fixations, also described as ‘sticky fixations’ [114], resulting in limited exploration of stimuli. Studies which explored the specificity of these gaze patterns, comparing responses on social versus non-social tasks, highlighted that a general visual processing difference may underly atypical visual social attention [87, 88, 100]. Regardless, many studies indicated that atypical attentional capture and appraisal of social information impacted response to social cues (e.g. gaze-following) and people’s ability to make explicit inferences about mental states [70, 73, 90, 98, 101]—demonstrating the significance of visual social attention for social-cognitive processes.

Furthermore, the gaze patterns seen on social-cognitive tasks were reminiscent of social behaviours described in specific syndromes. For instance, people with fragile X (a syndrome characterised by social avoidance; 32), tended to fixate less on the eye region of faces and were initially hesitant to look towards people. Likewise, in syndromes associated with hypersociability, such as Down syndrome and Williams syndrome [29, 32], a preference for faces and increased social content was described. Thus, differences in gaze patterns appear to parallel notable features of specific behavioural phenotypes.

Few studies considered how visual attention may vary within ID groups by comparing those with co-occurring autism or analysed the association between visual social attention and clinical variables, such as autism characteristics, despite frequent discussion of how social-cognitive differences may underly social behaviour in ID groups. The meta-analysis provided preliminary evidence of a relationship between reduced visual attention to SSRs of the stimuli and a greater degree of autism characteristics across people with ID. The range of effect sizes were similar in direction and size as the relationship between visual social attention and autism characteristics evident in previous research studying autistic people (e.g. [39, 41]). It is possible that the relationship shown here may be moderated by factors such as the aetiology of ID and/or the type of clinical assessment tool used. Though subgroup analyses highlighted some potential indications of this, the small number of effects and the highly confounded nature of these variables across studies prevent a firm drawing of conclusions.

More research within syndromic and non-syndromic ID is needed, to establish whether the strength and direction of the relationship seen here varies across ID groups. Current evidence, whilst limited, raises the intriguing possibility that in some groups—Cornelia de Lange and Rubinstein-Taybi syndrome—increased visual attention to SSRs of the stimuli may be related to greater autism characteristics. This should be investigated further and considered within the context of the heterogeneous autism profiles and divergent behavioural phenotypes (e.g. hypervigilance versus avoidance [67]) presented in these groups.

Methodological heterogeneity, small sample sizes and data quality

The social-cognitive domain studied most often using eye-tracking was expression discrimination. However, synthesis of the methodology highlighted variability in eye-tracking protocols and heterogeneity of stimuli used. There was also very little research on other abilities, such as false-belief reasoning (N = 1), which has been researched extensively in regard to the neurotypical development of social cognition [124] and theorised to be a core difficulty associated with autism [53, 125]. Furthermore, small sample size is a limitation of many of the studies reviewed, resulting in relatively low power and reduced replicability. Small sample sizes are also likely to be impacted by individual differences (e.g. age, co-occurring diagnoses) which are often broader in ID than that observed in neurotypical samples [126, 127]. Together, this emphasises the importance of sharing eye-tracking stimuli and protocols, to reduce methodological heterogeneity, enable further analyses of pooled effect sizes, and encourage replication. Given that there has been a much larger focus on using eye-tracking technology to measure social cognition in autism research, collaboration between autism and ID researchers is key to developing a bank of open access, validated paradigms. In doing this, researchers should establish normative data, which would support efforts to explore the developmental trajectory of mechanisms underlying social cognition in ID.

It should also be noted that none of the studies provided metrics to describe the quality of eye movement data beyond calibration, such as accuracy values (i.e. the difference between the true gaze position and the gaze position recorded) and the proportion of data loss, indicating a need to improve adherence to minimal reporting standards (e.g. [128]). Researchers should work towards incorporating these metrics where possible, considering associations with participant characteristics (e.g. hyperactivity), to support efforts to understand the feasibility of eye-tracking in ID more broadly [123].

Understanding the role of intellectual disability

The majority of the reviewed literature was on genetic syndromes, with Williams syndrome and fragile X syndrome being the groups studied most often. Surprisingly, there were relatively few studies in which a non-syndromic ID group were included, particularly those where a diagnosis of autism was reported. This may be, in part, due to ambiguity in the terminology used to describe autism co-occurring with ID. Some studies referred to samples as ‘low-functioning’, ‘minimally verbal’ or ‘severely’ autistic, in place of ID-specific descriptors—which, without evidence of co-occurring ID (e.g. IQ), led to exclusion from the review. With that being said, there is clearly a gap in current knowledge on social-cognitive processes in non-syndromic ID relative to syndromic ID, which should be explored further. A better understanding of what visual social attention is like in this group could support efforts to distinguish possible ID-, syndromic- and autism-specific social-cognitive profiles.

The degree to which associated ID may account for the relationship between visual attention on SSRs of the stimuli and autism characteristics is unclear. Limited data on IQ and/or adaptive functioning meant that degree of ID severity could not be explored as a factor within the meta-analysis. Although it should be noted that in studies where effect sizes were available for different ID groups [66, 67, 69], participants had been matched on adaptive functioning (ABC), yet there are clear differences in effect size and/or direction. For example, Crawford and colleagues [67] report a positive correlation between visual attention on SSRs and autism characteristics in Cornelia de Lange syndrome (ABC = 47.9 [SD =16.0]) and Rubinstein-Taybi syndrome (ABC = 47.8 [SD = 14.6]), whereas in fragile X syndrome (ABC = 51.3 [SD = 17.4]) this correlation was negative. The opposite association presented in these genetic syndromes indicates that the relationship between visual attention on SSRs and autism characteristics cannot be entirely attributed to adaptive functioning. Further research is needed to establish the extent to which ID severity, alongside other associated characteristics (e.g. ADHD, anxiety), contributes to the relationship between visual social attention and autism characteristics. It is particularly important to understand whether the nature of this association varies between genetic syndromes, given ongoing efforts to disentangle the heterogeneity of autism from characteristics inherent to the broader behavioural phenotype presented [129].

Visual social attention and the dyad of autism characteristics

The strength of association between visual social attention and autism characteristics in ID may differ in relation to social communication versus restricted and repetitive behaviour sub-scores on autism assessment tools. Studies with autistic children have reported a significant negative correlation between visual social attention and scores on the social affect subdomain of the ADOS (e.g. [130]). Yet, there is no association for the restricted and repetitive behaviour subdomain [131,132,133], whereas non-social visual attention in autism has been found to be strongly associated with restricted and repetitive behaviours [134]. These findings illustrate the ‘fractionation’ of autism characteristics at the cognitive level [135]. Here, we used total scores from clinical assessments of autism, due to there being limited data available. As restricted and repetitive behaviours are included alongside social communication difficulties in the total score, it is possible that the reported effect is weaker than it may be for social communication alone. To gain insight into the specificity of visual social attention and how it may be indicative of differences at the behavioural level in ID, further work is needed to establish whether the association is greater for social communication difficulties specifically. It is also important to consider the extent to which the relationship with autism characteristics is subserved by differences in visual attention more generally. That is, whether a high level of restricted and repetitive behaviours relate to the more restricted scan paths and ‘sticky fixations’ reported in ID groups.

Conclusions

Eye-tracking can be used as an accessible tool to measure more subtle social-cognitive processes among a range of people with ID. The reviewed literature highlighted differences in how people with ID attend to social stimuli compared to neurotypical comparison groups, and some similarities to autistic people. Interestingly, in genetic syndromes, some gaze patterns appear to parallel notable features of specific behavioural phenotypes. The meta-analysis provides preliminary evidence of a relationship between reduced visual social attention and a greater degree of autism characteristics on clinical assessment tools across ID groups. Together, these findings demonstrate that eye-tracking is sensitive to detecting discrete social-cognitive processes in people with ID, which appear associated with behavioural variability. Fine-grained measurement of social cognition could lead to improved understanding of autism and broader social differences presented by people with ID. Future research should seek to strengthen conclusions regarding visual social attention and the nature of association with autism characteristics, accounting for ID severity and other co-occurring conditions (e.g. ADHD, anxiety), in both syndromic and non-syndromic ID groups.