Objective Numerous studies have identified abnormal gaze in individuals with autism. Yet only a limited number of findings have been replicated, the magnitude of effects is unclear, and the pattern of gaze differences across stimuli remains poorly understood. To address these gaps, we conducted a comprehensive meta-analysis of autism eye tracking studies. Method PubMed and manual search of 1,132 publications were used to identify studies comparing looking behavior to social and/or nonsocial stimuli between individuals with autism and controls. Sample characteristics, eye tracking methods, stimulus features, and regions-of-interest (ROI) were coded for each comparison within each study. Multivariate mixed-effects meta-regression analyses examined the impact of study methodology, stimulus features, and ROI on effect sizes derived from comparisons using gaze fixation metrics. Results The search revealed 122 independent studies with 1,155 comparisons. Estimated effect sizes tended to be small-to-medium, but varied substantially across stimuli and ROI. Overall, nonsocial ROIs yielded larger effect sizes than social ROIs; however, eye and whole face regions from stimuli with human interaction produced the largest effects (Hedge’s g=.47 and .50, respectively). Studies with weaker study designs/reporting yielded larger effects, but key effects remained significant and medium-sized, even for high-rigor designs. Conclusion Individuals with autism show a reliable pattern of gaze abnormalities that suggests a basic problem with selecting socially-relevant versus irrelevant information for attention and that is persistent across age and worsens during perception of human interactions. Aggregation of gaze abnormalities across stimuli and ROI could yield clinically useful risk assessment and quantitative, objective outcome measures.

Objective: The primary aim of this study was to develop and validate eye tracking-based measures for estimating autism spectrum disorder (ASD) risk and quantifying autism symptom levels. Method: Eye tracking data were collected from youth during an initial evaluation visit, with administrators blinded to all clinical information. Consensus diagnoses were given by the multidisciplinary team. Participants viewed a 5- minute video that included 44 dynamic stimuli from 7 distinct paradigms while gaze was recorded. Gaze metrics were computed for temporally-defined regions-of-interest. Autism risk and symptom indices aggregated gaze measures showing significant bivariate relationships with ASD diagnosis and Autism Diagnostic Observation Schedule 2 (ADOS-2) symptom severity levels in a training sample (75%, n=150). Receiver operating characteristic curve analysis and non-parametric correlations were used to cross-validate findings in a test sample (25%; n=51). Results: Most children (n=201, 92%) completed a valid eye tracking assessment (ages 1.6–17.6; 80% male; ASD n=91, non-ASD n=110). In the test sub-sample, the autism risk index had high accuracy for ASD diagnosis (area under the curve [AUC]=.86, 95%CIs=.75-.95), while the autism symptom index was strongly associated with ADOS-2 total severity scores (r=.41, p<.001). Validity was not substantively attenuated after adjustment for language, non-verbal cognitive ability, or other psychopathology symptoms (r=.40-.67, p>.001). Conclusion: Eye tracking measures appear to be useful quantitative, objective measures of ASD risk and autism symptom levels. If independently replicated and scaled for clinical use, eye tracking-based measures could be used to inform clinical judgment regarding ASD identification and to track autism symptom levels.

Objective Abnormal eye gaze is a hallmark characteristic of autism spectrum disorder (ASD), and numerous studies have identified abnormal attention patterns in ASD. The primary aim of the present study was to create an objective, eye tracking-based autism risk index. Method In initial and replication studies, children were recruited after referral for comprehensive multidisciplinary evaluation of ASD and subsequently grouped by clinical consensus diagnosis (ASD n=25/15, non-ASD n=20/19 for initial/replication samples). Remote eye tracking was blinded to diagnosis and included multiple stimuli. Dwell times were recorded to each a priori-defined region-of-interest (ROI) and averaged across ROIs to create an autism risk index. Receiver operating characteristic curve analyses examined classification accuracy. Correlations with clinical measures evaluated whether the autism risk index was associated with autism symptom severity independent of language ability. Results In both samples, the autism risk index had high diagnostic accuracy (area under the curve [AUC]=.91 and .85, 95%CIs=.81–.98 and .71–.96), was strongly associated with Autism Diagnostic Observation Schedule–Second Edition (ADOS-2) severity scores (r=.58 and .59, p<.001), and not significantly correlated with language ability (r≤|−.28|, p>.095). Conclusion The autism risk index may be a useful quantitative and objective measure of risk for autism in at-risk settings. Future research in larger samples is needed to cross-validate these findings. If a validated scale for clinical use, this measure could inform clinical judgment regarding ASD diagnosis and track symptom improvements.