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.

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The primary objectives of this study were to evaluate the structure and age‐related stability of social attention in English and Arabic‐speaking youth and to compare social attention between children with autism spectrum disorder (ASD), other developmental disabilities (DD), and typically‐developing controls. Eye‐tracking data were collected from US (N = 270) and Qatari (N = 242) youth ages 1–17, including children evaluated for possible ASD. Participants viewed 44 stimuli from seven social paradigms. Fixation was computed for areas of interest within each stimulus. Latent variable models examined the structure of social attention. Generalized estimating equation models examined the effect of age, sex, culture, and diagnostic group on social attention. The best‐fitting model included a general social attention factor and six specific factors. Cultural differences in social attention were minimal and social attention was stable across age (r = 0.03), but females showed significantly greater social attention than males (d = 0.28). Social attention was weaker in DD (d = −0.17) and lowest in ASD (d = −0.38) relative to controls. Differences were of sufficient magnitude across areas‐of‐interest to reliably differentiate DD from controls (AUC = 0.80) and ASD‐only from all other cases (AUC = 0.76). A social attention dimension that represents an early‐life preference for socially salient information was identified. This preference was cross‐culturally consistent and stable across development but stronger in females and weaker in DD, especially ASD. Given rapid and easy‐to‐collect remote eye tracking administration, social attention measurement may be useful for developmental monitoring. Acquisition of population norms, analogous to height/weight/head circumference, might enhance early screening and tracking of neurodevelopment. Lay Summary This research found that social attention is a single dimension of behavior that represents a strong preference for social stimuli, is consistent across cultures, stable across age, and stronger in females. Children with developmental disabilities had lower levels of social attention than neurotypical children and children with autism spectrum disorder had the lowest levels of social attention.

A future large sample phase 2 trial of the STS system is warranted and would benefit from extended study duration, an objective primary efficacy outcome, and careful attention to methodological issues that promote compliance with the intervention and study procedures.

Background The ability to quantitatively analyze how we look at a face and determine if this changes following facial surgery should be of interest to the plastic surgeon. Eye tracking technology (ETT) provides the ability to record where observers fixate when viewing a facial image, enabling quantitative data to be obtained comparing pre- and postoperative changes. Objectives The authors sought to investigate ETT as a novel outcome assessment tool, determining if facial rejuvenation surgery shifts attention away from the prominent signs of aging, and if so, where this attention shifts. Methods Twenty-five volunteers viewed 32 randomized frontal, oblique, and lateral images of 11 patients pre- and post-facelift. An eye movement monitoring system recorded the observer’s eye position, net dwell time, fixation count, fixation time, and revisits into predefined areas of interest. Data were grouped and analyzed by angle and areas of interest. Paired t tests were employed to detect significant differences in pre- and post-images. Results On frontal images, less dwell time, fixations, and revisits were noted on the bottom third, forehead, perioral region, and neck (P &lt; 0.05). On the lateral view, less visual attention was given to the neck, upper third, and perioral region, with more time in the cheek, nose, and middle third (P &lt; 0.05). On oblique images, less attention was given to the neck and upper lid with more aimed at the middle third of the face (P &lt; 0.05). Conclusions ETT provides quantitative data post-facial rejuvenation. Facial aesthetic surgery does alter where observers look when viewing a face, decreasing the time spent inspecting the prominent signs of aging.

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