Atypical gaze patterns in autistic adults are heterogeneous across but reliable within individuals.

Researchers studied how autistic adults look at TV shows compared to non-autistic adults. They found that each autistic person has their own consistent way of looking at things (like faces on screen), but these patterns vary greatly between different autistic people. While each person's eye movements were reliable and predictable, the differences between autistic individuals were so large that researchers couldn't use eye tracking to reliably identify who was autistic. This suggests autism may involve different subtypes with distinct visual attention patterns.

This study examined eye tracking patterns in 48 autistic adults and 105 controls while watching television sitcom episodes across two research sites. Researchers tested whether variability in autistic individuals' gaze patterns stems from unreliable individual responses or genuine heterogeneity across people. Using computer vision analysis, they found that autistic individuals showed equally reliable gaze patterns as controls when tracking specific video features like faces - reliable enough for individual identification from 2-minute video segments. However, attempts to classify participants into diagnostic groups failed due to significant heterogeneity within the autistic group.

The findings support the hypothesis that gaze patterns are individually reliable but highly variable across autistic individuals, potentially reflecting autism subtypes.

Findings suggest autism interventions should focus on individual visual attention patterns rather than assuming uniform group differences. The reliability of individual gaze patterns supports developing personalized assessment and intervention strategies, while heterogeneity across autistic individuals reinforces the importance of individualized approaches in clinical practice.

Relatively small sample size, assessment limited to two episodes from the same TV series, desktop-based eye tracking only, absence of other measures like neuroimaging or genetics that might reveal additional individual variability not captured by eye tracking alone.

Across behavioral studies, autistic individuals show greater variability than typically developing individuals. However, it remains unknown to what extent this variability arises from heterogeneity across individuals, or from unreliability within individuals. Here, we focus on eye tracking, which provides rich dependent measures that have been used extensively in studies of autism. Autistic individuals have an atypical gaze onto both static visual images and dynamic videos that could be leveraged for diagnostic purposes if the above open question could be addressed.

We tested three competing hypotheses: (1) that gaze patterns of autistic individuals are less reliable or noisier than those of controls, (2) that atypical gaze patterns are individually reliable but heterogeneous across autistic individuals, or (3) that atypical gaze patterns are individually reliable and also homogeneous among autistic individuals. We collected desktop-based eye tracking data from two different full-length television sitcom episodes, at two independent sites (Caltech and Indiana University), in a total of over 150 adult participants (N = 48 autistic individuals with IQ in the normal range, 105 controls) and quantified gaze onto features of the videos using automated computer vision-based feature extraction. We found support for the second of these hypotheses. Autistic people and controls showed equivalently reliable gaze onto specific features of videos, such as faces, so much so that individuals could be identified significantly above chance using a fingerprinting approach from video epochs as short as 2 min.

However, classification of participants into diagnostic groups based on their eye tracking data failed to produce clear group classifications, due to heterogeneity in the autistic group. Three limitations are the relatively small sample size, assessment across only two videos (from the same television series), and the absence of other dependent measures (e.g., neuroimaging or genetics) that might have revealed individual-level variability that was not evident with eye tracking. Future studies should expand to larger samples across longer longitudinal epochs, an aim that is now becoming feasible with Internet- and phone-based eye tracking. These findings pave the way for the investigation of autism subtypes, and for elucidating the specific visual features that best discriminate gaze patterns-directions that will also combine with and inform neuroimaging and genetic studies of this complex disorder.