Gauging facial feature viewing preference as a stable individual trait in autism spectrum disorder. Issue 8 (19th May 2021)

• Record Type: Journal Article
• Authors: Reimann, Gabrielle E.; Walsh, Catherine; Csumitta, Kelsey D.; McClure, Patrick; Pereira, Francisco; Martin, Alex; Ramot, Michal
• Is Part Of: Autism research. Volume 14:Issue 8(2021)
• Page Start: 1670
• Page End: 1683
• Publication Date: 2021-05-19
• Subjects: autism spectrum disorder -- machine learning -- social behavior; Autism -- Periodicals; Autism -- Research -- Periodicals; 616.85882005
• DOI: 10.1002/aur.2540
• Languages: English
• ISSNs: 1939-3792

Abstract

Abstract: Eye tracking provides insights into social processing deficits in autism spectrum disorder (ASD), especially in conjunction with dynamic, naturalistic free‐viewing stimuli. However, the question remains whether gaze characteristics, such as preference for specific facial features, can be considered a stable individual trait, particularly in those with ASD. If so, how much data are needed for consistent estimations? To address these questions, we assessed the stability and robustness of gaze preference for facial features as incremental amounts of movie data were introduced for analysis. We trained an artificial neural network to create an object‐based segmentation of naturalistic movie clips (14 s each, 7410 frames total). Thirty‐three high‐functioning individuals with ASD and 36 age‐ and IQ‐equated typically developing individuals (age range: 12–30 years) viewed 22 Hollywood movie clips, each depicting a social interaction. As we evaluated combinations of one, three, five, eight, and 11 movie clips, gaze dwell times on core facial features became increasingly stable at within‐subject, within‐group, and between‐group levels. Using a number of movie clips deemed sufficient by our analysis, we found that individuals with ASD displayed significantly less face‐centered gaze (centralized on the nose; p < 0.001) but did not significantly differ from typically developing participants in eye or mouth looking times. Our findings validate gaze preference for specific facial features as a stable individual trait and highlight the possibility of misinterpretation with insufficient data. Additionally, we propose the use of a machine learning approach to stimuli segmentation to quickly and flexibly prepare dynamic stimuli for analysis. Lay Summary: Using a data‐driven approach to segmenting movie stimuli, we examined varying amounts of data to assess the stability of social gaze in individuals with autism spectrum disorder (ASD). We found a reduction in social fixations in participants with ASD, driven by decreased attention to the center of the face. Our findings further support the validity of gaze preference for face features as a stable individual trait when sufficient data are used.