Using Markov Models and Classification to Understand Face Exploration Dynamics in Boys with Autism

Abstract

Scanning faces is important for social interactions, and maintaining good eye contact carries significant social value. Difficulty with the social use of eye contact constitutes one of the clinical symptoms of autism spectrum disorder (ASD). It has been suggested that individuals with ASD look less at the eyes and more at the mouth than typically developing individuals, possibly due to gaze aversion (Tanaka & Sung, 2016) or gaze indifference (Chevallier et al., 2012). Eye tracking evidence for this hypothesis is mixed (e.g. Falck-Ytter & von Hofsten, 2011; Frazier et al., 2017). Face exploration dynamics (rather than the overall looking time to facial parts) might be altered in ASD. Recent studies have proposed a method for scanpath modeling and classification to capture systematic patterns diagnostic of a given class of observers and/or stimuli (Coutrot et al., 2018). We adopted this method combining Markov Models and classification analyses to understand face exploration dynamics in boys with ASD and typically developing school-aged boys (N = 42). Eye tracking data were recorded while participants viewed static faces. Faces were divided in areas of interest (AOIs) by means of limited-radius Voronoi tessellation (LRVT) (Hessels et al., 2016). Proportional looking time analyses show that both groups looked longer to eyes than mouth and we did not observe group differences in fixation duration to these features. TD boys look significantly longer to the nose while the ASD boys looked more outside the face. We modeled the temporal dynamics of the gaze behavior using Markov Models (MMs). To determine the individual separability of the resulting transition matrices we constructed a classification model using linear discriminant analysis (LDA). We found that the ASD group displays more exploratory dynamic gaze behavior as compared to the TD group, as indicated by higher transition probabilities of moving gaze between AOIs. Based on a leave-one-out cross validation analysis, we find an accuracy of 72%, implying that there is 72% chance to correctly predict group membership based on the face exploration dynamics. These results indicate that atypical eye contact in ASD might be manifested through more frequent gaze shifting, even when total looking time to the eyes is the same. Whereas individual accuracy is modest in this experiment, we hypothesize that when used in more realistic paradigms (e.g. real-life interaction), this method could be highly accurate in individual separability.