Social Visual Perception Under the Eye of Bayesian Theories in Autism Spectrum Disorder Using Advanced Modeling of Spatial and Temporal Parameters.

Chara Ioannou,Christoph Klein,Ludger Tebartz Van Elst,Giuseppe Boccignone,Divya Seernani,Monica Biscaldi-Schaefer,Christian Fleischhaker,Maria Elena Stefanou

doi:10.3389/fpsyt.2020.585149

Abstract

Social interaction in individuals with Autism Spectrum Disorder (ASD) is characterized by qualitative impairments that highly impact quality of life. Bayesian theories in ASD frame an understanding of underlying mechanisms suggesting atypicalities in the evaluation of probabilistic links within the perceptual environment of the affected individual. To address these theories, the present study explores the applicability of an innovative Bayesian framework on social visual perception in ASD and demonstrates the use of gaze transitions between different parts of social scenes. We applied advanced analyses with Bayesian Hidden Markov Modeling (BHMM) to track gaze movements while presenting real-life scenes to typically developing (TD) children and adolescents (N = 25) and participants with ASD and Attention-Deficit/Hyperactivity Disorder (ASD+ADHD, N = 15) and ASD without comorbidity (ASD, N = 12). Regions of interest (ROIs) were generated by BHMM based both on spatial and temporal gaze behavior. Social visual perception was compared between groups using transition and fixation variables for social (faces, bodies) and non-social ROIs. Transition variables between faces, namely gaze transitions between faces and likelihood of linking faces, were reduced in the ASD+ADHD compared to TD participants. Fixation count to faces was also reduced in this group. The ASD group showed similar performance to TD in the studied variables. There was no difference between groups for non-social ROIs. Our study provides an innovative, interpretable example of applying Bayesian theories of social visual perception in ASD. BHMM analyses and gaze transitions have the potential to reveal fundamental social perception components in ASD, contributing thus to amelioration of social-skill interventions.

Highlights

Autism Spectrum Disorder is a heterogeneous disorder, with qualitative impairments in social interaction being one of the cardinal symptoms [1]
The process of Regions of interest (ROIs) generation by the Bayesian Hidden Markov Modeling (BHMM) is visualized in Figure 1, exemplary for stimulus A
We set out to investigate social perception in autism spectrum disorder groups under the framework of the current Bayesian autism spectrum disorder theories and using, to our knowledge for the first time in this field, dynamic gaze modeling with Bayesian Hidden Markov Models (BHMM) on real-life scenes

Summary

Introduction

Autism Spectrum Disorder is a heterogeneous disorder, with qualitative impairments in social interaction being one of the cardinal symptoms [1]. Some of the most recent and promising theories are the Bayesian theories of autism spectrum disorder, which unify a wide range of its clinical characteristics In this Bayesian framework, it is suggested that perception in autism spectrum disorder is atypically resistant to prior acquired information, the affected individual engages with an “almost always new” input in probabilistic terms when experiencing an event [5]. This implicit divergence in predictive coding is referred to as «Hypothesis of Predictive Impairment in Autism» [6]. These theories can explain manifestations of autism spectrum disorder such as atypicalities in apprehending social stimuli, inferring social causalities, predicting social intention or developing a regulating system for social processes [7]

Methods

Results

Conclusion