Increased variability but intact integration during visual navigation in Autism Spectrum Disorder

Significance Recent computationally focused theories of Autism Spectrum Disorder (ASD) have postulated that the pathological condition is broadly defined by anomalies in either the width of sensory likelihoods (i.e., the reliability of incoming sensory information) and/or the strength and flexibility of priors (i.e., contextual information)—the two components forming Bayes’ Rule. Furthermore, many consider that the process of integration is impaired in ASD. Here, we use an ecologically valid and data-rich navigation task to fit Bayesian likelihoods and priors as well as examine how self-velocity estimates are integrated into self-position in control and ASD subjects. Results suggest that priors and integration are intact in ASD; instead, their variability is heightened.