Bayesian approaches to autism: Towards volatility, action, and behavior

Colin J. Palmer, Rebecca P. Lawson, Jakob Hohwy

Research output: Contribution to journalArticleResearchpeer-review

48 Citations (Scopus)

Abstract

Autism spectrum disorder currently lacks an explanation that bridges cognitive, computational, and neural domains. In the past 5 years, progress has been sought in this area by drawing on Bayesian probability theory to describe both social and nonsocial aspects of autism in terms of systematic differences in the processing of sensory information in the brain. The present article begins by synthesizing the existing literature in this regard, including an introduction to the topic for unfamiliar readers. The key proposal is that autism is characterized by a greater weighting of sensory information in updating probabilistic representations of the environment. Here, we unpack further how the hierarchical setting of Bayesian inference in the brain (i.e., predictive processing) adds significant depth to this approach. In particular, autism may relate to finer mechanisms involved in the context-sensitive adjustment of sensory weightings, such as in how neural representations of environmental volatility inform perception. Crucially, in light of recent sensorimotor treatments of predictive processing (i.e., active inference), hypotheses regarding atypical sensory weighting in autism have direct implications for the regulation of action and behavior. Given that core features of autism relate to how the individual interacts with and samples the world around them (e.g., reduced social responding, repetitive behaviors, motor impairments, and atypical visual sampling), the extension of Bayesian theories of autism to action will be critical for yielding insights into this condition.

Original languageEnglish
Pages (from-to)521-542
Number of pages22
JournalPsychological Bulletin
Volume143
Issue number5
DOIs
Publication statusPublished - 1 May 2017

Keywords

  • Active inference
  • Autism spectrum disorder
  • Bayesian inference
  • Predictive coding
  • Volatility

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