Abstract
Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference that is, how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: (i) how proposed theories differ in accounts of ASD vs. schizophrenia and (ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals' likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due, not to weaker prior expectations, but to more precise sensory representations.
Highlights
IntroductionIn recent years Bayesian inference has come to be regarded as a general principle of brain function that underlies perception and motor execution, but hierarchically extends all the way to higher cognitive phenomena, such as belief formation and social cognition
Impairments of Bayesian inference have been proposed to underlie deficits observed in mental illness, schizophrenia1-‐‐3 and autistic spectrum disorder (ASD)4-‐‐7
We found that autistic traits were associated with less ’hallucinations’ (Fig. 4c; ρ = −0.270, p = 0.014), while schizotypal traits were found to have no effect on the number of ’hallucinations’ (RISC: ρ = 0.151, p = 0.173; Schizotypal Personality Questionnaire (SPQ) (N=39): ρ = 0.006, p = 0.971)
Summary
In recent years Bayesian inference has come to be regarded as a general principle of brain function that underlies perception and motor execution, but hierarchically extends all the way to higher cognitive phenomena, such as belief formation and social cognition. Impairments of Bayesian inference have been proposed to underlie deficits observed in mental illness, schizophrenia1-‐‐3 and autistic spectrum disorder (ASD)4-‐‐7. The general hypothesis for both disorders is that the weight, called “precision”, ascribed to sensory evidence and prior expectations is imbalanced, resulting in sensory evidence having relatively too much influence on perception. The systematically weakened low-‐‐level prior expectations might lead to forming compensatory strong and idiosyncratic high-‐‐level priors (beliefs), which would explain the emergence and persistence of delusions as well as reoccurring hallucinations . 1-‐‐3
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