Investigated
How do Bayesian models explain psychosis?
Short answer
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Bayesian models describe the brain as combining prior expectations with sensory evidence. Psychosis may arise when the system assigns abnormal precision to priors, sensations, or prediction errors, producing hallucinations, delusions, or unstable beliefs. The framework is influential, but competing models and findings prevent a single settled explanation.
The views on this question
Two lenses on the same evidence. Source weight and the primary source ratio show what each rests on.
Alternative
Source weight 40%Lens adapted to this topic: Critiques and competing interpretations
Critical perspectives question whether Bayesian language specifies a biologically testable mechanism or mainly supplies a flexible description of symptoms. They also argue that domain-general inference can obscure the social and phenomenological character of delusions. Empirical findings conflict over whether priors are weakened or strengthened, and alternative accounts emphasize dynamic systems, ecological, embodied, or more socially specific explanations.
- Evidence does not consistently support weakened priors: some studies find stronger priors, and effects may differ across modalities and hierarchical levels.
- The Bayesian-brain framework has been criticized as potentially unfalsifiable, biologically implausible, and able to accommodate findings retrospectively.
- A social-phenomenological critique argues that abstract, domain-general inference may miss distinctive ways delusions concern other people and social meaning.
- Alternative frameworks discussed in the critique include dynamic-systems theory, ecological psychology, and embodied cognition.
- Psychotic-like experiences in nonclinical participants were associated with greater reliance on sensory information, but the study also found more complex effects when priors were uncertain.