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The Predictive Coding Account of Psychosis.

Philipp Sterzer1, Rick A Adams2, Paul Fletcher3

  • 1Department of Psychiatry, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany.

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Summary
This summary is machine-generated.

Computational neuroscience offers insights into psychosis through predictive coding. This review explores how altered belief precision and prediction errors in the brain may explain psychosis symptoms, suggesting a more complex model.

Keywords:
Bayesian brainCognitionDelusionsHallucinationsLearningPerceptionPredictive codingSchizophrenia

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Area of Science:

  • Computational neuroscience
  • Neurobiology
  • Psychiatry

Background:

  • Increasing interest in neurocomputational mechanisms of psychosis.
  • Predictive coding and Bayesian inference as a successful approach.
  • Psychosis linked to aberrant predictive coding and altered precision of prior beliefs.

Purpose of the Study:

  • Review current evidence for aberrant predictive coding in psychosis.
  • Discuss challenges for the canonical predictive coding account.
  • Propose a nuanced framework for understanding psychosis.

Main Methods:

  • Review of existing literature on predictive coding and psychosis.
  • Analysis of evidence for altered precision in prior beliefs and sensory data.
  • Exploration of hierarchical processing and active inference in psychosis models.

Main Results:

  • Conflicting findings exist regarding prior belief strength in psychosis.
  • Hallucinations and delusions may involve distinct predictive coding alterations.
  • Nuanced predictive coding models accounting for modality-specific deficits and hierarchy are needed.

Conclusions:

  • A more complex predictive coding framework is necessary to explain psychosis.
  • Considering hierarchical organization and active inference may reconcile conflicting findings.
  • This nuanced approach can better address the diverse manifestations of psychosis.