Abstract

We will use schizophrenia as a case study of computational psychiatry We first review the basic phenomenology and pathophysiological theories of schizophrenia These motivate the choice of a formal or computational framework within which to understand the symptoms and signs of schizophrenia This framework is the Bayesian brain or Bayesian decision theory We will focus on the encoding of uncertainty or precision within predictive coding implementations of the Bayesian brain to demonstrate how computational approaches can disclose the nature of hallucinations and delusions

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