Computational psychiatry offers a novel perspective on understanding the pathophysiology of psychosis. This review focuses on the predictive processing framework, one of the most influential computational theories of the brain, to explore the abnormalities in prediction, prediction error, and prediction precision that underlie psychotic symptoms. Hallucinations and delusions, key features of psychosis, are hypothesized to result from hierarchical interactions among these factors. Using a neuro‐robotics experimental framework, studies have empirically examined how disruptions in prediction, prediction error, and precision can contribute to the formation of psychotic symptoms. These experiments replicated behaviors and experiences analogous to schizophrenia, providing insights into the mechanisms driving the disorder. Furthermore, this review highlights the importance of biological non‐specificity(multifinality)and heterogeneity(equifinality)in psychosis pathophysiology. Non‐specificity refers to the phenomenon where the same biological factor leads to distinct symptoms, while heterogeneity describes how different biological factors produce similar symptoms. These concepts underscore the necessity of moving beyond a single causal pathway to consider multiple interacting mechanisms. Understanding these dynamics may pave the way for integrative approaches to the diagnosis and treatment of psychosis, contributing to a more unified and comprehensive understanding of its pathophysiological basis.
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