Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders

Predictive coding offers a powerful computational framework for understanding brain function and psychiatric disorders at a mechanistic level. This perspective synthesizes advances in computational psychiatry, proposing that mental disorders can be conceptualized as specific alterations in the brain's predictive inference machinery. We first outline the theoretical foundations of predictive coding, including Bayesian inference, free-energy minimization, and neural population dynamics, illustrating how these abstract computational principles map onto specific neural circuits and biophysical mechanisms. We then argue that diverse psychiatric conditions can be understood within this unified framework. Taken…

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