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 synthesises advances in computational psychiatry, proposing that mental disorders can be conceptualised as specific alterations in the brain’s predictive inference machinery. We first outline the theoretical foundations of predictive coding, including Bayesian inference, free-energy minimisation, and neural population dynamics, showing how these abstract computational principles map onto specific neural circuits and bio- physical mechanisms. We then demonstrate how diverse psychiatric conditions can be understood within this unified framework. Critically, this additionally provides a basis upon which predictive coding becomes a testable, modifiable, falsifiable construct within biological psychiatry.Beyond offering conceptual clarity, this framework has significant clinical implications, including the development of mechanistic biomarkers, personalised treatment approaches based on computational phenotypes, and novel therapeutic interventions targeting specific inferential ab- normalities. By grounding psychiatric symptoms in aberrant predictive processes implemented in neural circuitry, this approach promises a more mechanistic understanding of mental disorders and a path toward more targeted, effective interventions.