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

Area of Science:

• Computational neuroscience
• Psychiatry
• Cognitive science

Background:

• Predictive coding provides a computational framework for brain function.
• Mental disorders are increasingly studied through computational psychiatry.

Purpose of the Study:

• To synthesize advances in computational psychiatry using predictive coding.
• To propose mental disorders as alterations in the brain's predictive inference machinery.
• To explore the clinical implications of this framework.

Main Methods:

• Outlining theoretical foundations of predictive coding (Bayesian inference, free-energy minimization, neural dynamics).
• Mapping computational principles to neural circuits and biophysical mechanisms.
• Integrating theory, generative models, and empirical data.

Main Results:

• Diverse psychiatric conditions can be understood within the predictive coding framework.
• Predictive coding can be a testable, modifiable, and falsifiable construct in biological psychiatry.

Conclusions:

• The predictive coding framework offers conceptual clarity for mental disorders.
• This approach has significant clinical implications, including mechanistic biomarkers and personalized treatments.
• Grounding psychiatric symptoms in aberrant predictive processes promises more targeted interventions.