Predictive Processing, Source Monitoring, and Psychosis.

Area of Science:

• Neuroscience
• Cognitive Science
• Psychiatry

Background:

• Understanding psychosis requires integrating neurobiological, cognitive, subjective, and social factors.
• Gaps exist in explaining how brain changes cause psychosis symptoms or how adversity affects brain processes.

Purpose of the Study:

• To propose the predictive processing framework as a unifying model for psychosis.
• To demonstrate how predictive processing can explain delusions and hallucinations.
• To model key clinical features of psychosis using a hierarchical predictive system.

Main Methods:

• Conceptual integration of predictive processing with source monitoring theories.
• Application of predictive processing to explain psychosis phenomena.
• Modeling psychosis through a dynamic, hierarchical predictive system.

Main Results:

• Predictive processing provides a framework to bridge explanatory gaps in psychosis.
• The framework complements source monitoring theories of delusions and hallucinations.
• A hierarchical predictive system models key clinical features of psychosis.

Conclusions:

• The predictive processing framework offers a robust model for understanding psychosis.
• It integrates diverse levels of explanation, from neurobiology to subjective experience.
• It reconciles and deepens understanding offered by source monitoring theories.