A new predictive coding model for a more comprehensive account of delusions.

Summary

A modified version of predictive coding called hybrid predictive coding offers a more comprehensive model of how healthy agents infer reality than standard approaches. This framework provides a richer understanding of psychosis, particularly the phenomenology of delusions. The authors describe the hybrid model and suggest it could become a powerful new framework for computational psychiatry, offering suggestions for future formalization of this perspective.

Study at a glance

Abstract

Attempts to understand psychosis-the experience of profoundly altered perceptions and beliefs-raise questions about how the brain models the world. Standard predictive coding approaches suggest that it does so by minimising mismatches between incoming sensory evidence and predictions. By adjusting predictions, we converge iteratively on a best guess of the nature of the reality. Recent arguments have shown that a modified version of this framework-hybrid predictive coding-provides a better model of how healthy agents make inferences about external reality. We suggest that this more comprehensive model gives us a richer understanding of psychosis compared with standard predictive coding accounts. In this Personal View, we briefly describe the hybrid predictive coding model and show how it offers a more comprehensive account of the phenomenology of delusions, thereby providing a potentially powerful new framework for computational psychiatric approaches to psychosis. We also make suggestions for future work that could be important in formalising this novel perspective.