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

Abstract

Attempts to understand the unshared reality of psychosis 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. Recently, it has been argued 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 furnishes us with a richer understanding of psychosis. 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, thus providing a potentially powerful new framework for computational psychiatric approaches to psychosis. We also make suggestions for future work that will be important in formalising this novel perspective.