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

Jessica Niamh Harding*, Noham Wolpe, Stefan Peter Brugger, Victor Navarro, Christoph Teufel, Paul Charles Fletcher

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations


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.

Original languageEnglish
Pages (from-to)295-302
Number of pages8
JournalThe Lancet Psychiatry
Issue number4
StatePublished - Apr 2024


FundersFunder number
Bernard Wolfe Health Neuroscience Fund
Wellcome Trust206368/Z/17/Z
Wellcome Trust
UK Research and InnovationMR/N0137941/1, EP/Y026489/1
UK Research and Innovation
Medical Research Council
National Institute for Health and Care Research
Israel Science Foundation1603/22
Israel Science Foundation
UCLH Biomedical Research CentreNIHR203312
UCLH Biomedical Research Centre


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