The predictive coding account of psychosis

Sterzer, P., Adams, R. A., Fletcher, P., Frith, C., Lawrie, S. M., Muckli, L. , Petrovic, P., Uhlhaas, P. , Voss, M. and Corlett, P. R. (2018) The predictive coding account of psychosis. Biological Psychiatry, 84(9), pp. 634-643. (doi: 10.1016/j.biopsych.2018.05.015) (PMID:30007575) (PMCID:PMC6169400)

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Fueled by developments in computational neuroscience, there has been increasing interest in the underlying neurocomputational mechanisms of psychosis. One successful approach involves predictive coding and Bayesian inference. Here, inferences regarding the current state of the world are made by combining prior beliefs with incoming sensory signals. Mismatches between prior beliefs and incoming signals constitute prediction errors that drive new learning. Psychosis has been suggested to result from a decreased precision in the encoding of prior beliefs relative to the sensory data, thereby garnering maladaptive inferences. Here, we review the current evidence for aberrant predictive coding and discuss challenges for this canonical predictive coding account of psychosis. For example, hallucinations and delusions may relate to distinct alterations in predictive coding, despite their common co-occurrence. More broadly, some studies implicate weakened prior beliefs in psychosis, and others find stronger priors. These challenges might be answered with a more nuanced view of predictive coding. Different priors may be specified for different sensory modalities and their integration, and deficits in each modality need not be uniform. Furthermore, hierarchical organization may be critical. Altered processes at lower levels of a hierarchy need not be linearly related to processes at higher levels (and vice versa). Finally, canonical theories do not highlight active inference-the process through which the effects of our actions on our sensations are anticipated and minimized. It is possible that conflicting findings might be reconciled by considering these complexities, portending a framework for psychosis more equipped to deal with its many manifestations.

Item Type:Articles
Additional Information:This work was supported by German Research Foundation Grant Nos. STE1430/6-2 (to PS) and STE 1430/7-1 (to PS); Academy of Medical SciencesGrant No. AMS-SGCL13-Adams (to RAA); National Institute of HealthResearch Grant No. CL-2013-18-003 (to RAA); Wellcome Trust Grant No.WT095692MA (to PF); the Bernard Wolfe Health Neuroscience Fund (to PF);European Research Council Grant No. ERC StG 2012_311751 (Brainreading of contextual feedback and predictions) (to LM); European UnionHorizon Research and Innovation Programme Grant No. 720270 (HBPSGAI)(to LM); the Swedish Research Council (to PP); the Marianne och MarcusWallenberg Foundation (to PP); the Swedish Brain Foundation (to PP); theStockholm County Council (to PP); the Karolinska Institute (to PP); Lund-beck Foundation (to PU); Lilly (to PU); the Connecticut Mental Health Centerand Connecticut State Department of Mental Health and Addiction Services(to PRC) ; National Institute of Mental Health Grant Nos. R01MH112887 (toPRC) and 5R01MH067073-09 (to PRC); International Mental Health ReviewOrder/Janssen Rising Star Translational Research Award (to PRC); NationalCenter for Research Resources and National Center for Advancing Trans-lational Science Clinical and Translational Science Award Grant No. UL1TR000142 (to PRC); the National Institutes of Health (to PRC); the NationalInstitutes of Health Roadmap for Medical Research (to PRC); and the ClinicalNeurosciences Division of the U.S. Department of Veterans Affairs, NationalCenter for Post-Traumatic Stress Disorders, Veterans Affairs ConnecticutHealthcare System (West Haven, CT).
Keywords:Bayesian brain, cognition, delusions, hallucinations, learning, perception, predictive coding, schizophrenia.
Glasgow Author(s) Enlighten ID:Uhlhaas, Professor Peter and Muckli, Professor Lars
Authors: Sterzer, P., Adams, R. A., Fletcher, P., Frith, C., Lawrie, S. M., Muckli, L., Petrovic, P., Uhlhaas, P., Voss, M., and Corlett, P. R.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Biological Psychiatry
ISSN (Online):0006-3223
Published Online:25 May 2018
Copyright Holders:Copyright © 2018 Society of Biological Psychiatry
First Published:First published in Biological Psychiatry 84(9): 634-643
Publisher Policy:Reproduced under a Creative Commons License

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