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Biol Psychiatry. 2018 Nov 1;84(9):634-643. doi: 10.1016/j.biopsych.2018.05.015. Epub 2018 May 25.

The Predictive Coding Account of Psychosis.

Author information

1
Department of Psychiatry, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany.
2
Division of Psychiatry, University College London, London, United Kingdom.
3
Department of Psychiatry, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom; Wellcome-MRC Behavioral and Clinical Neuroscience Institute, Cambridge and Peterborough Foundation Trust, Cambridge, United Kingdom.
4
Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.
5
Center for Clinical and Brain Sciences, Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, United Kingdom.
6
Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom.
7
Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
8
Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig Hospital, Berlin Center for Advanced Neuroimaging, Humboldt University Berlin, Berlin, Germany.
9
Department of Psychiatry, Yale University, New Haven, Connecticut. Electronic address: philip.corlett@yale.edu.

Abstract

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.

KEYWORDS:

Bayesian brain; Cognition; Delusions; Hallucinations; Learning; Perception; Predictive coding; Schizophrenia

PMID:
30007575
PMCID:
PMC6169400
DOI:
10.1016/j.biopsych.2018.05.015
[Indexed for MEDLINE]
Free PMC Article

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