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The Predictive Coding Account of Psychosis
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 comb...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6169400/ https://www.ncbi.nlm.nih.gov/pubmed/30007575 http://dx.doi.org/10.1016/j.biopsych.2018.05.015 |
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author | Sterzer, Philipp Adams, Rick A. Fletcher, Paul Frith, Chris Lawrie, Stephen M. Muckli, Lars Petrovic, Predrag Uhlhaas, Peter Voss, Martin Corlett, Philip R. |
author_facet | Sterzer, Philipp Adams, Rick A. Fletcher, Paul Frith, Chris Lawrie, Stephen M. Muckli, Lars Petrovic, Predrag Uhlhaas, Peter Voss, Martin Corlett, Philip R. |
author_sort | Sterzer, Philipp |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6169400 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-61694002018-11-01 The Predictive Coding Account of Psychosis Sterzer, Philipp Adams, Rick A. Fletcher, Paul Frith, Chris Lawrie, Stephen M. Muckli, Lars Petrovic, Predrag Uhlhaas, Peter Voss, Martin Corlett, Philip R. Biol Psychiatry Article 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. Elsevier 2018-11-01 /pmc/articles/PMC6169400/ /pubmed/30007575 http://dx.doi.org/10.1016/j.biopsych.2018.05.015 Text en © 2018 Society of Biological Psychiatry. All rights reserved. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sterzer, Philipp Adams, Rick A. Fletcher, Paul Frith, Chris Lawrie, Stephen M. Muckli, Lars Petrovic, Predrag Uhlhaas, Peter Voss, Martin Corlett, Philip R. The Predictive Coding Account of Psychosis |
title | The Predictive Coding Account of Psychosis |
title_full | The Predictive Coding Account of Psychosis |
title_fullStr | The Predictive Coding Account of Psychosis |
title_full_unstemmed | The Predictive Coding Account of Psychosis |
title_short | The Predictive Coding Account of Psychosis |
title_sort | predictive coding account of psychosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6169400/ https://www.ncbi.nlm.nih.gov/pubmed/30007575 http://dx.doi.org/10.1016/j.biopsych.2018.05.015 |
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