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...
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| Vydáno v: | Biological psychiatry (1969) Ročník 84; číslo 9; s. 634 - 643 |
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| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
Elsevier Inc
01.11.2018
Elsevier |
| Témata: | |
| ISSN: | 0006-3223, 1873-2402, 1873-2402 |
| On-line přístup: | Získat plný text |
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| 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. |
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| AbstractList | 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.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. 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. |
| Author | Fletcher, Paul Uhlhaas, Peter Adams, Rick A. Sterzer, Philipp Petrovic, Predrag Frith, Chris Voss, Martin Lawrie, Stephen M. Muckli, Lars Corlett, Philip R. |
| AuthorAffiliation | a Department of Psychiatry, Campus Charité Mitte, Charité – Universitätsmedizin Berlin, Berlin, Germany i Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden e Department of Psychiatry, Addenbrooke’s Hospital, University of Cambridge, Cambridge, United Kingdom d Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom b Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig Hospital, Berlin Center for Advanced Neuroimaging, Humboldt University Berlin, Berlin, Germany g Center for Clinical and Brain Sciences, Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, United Kingdom j Department of Psychiatry, Yale University, New Haven, Connecticut f Wellcome-MRC Behavioral and Clinical Neuroscience Institute, Cambridge and Peterborough Foundation Trust, Cambridge, United Kingdom c Division of Psychiatry, University College London, London, United Kingdom h Centre for Cognitive Ne |
| AuthorAffiliation_xml | – name: h Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom – name: j Department of Psychiatry, Yale University, New Haven, Connecticut – name: c Division of Psychiatry, University College London, London, United Kingdom – name: a Department of Psychiatry, Campus Charité Mitte, Charité – Universitätsmedizin Berlin, Berlin, Germany – name: f Wellcome-MRC Behavioral and Clinical Neuroscience Institute, Cambridge and Peterborough Foundation Trust, Cambridge, United Kingdom – name: d Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom – name: e Department of Psychiatry, Addenbrooke’s Hospital, University of Cambridge, Cambridge, United Kingdom – name: b Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig Hospital, Berlin Center for Advanced Neuroimaging, Humboldt University Berlin, Berlin, Germany – name: g Center for Clinical and Brain Sciences, Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, United Kingdom – name: i Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden |
| Author_xml | – sequence: 1 givenname: Philipp surname: Sterzer fullname: Sterzer, Philipp organization: Department of Psychiatry, Campus Charité Mitte, Charité – Universitätsmedizin Berlin, Berlin, Germany – sequence: 2 givenname: Rick A. surname: Adams fullname: Adams, Rick A. organization: Division of Psychiatry, University College London, London, United Kingdom – sequence: 3 givenname: Paul orcidid: 0000-0001-8257-1517 surname: Fletcher fullname: Fletcher, Paul organization: Department of Psychiatry, Addenbrooke’s Hospital, University of Cambridge, Cambridge, United Kingdom – sequence: 4 givenname: Chris orcidid: 0000-0002-8665-0690 surname: Frith fullname: Frith, Chris organization: Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom – sequence: 5 givenname: Stephen M. surname: Lawrie fullname: Lawrie, Stephen M. organization: Center for Clinical and Brain Sciences, Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, United Kingdom – sequence: 6 givenname: Lars orcidid: 0000-0002-0143-4324 surname: Muckli fullname: Muckli, Lars organization: Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom – sequence: 7 givenname: Predrag surname: Petrovic fullname: Petrovic, Predrag organization: Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden – sequence: 8 givenname: Peter surname: Uhlhaas fullname: Uhlhaas, Peter organization: Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom – sequence: 9 givenname: Martin surname: Voss fullname: Voss, Martin organization: Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig Hospital, Berlin Center for Advanced Neuroimaging, Humboldt University Berlin, Berlin, Germany – sequence: 10 givenname: Philip R. surname: Corlett fullname: Corlett, Philip R. email: philip.corlett@yale.edu organization: Department of Psychiatry, Yale University, New Haven, Connecticut |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30007575$$D View this record in MEDLINE/PubMed http://kipublications.ki.se/Default.aspx?queryparsed=id:139383369$$DView record from Swedish Publication Index (Karolinska Institutet) |
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| PublicationYear | 2018 |
| Publisher | Elsevier Inc Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier |
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| SubjectTerms | Bayes Theorem Bayesian brain Cognition Delusions Delusions - physiopathology Hallucinations Hallucinations - physiopathology Humans Learning Perception Predictive coding Psychotic Disorders - physiopathology Schizophrenia |
| Title | The Predictive Coding Account of Psychosis |
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