Model averaging, optimal inference, and habit formation
Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and partic...
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| Published in | Frontiers in human neuroscience Vol. 8; p. 457 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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26.06.2014
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| Abstract | Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge-that of determining which model or models of their environment are the best for guiding behavior. Bayesian model averaging-which says that an agent should weight the predictions of different models according to their evidence-provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent's behavior should show an equivalent balance. We hypothesize that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realizable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behavior. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded) Bayesian inference, focusing particularly upon the relationship between goal-directed and habitual behavior. |
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| AbstractList | Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function—the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge—that of determining which model or models of their environment are the best for guiding behavior. Bayesian model averaging—which says that an agent should weight the predictions of different models according to their evidence—provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent's behavior should show an equivalent balance. We hypothesize that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realizable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behavior. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded) Bayesian inference, focusing particularly upon the relationship between goal-directed and habitual behavior. Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function – the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge – that of determining which model or models of their environment are the best for guiding behaviour. Bayesian model averaging – which says that an agent should weight the predictions of different models according to their evidence – provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent’s behaviour should show an equivalent balance. We hypothesise that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realisable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behaviour. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded) Bayesian inference, focussing particularly upon the relationship between goal-directed and habitual behaviour. Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge-that of determining which model or models of their environment are the best for guiding behavior. Bayesian model averaging-which says that an agent should weight the predictions of different models according to their evidence-provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent's behavior should show an equivalent balance. We hypothesize that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realizable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behavior. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded) Bayesian inference, focusing particularly upon the relationship between goal-directed and habitual behavior.Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge-that of determining which model or models of their environment are the best for guiding behavior. Bayesian model averaging-which says that an agent should weight the predictions of different models according to their evidence-provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent's behavior should show an equivalent balance. We hypothesize that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realizable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behavior. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded) Bayesian inference, focusing particularly upon the relationship between goal-directed and habitual behavior. |
| Author | Dolan, Raymond J. FitzGerald, Thomas H. B. Friston, Karl J. |
| AuthorAffiliation | Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK |
| AuthorAffiliation_xml | – name: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK |
| Author_xml | – sequence: 1 givenname: Thomas H. B. surname: FitzGerald fullname: FitzGerald, Thomas H. B. – sequence: 2 givenname: Raymond J. surname: Dolan fullname: Dolan, Raymond J. – sequence: 3 givenname: Karl J. surname: Friston fullname: Friston, Karl J. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25018724$$D View this record in MEDLINE/PubMed |
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| Copyright | 2014. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2014 FitzGerald, Dolan and Friston. 2014 |
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| Keywords | habit interference effect active inference Bayesian inference predictive coding |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Edited by: Javier Bernacer, University of Navarra, Spain Reviewed by: Vincent De Gardelle, Université Paris Descartes, France; Samuel Joseph Gershman, Princeton University, USA This article was submitted to the journal Frontiers in Human Neuroscience. |
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| Snippet | Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of... Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function – the subject of... Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function—the subject of... |
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| StartPage | 457 |
| SubjectTerms | Accuracy active inference Bayesian analysis Bayesian inference bistable perception bounded rationality Cognition habit Mathematical models Nervous system Neuroscience predictive coding Trends |
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| Title | Model averaging, optimal inference, and habit formation |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/25018724 https://www.proquest.com/docview/2292132836 https://www.proquest.com/docview/1545190338 https://pubmed.ncbi.nlm.nih.gov/PMC4071291 https://doaj.org/article/fb6b18a12efe40dcbef87ea8edfbb752 |
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