Dopamine, affordance and active inference.

The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or sal...

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Autores principales:	Karl J Friston, Tamara Shiner, Thomas FitzGerald, Joseph M Galea, Rick Adams, Harriet Brown, Raymond J Dolan, Rosalyn Moran, Klaas Enno Stephan, Sven Bestmann
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/72eba6ac47eb408a93308fbc4b1e1213
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spelling	oai:doaj.org-article:72eba6ac47eb408a93308fbc4b1e12132021-11-18T05:51:40ZDopamine, affordance and active inference.1553-734X1553-735810.1371/journal.pcbi.1002327https://doaj.org/article/72eba6ac47eb408a93308fbc4b1e12132012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22241972/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level.Karl J FristonTamara ShinerThomas FitzGeraldJoseph M GaleaRick AdamsHarriet BrownRaymond J DolanRosalyn MoranKlaas Enno StephanSven BestmannPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 1, p e1002327 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Karl J Friston Tamara Shiner Thomas FitzGerald Joseph M Galea Rick Adams Harriet Brown Raymond J Dolan Rosalyn Moran Klaas Enno Stephan Sven Bestmann Dopamine, affordance and active inference.
description	The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level.
format	article
author	Karl J Friston Tamara Shiner Thomas FitzGerald Joseph M Galea Rick Adams Harriet Brown Raymond J Dolan Rosalyn Moran Klaas Enno Stephan Sven Bestmann
author_facet	Karl J Friston Tamara Shiner Thomas FitzGerald Joseph M Galea Rick Adams Harriet Brown Raymond J Dolan Rosalyn Moran Klaas Enno Stephan Sven Bestmann
author_sort	Karl J Friston
title	Dopamine, affordance and active inference.
title_short	Dopamine, affordance and active inference.
title_full	Dopamine, affordance and active inference.
title_fullStr	Dopamine, affordance and active inference.
title_full_unstemmed	Dopamine, affordance and active inference.
title_sort	dopamine, affordance and active inference.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/72eba6ac47eb408a93308fbc4b1e1213
work_keys_str_mv	AT karljfriston dopamineaffordanceandactiveinference AT tamarashiner dopamineaffordanceandactiveinference AT thomasfitzgerald dopamineaffordanceandactiveinference AT josephmgalea dopamineaffordanceandactiveinference AT rickadams dopamineaffordanceandactiveinference AT harrietbrown dopamineaffordanceandactiveinference AT raymondjdolan dopamineaffordanceandactiveinference AT rosalynmoran dopamineaffordanceandactiveinference AT klaasennostephan dopamineaffordanceandactiveinference AT svenbestmann dopamineaffordanceandactiveinference
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Dopamine, affordance and active inference.

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