Dopamine, affordance and active inference

Research output: Contribution to journalArticle

Standard

Dopamine, affordance and active inference. / Friston, Karl J; Shiner, Tamara; FitzGerald, Thomas; Galea, Joseph M; Adams, Rick; Brown, Harriet; Dolan, Raymond J; Moran, Rosalyn; Stephan, Klaas Enno; Bestmann, Sven.

In: PLoS Computational Biology, Vol. 8, No. 1, 01.2012, p. e1002327.

Research output: Contribution to journalArticle

Harvard

Friston, KJ, Shiner, T, FitzGerald, T, Galea, JM, Adams, R, Brown, H, Dolan, RJ, Moran, R, Stephan, KE & Bestmann, S 2012, 'Dopamine, affordance and active inference', PLoS Computational Biology, vol. 8, no. 1, pp. e1002327. https://doi.org/10.1371/journal.pcbi.1002327

APA

Friston, K. J., Shiner, T., FitzGerald, T., Galea, J. M., Adams, R., Brown, H., Dolan, R. J., Moran, R., Stephan, K. E., & Bestmann, S. (2012). Dopamine, affordance and active inference. PLoS Computational Biology, 8(1), e1002327. https://doi.org/10.1371/journal.pcbi.1002327

Vancouver

Friston KJ, Shiner T, FitzGerald T, Galea JM, Adams R, Brown H et al. Dopamine, affordance and active inference. PLoS Computational Biology. 2012 Jan;8(1):e1002327. https://doi.org/10.1371/journal.pcbi.1002327

Author

Friston, Karl J ; Shiner, Tamara ; FitzGerald, Thomas ; Galea, Joseph M ; Adams, Rick ; Brown, Harriet ; Dolan, Raymond J ; Moran, Rosalyn ; Stephan, Klaas Enno ; Bestmann, Sven. / Dopamine, affordance and active inference. In: PLoS Computational Biology. 2012 ; Vol. 8, No. 1. pp. e1002327.

Bibtex

@article{0499b312f02e49ed8db54dcb335f092f,
title = "Dopamine, affordance and active inference",
abstract = "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.",
keywords = "Brain, Computer Simulation, Cues, Decision Making, Dopamine, Humans, Models, Neurological, Nerve Net, Perceptual Masking, Synaptic Transmission",
author = "Friston, {Karl J} and Tamara Shiner and Thomas FitzGerald and Galea, {Joseph M} and Rick Adams and Harriet Brown and Dolan, {Raymond J} and Rosalyn Moran and Stephan, {Klaas Enno} and Sven Bestmann",
note = "{\textcopyright} 2012 Friston et al.",
year = "2012",
month = jan
doi = "10.1371/journal.pcbi.1002327",
language = "English",
volume = "8",
pages = "e1002327",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science (PLOS)",
number = "1",

}

RIS

TY - JOUR

T1 - Dopamine, affordance and active inference

AU - Friston, Karl J

AU - Shiner, Tamara

AU - FitzGerald, Thomas

AU - Galea, Joseph M

AU - Adams, Rick

AU - Brown, Harriet

AU - Dolan, Raymond J

AU - Moran, Rosalyn

AU - Stephan, Klaas Enno

AU - Bestmann, Sven

N1 - © 2012 Friston et al.

PY - 2012/1

Y1 - 2012/1

N2 - 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.

AB - 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.

KW - Brain

KW - Computer Simulation

KW - Cues

KW - Decision Making

KW - Dopamine

KW - Humans

KW - Models, Neurological

KW - Nerve Net

KW - Perceptual Masking

KW - Synaptic Transmission

U2 - 10.1371/journal.pcbi.1002327

DO - 10.1371/journal.pcbi.1002327

M3 - Article

C2 - 22241972

VL - 8

SP - e1002327

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 1

ER -