A computational substrate for incentive salience

Samuel M. McClure, Nathaniel D. Daw, P. Read Montague

Research output: Contribution to journalArticle

Abstract

Theories of dopamine function are at a crossroads. Computational models derived from single-unit recordings capture changes in dopaminergic neuron firing rate as a prediction error signal. These models employ the prediction error signal in two roles: learning to predict future rewarding events and biasing action choice. Conversely, pharmacological inhibition or lesion of dopaminergic neuron function diminishes the ability of an animal to motivate behaviors directed at acquiring rewards. These lesion experiments have raised the possibility that dopamine release encodes a measure of the incentive value of a contemplated behavioral act. The most complete psychological idea that captures this notion frames the dopamine signal as carrying 'incentive salience'. On the surface, these two competing accounts of dopamine function seem incommensurate. To the contrary, we demonstrate that both of these functions can be captured in a single computational model of the involvement of dopamine in reward prediction for the purpose of reward seeking.

Original languageEnglish (US)
Pages (from-to)423-428
Number of pages6
JournalTrends in Neurosciences
Volume26
Issue number8
DOIs
StatePublished - Aug 1 2003

Fingerprint

Motivation
Dopamine
Reward
Dopaminergic Neurons
Aptitude
Learning
Pharmacology
Psychology

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

A computational substrate for incentive salience. / McClure, Samuel M.; Daw, Nathaniel D.; Read Montague, P.

In: Trends in Neurosciences, Vol. 26, No. 8, 01.08.2003, p. 423-428.

Research output: Contribution to journalArticle

McClure, SM, Daw, ND & Read Montague, P 2003, 'A computational substrate for incentive salience', Trends in Neurosciences, vol. 26, no. 8, pp. 423-428. https://doi.org/10.1016/S0166-2236(03)00177-2
McClure, Samuel M. ; Daw, Nathaniel D. ; Read Montague, P. / A computational substrate for incentive salience. In: Trends in Neurosciences. 2003 ; Vol. 26, No. 8. pp. 423-428.
@article{867af8a4160c4a879cb93e34ccad17e3,
title = "A computational substrate for incentive salience",
abstract = "Theories of dopamine function are at a crossroads. Computational models derived from single-unit recordings capture changes in dopaminergic neuron firing rate as a prediction error signal. These models employ the prediction error signal in two roles: learning to predict future rewarding events and biasing action choice. Conversely, pharmacological inhibition or lesion of dopaminergic neuron function diminishes the ability of an animal to motivate behaviors directed at acquiring rewards. These lesion experiments have raised the possibility that dopamine release encodes a measure of the incentive value of a contemplated behavioral act. The most complete psychological idea that captures this notion frames the dopamine signal as carrying 'incentive salience'. On the surface, these two competing accounts of dopamine function seem incommensurate. To the contrary, we demonstrate that both of these functions can be captured in a single computational model of the involvement of dopamine in reward prediction for the purpose of reward seeking.",
author = "McClure, {Samuel M.} and Daw, {Nathaniel D.} and {Read Montague}, P.",
year = "2003",
month = "8",
day = "1",
doi = "10.1016/S0166-2236(03)00177-2",
language = "English (US)",
volume = "26",
pages = "423--428",
journal = "Trends in Neurosciences",
issn = "0378-5912",
publisher = "Elsevier Limited",
number = "8",

}

TY - JOUR

T1 - A computational substrate for incentive salience

AU - McClure, Samuel M.

AU - Daw, Nathaniel D.

AU - Read Montague, P.

PY - 2003/8/1

Y1 - 2003/8/1

N2 - Theories of dopamine function are at a crossroads. Computational models derived from single-unit recordings capture changes in dopaminergic neuron firing rate as a prediction error signal. These models employ the prediction error signal in two roles: learning to predict future rewarding events and biasing action choice. Conversely, pharmacological inhibition or lesion of dopaminergic neuron function diminishes the ability of an animal to motivate behaviors directed at acquiring rewards. These lesion experiments have raised the possibility that dopamine release encodes a measure of the incentive value of a contemplated behavioral act. The most complete psychological idea that captures this notion frames the dopamine signal as carrying 'incentive salience'. On the surface, these two competing accounts of dopamine function seem incommensurate. To the contrary, we demonstrate that both of these functions can be captured in a single computational model of the involvement of dopamine in reward prediction for the purpose of reward seeking.

AB - Theories of dopamine function are at a crossroads. Computational models derived from single-unit recordings capture changes in dopaminergic neuron firing rate as a prediction error signal. These models employ the prediction error signal in two roles: learning to predict future rewarding events and biasing action choice. Conversely, pharmacological inhibition or lesion of dopaminergic neuron function diminishes the ability of an animal to motivate behaviors directed at acquiring rewards. These lesion experiments have raised the possibility that dopamine release encodes a measure of the incentive value of a contemplated behavioral act. The most complete psychological idea that captures this notion frames the dopamine signal as carrying 'incentive salience'. On the surface, these two competing accounts of dopamine function seem incommensurate. To the contrary, we demonstrate that both of these functions can be captured in a single computational model of the involvement of dopamine in reward prediction for the purpose of reward seeking.

UR - http://www.scopus.com/inward/record.url?scp=0142058800&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0142058800&partnerID=8YFLogxK

U2 - 10.1016/S0166-2236(03)00177-2

DO - 10.1016/S0166-2236(03)00177-2

M3 - Article

C2 - 12900173

AN - SCOPUS:0142058800

VL - 26

SP - 423

EP - 428

JO - Trends in Neurosciences

JF - Trends in Neurosciences

SN - 0378-5912

IS - 8

ER -