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.
authors.affiliation: Center for Theoretical Neuroscience, Human Neuroimaging Laboratory, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA.
authors.forename: Samuel M
authors.initials: SM
authors.lastname: McClure
authors.forename: Nathaniel D
authors.initials: ND
authors.lastname: Daw
authors.forename: P Read
authors.initials: PR
authors.lastname: Montague
mesh_terms: D000465:Algorithms; D000818:Animals; D001923:Brain Chemistry / Q000502:physiology*; D002755:Choice Behavior / Q000502:physiology; D019295:Computational Biology; D004298:Dopamine / Q000502:physiology*; D005544:Forecasting; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D008960:Models, Psychological; D009042:Motivation; D009473:Neuronal Plasticity / Q000502:physiology; D051381:Rats; D012201:Reward
chemical_list: D004298:Dopamine