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Theories of instrumental learning are centred on understanding how success and failure are used to improve future decisions. These theories highlight a central role for reward prediction errors in updating the values associated with available actions. In animals, substantial evidence indicates that the neurotransmitter dopamine might have a key function in this type of learning, through its ability to modulate cortico-striatal synaptic efficacy. However, no direct evidence links dopamine, striatal activity and behavioural choice in humans. Here we show that, during instrumental learning, the magnitude of reward prediction error expressed in the striatum is modulated by the administration of drugs enhancing (3,4-dihydroxy-L-phenylalanine; L-DOPA) or reducing (haloperidol) dopaminergic function. Accordingly, subjects treated with L-DOPA have a greater propensity to choose the most rewarding action relative to subjects treated with haloperidol. Furthermore, incorporating the magnitude of the prediction errors into a standard action-value learning algorithm accurately reproduced subjects' behavioural choices under the different drug conditions. We conclude that dopamine-dependent modulation of striatal activity can account for how the human brain uses reward prediction errors to improve future decisions.

Original publication

DOI

10.1038/nature05051

Type

Journal article

Journal

Nature

Publication Date

31/08/2006

Volume

442

Pages

1042 - 1045

Keywords

Adult, Algorithms, Behavior, Computer Simulation, Computers, Dopamine, Dopamine Agents, Dopamine Antagonists, Female, Forecasting, Haloperidol, Humans, Learning, Levodopa, Male, Models, Neurological, Neostriatum, Punishment, Reward