title: Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans.
abstract: 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.
authors.affiliation: Wellcome Department of Imaging Neuroscience, 12 Queen Square, London WC1N 3BG, UK. m.pessiglione@fil.ion.ucl.ac.uk
authors.forename: Mathias
authors.initials: M
authors.lastname: Pessiglione
authors.forename: Ben
authors.initials: B
authors.lastname: Seymour
authors.forename: Guillaume
authors.initials: G
authors.lastname: Flandin
authors.forename: Raymond J
authors.initials: RJ
authors.lastname: Dolan
authors.forename: Chris D
authors.initials: CD
authors.lastname: Frith
mesh_terms: D000328:Adult; D000465:Algorithms; D001519:Behavior / Q000187:drug effects / Q000502:physiology*; D003198:Computer Simulation; D003201:Computers; D004298:Dopamine / Q000378:metabolism*; D015259:Dopamine Agents / Q000494:pharmacology; D018492:Dopamine Antagonists / Q000494:pharmacology; D005260:Female; D005544:Forecasting; D006220:Haloperidol / Q000494:pharmacology; D006801:Humans; D007858:Learning / Q000187:drug effects / Q000502:physiology*; D007980:Levodopa / Q000494:pharmacology; D008297:Male; D008959:Models, Neurological; D017072:Neostriatum / Q000187:drug effects / Q000502:physiology; D011678:Punishment; D012201:Reward
chemical_list: D015259:Dopamine Agents; D018492:Dopamine Antagonists; D007980:Levodopa; D006220:Haloperidol; D004298:Dopamine