title: Dopamine selectively remediates 'model-based' reward learning: a computational approach.
abstract: Patients with loss of dopamine due to Parkinson's disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from 'model-free' learning. The other, 'model-based' learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for dopamine in model-free learning. But recent work suggests model-based learning may also involve dopamine modulation, raising the possibility that model-based learning may contribute to the learning impairment in Parkinson's disease. To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in patients with Parkinson's disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson's disease may be related to an inability to pursue reward based on complete representations of the environment.
authors.affiliation: 1 Department of Neurology, Columbia University Medical Centre, New York NY, USA.
authors.forename: Madeleine E
authors.initials: ME
authors.lastname: Sharp
authors.affiliation: 2 Department of Psychology, New York University, New York NY, USA.
authors.forename: Karin
authors.initials: K
authors.lastname: Foerde
authors.affiliation: 3 Center for Neural Science, New York University, New York NY, USA.
authors.forename: Nathaniel D
authors.initials: ND
authors.lastname: Daw
authors.affiliation: 4 Department of Psychology and Kavli Center for Brain Sciences, Columbia University, New York NY, USA shohamy@psych.columbia.edu.
authors.forename: Daphna
authors.initials: D
authors.lastname: Shohamy
mesh_terms: D000368:Aged; D004298:Dopamine / Q000502:physiology*; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000175:diagnosis / Q000523:psychology*; D066230:Patient-Specific Modeling; D010775:Photic Stimulation / Q000379:methods; D011597:Psychomotor Performance / Q000502:physiology*; D012201:Reward
chemical_list: D004298:Dopamine
keywords: Parkinson’s disease; dopamine; model-based; model-free; reinforcement learning