Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 104, Issue 41, Pages 16311-16316Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.0706111104
Keywords
basal ganglia; prefrontal cortex; computational model
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Funding
- NIAAA NIH HHS [R29 AA012238, AA012238, R01 AA012238] Funding Source: Medline
- NIDA NIH HHS [DA022630, R21 DA022630] Funding Source: Medline
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What are the genetic and neural components that support adaptive learning from positive and negative outcomes? Here, we show with genetic analyses that three independent dopaminergic mechanisms contribute to reward and avoidance learning in humans. A polymorphism in the DARPP-32 gene, associated with striatal dopamine function, predicted relatively better probabilistic reward learning. Conversely, the C957T polymorphism of the DRD2 gene, associated with striatal D2 receptor function, predicted the degree to which participants learned to avoid choices that had been probabilistically associated with negative outcomes. The Val/Met polymorphism of the COMT gene, associated with prefrontal cortical dopamine function, predicted participants' ability to rapidly adapt behavior on a trial-to-trial basis. These findings support a neurocomputational dissociation between striatal and prefrontal dopaminergic mechanisms in reinforcement learning. Computational maximum likelihood analyses reveal independent gene effects on three reinforcement learning parameters that can explain the observed dissociations.
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