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Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning.

ABSTRACT: 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.

SUBMITTER: Frank MJ

PROVIDER: S-EPMC2042203 | biostudies-literature | 2007 Oct

REPOSITORIES: biostudies-literature

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Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning.

Frank Michael J MJ Moustafa Ahmed A AA Haughey Heather M HM Curran Tim T Hutchison Kent E KE

Proceedings of the National Academy of Sciences of the United States of America 20071003 41

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 t ...[more]

PMID: 17913879

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Project description:Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning.Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes. Research implicates a dopamine-dependent striatal learning mechanism in the former type of choice. Although recent work has indicated that dopamine is also involved in flexible, goal-directed decision-making, it remains unclear whether it also contributes via striatum or via the dopamine-dependent working memory function of prefrontal cortex. We examined genetic indices of dopamine function in these regions and their relation to the two choice strategies. We found that striatal dopamine function related most clearly to the reflexive strategy, as previously shown, and that prefrontal dopamine related most clearly to the flexible strategy. These findings suggest that dissociable brain regions support dissociable choice strategies.

Project description:Parkinson's disease is clinically defined by bradykinesia, along with rigidity and tremor. However, the severity of these motor signs is greatly variable between individuals, particularly the presence or absence of tremor. This variability in tremor relates to variation in cognitive/motivational impairment, as well as the spatial distribution of neurodegeneration in the midbrain and dopamine depletion in the striatum. Here we ask whether interindividual heterogeneity in tremor symptoms could account for the puzzlingly large variability in the effects of dopaminergic medication on reinforcement learning, a fundamental cognitive function known to rely on dopamine. Given that tremor-dominant and non-tremor Parkinson's disease patients have different dopaminergic phenotypes, we hypothesized that effects of dopaminergic medication on reinforcement learning differ between tremor-dominant and non-tremor patients. Forty-three tremor-dominant and 20 non-tremor patients with Parkinson's disease were recruited to be tested both OFF and ON dopaminergic medication (200/50 mg levodopa-benserazide), while 22 age-matched control subjects were recruited to be tested twice OFF medication. Participants performed a reinforcement learning task designed to dissociate effects on learning rate from effects on motivational choice (i.e. the tendency to 'Go/NoGo' in the face of reward/threat of punishment). In non-tremor patients, dopaminergic medication improved reward-based choice, replicating previous studies. In contrast, in tremor-dominant patients, dopaminergic medication improved learning from punishment. Formal modelling showed divergent computational effects of dopaminergic medication as a function of Parkinson's disease motor phenotype, with a modulation of motivational choice bias and learning rate in non-tremor and tremor patients, respectively. This finding establishes a novel cognitive/motivational difference between tremor and non-tremor Parkinson's disease patients, and highlights the importance of considering motor phenotype in future work.

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Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning.

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Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning.

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