Frank Michael J, Moustafa Ahmed A, Haughey Heather M, Curran Tim, Hutchison Kent E
Department of Psychology and Program in Neuroscience, University of Arizona, Tucson, AZ 85721, USA.
Proc Natl Acad Sci U S A. 2007 Oct 9;104(41):16311-6. doi: 10.1073/pnas.0706111104. Epub 2007 Oct 3.
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.
支持从正面和负面结果中进行适应性学习的遗传和神经成分是什么?在此,我们通过基因分析表明,三种独立的多巴胺能机制有助于人类的奖励和回避学习。与纹状体多巴胺功能相关的DARPP - 32基因多态性预测了相对更好的概率性奖励学习。相反,与纹状体D2受体功能相关的DRD2基因的C957T多态性预测了参与者学会避免与负面结果有概率关联的选择的程度。与前额叶皮质多巴胺功能相关的COMT基因的Val/Met多态性预测了参与者在逐次试验基础上快速调整行为的能力。这些发现支持了强化学习中纹状体和前额叶多巴胺能机制之间的神经计算分离。计算最大似然分析揭示了独立基因对三个强化学习参数的影响,这可以解释观察到的分离现象。