期刊
PSYCHOLOGICAL REVIEW
卷 121, 期 3, 页码 337-366出版社
AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/a0037015
关键词
dopamine; striatum; reinforcement learning; choice incentive; computational model
资金
- National Science Foundation [1125788]
- National Institute of Mental Health [R01MH080066-01]
- Division Of Behavioral and Cognitive Sci
- Direct For Social, Behav & Economic Scie [1125788] Funding Source: National Science Foundation
The striatal dopaminergic system has been implicated in reinforcement learning (RL), motor performance, and incentive motivation. Various computational models have been proposed to account for each of these effects individually, but a formal analysis of their interactions is lacking. Here we present a novel algorithmic model expanding the classical actor-critic architecture to include fundamental interactive properties of neural circuit models, incorporating both incentive and learning effects into a single theoretical framework. The standard actor is replaced by a dual opponent actor system representing distinct striatal populations, which come to differentially specialize in discriminating positive and negative action values. Dopamine modulates the degree to which each actor component contributes to both learning and choice discriminations. In contrast to standard frameworks, this model simultaneously captures documented effects of dopamine on both learning and choice incentive-and their interactions-across a variety of studies, including probabilistic RL, effort-based choice, and motor skill learning.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据