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Generalization of value in reinforcement learning by humans

机译:人类加强学习价值的概括

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摘要

Research in decision making has focused on the role of dopamine and its striatal targets in guiding choices via learned stimulus-reward or stimulus-response associations, behavior that is well-described by reinforcement learning (RL) theories. However, basic RL is relatively limited in scope and does not explain how learning about stimulus regularities or relations may guide decision making. A candidate mechanism for this type of learning comes from the domain of memory, which has highlighted a role for the hippocampus in learning of stimulus-stimulus relations, typically dissociated from the role of the striatum in stimulus-response learning. Here, we used fMRI and computational model-based analyses to examine the joint contributions of these mechanisms to RL. Humans performed an RL task with added relational structure, modeled after tasks used to isolate hippocampal contributions to memory. On each trial participants chose one of four options, but the reward probabilities for pairs of options were correlated across trials. This (uninstructed) relationship between pairs of options potentially enabled an observer to learn about options’ values based on experience with the other options and to generalize across them. We observed BOLD activity related to learning in the striatum and also in the hippocampus. By comparing a basic RL model to one augmented to allow feedback to generalize between correlated options, we tested whether choice behavior and BOLD activity were influenced by the opportunity to generalize across correlated options. Although such generalization goes beyond standard computational accounts of RL and striatal BOLD, both choices and striatal BOLD were better explained by the augmented model. Consistent with the hypothesized role for the hippocampus in this generalization, functional connectivity between the ventral striatum and hippocampus was modulated, across participants, by the ability of the augmented model to capture participants’ choice. Our results thus point toward an interactive model in which striatal RL systems may employ relational representations typically associated with the hippocampus.
机译:决策的研究侧重于多巴胺及其纹状体目标通过学习刺激奖励或刺激 - 响应协会,由加强学习(RL)理论良好描述的行为的指导选择。然而,基本RL的范围相对较为有限,并且没有解释关于刺激规律或关系的学习如何指导决策。这种学习的候选机制来自记忆域,这凸显了海马在学习刺激刺激关系方面的作用,通常与刺激反应学习中的纹章中的作用解离。在这里,我们使用FMRI和基于计算模型的分析来检查这些机制对RL的联合贡献。人类使用添加关系结构进行了RL任务,在用于将海马贡献隔离到内存的任务后建模。在每次试验中,参与者选择了四种选项中的一个,但在试验中,奖励概率与对的奖励概率相关联。对选项对之间的这种(无解释的)关系可能使观察者基于具有其他选项的体验和概括地概括了观察者来了解选项的值。我们观察到与纹状体中的学习和海马有关的大胆活动。通过将基本RL模型与一个增强进行比较以允许反馈来概括相关选项之间的概括,我们测试了选择行为和大胆活动是否受到跨相关选择概括的机会的影响。虽然这种概括超出了RL的标准计算账户和纹纹粗体,但增强模型更好地解释了两种选择和纹身大胆。与海马的假设作用一致,通过增强模型捕获参与者选择的能力,调制腹部纹状体和海马之间的功能性连通性。因此,我们的结果指出了一种交互式模型,其中纹状体RL系统可以采用通常与海马相关联的关系表示。

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