Interactions Among Working Memory, Reinforcement Learning, and Effort in Value-Based Choice: A New Paradigm and Selective Deficits in Schizophrenia.
Interactions Among Working Memory, Reinforcement Learning, and Effort in Value-Based Choice: A New Paradigm and Selective Deficits in Schizophrenia.
复制标题
工作记忆，强化学习和基于价值选择的努力之间的相互作用：精神分裂症的新范式和选择性缺陷。
DOI：
10.1016/j.biopsych.2017.05.017
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发表时间：
2017-09-15
影响因子：
10.6
通讯作者：
Frank MJ
中科院分区：
文献类型：
--
作者：
Collins AGE;Albrecht MA;Waltz JA;Gold JM;Frank MJ
在研究学习时，研究者只直接观察参与者的选择，这通常被认为是从一个单一的学习过程中产生的。然而，许多可分离的系统，如工作记忆（WM）和强化学习（RL），同时有助于人类的学习。识别每个系统的贡献是必不可少的映射的神经基板并行行为的贡献;计算建模可以帮助设计任务，允许这样一个可分离的过程识别，并推断他们的个人贡献。我们提出了一个新的实验协议，分别确定RL和WM的贡献学习，是敏感的参数变化，在这两个，并允许我们调查是否过程相互作用。在实验1-2中，我们用健康的年轻人（n=29和n=52）测试该方案。在实验3中，我们使用它来调查精神分裂症患者（n=49例，n=32对照）的学习障碍。实验1-2建立了WM和RL的贡献学习，证明了负载和延迟，奖励历史，分别选择的参数调制。实验结果还表明，WM和RL之间存在相互作用，在高WM负荷下RL增强。此外，我们观察到的成本的心理努力，控制强化的历史：参与者更喜欢刺激，他们遇到低WM负荷。实验3显示选择性赤字WM的贡献和保留RL值学习精神分裂症患者相比，对照组。计算方法使我们能够理清多个系统对学习的贡献，从而进一步加深我们对精神疾病的理解。
When studying learning, researchers directly observe only the participants' choices, which are often assumed to arise from a unitary learning process. However, a number of separable systems, such as working memory (WM) and reinforcement learning (RL), contribute simultaneously to human learning. Identifying each system's contributions is essential for mapping the neural substrates contributing in parallel to behavior; computational modeling can help design tasks that allow such a separable identification of processes, and infer their contributions in individuals. We present a new experimental protocol that separately identifies the contributions of RL and WM to learning, is sensitive to parametric variations in both, and allows us to investigate whether the processes interact. In experiments 1-2, we test this protocol with healthy young adults (n=29 and n=52). In experiment 3, we use it to investigate learning deficits in medicated individuals with schizophrenia (n=49 patients, n=32 controls). Experiments 1-2 established WM and RL contributions to learning, evidenced by parametric modulations of choice by load and delay, and reward history, respectively. It also showed interactions between WM and RL, where RL was enhanced under high WM load. Moreover, we observed a cost of mental effort, controlling for reinforcement history: participants preferred stimuli they encountered under low WM load. Experiment 3 revealed selective deficits in WM contributions and preserved RL value learning in individuals with schizophrenia compared to controls. Computational approaches allow us to disentangle contributions of multiple systems to learning and, consequently, further our understanding of psychiatric diseases.
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影响因子：
7.2
作者：
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