Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory
Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory
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DOI：
10.1073/pnas.1720963115
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发表时间：
2018-03-06
影响因子：
11.1
通讯作者：
Frank, Michael J.
中科院分区：
文献类型：
--
作者：
Collins, Anne G. E.;Frank, Michael J.
从奖惩中学习对于生存是必不可少的，并有助于人类灵活的行为。人们普遍认识到，多个认知和强化学习系统有助于决策，但它们相互作用的性质是难以捉摸的。在这里，我们利用方法来提取人类脑电中强化学习(RL)和工作记忆(WM)的逐次试验指数，以揭示超出仅由行为所提供的单次试验计算。神经动力学证实，神经期望的增加预示着在接下来的反馈阶段减少了神经惊讶，这支持了RL模型的中心原则。试验内和交叉试验的动力学揭示了学习系统之间的合作相互作用，其中WM贡献期望来指导RL，尽管在选择过程中系统之间存在竞争。总之，这些结果提供了对多个神经系统如何相互作用进行学习和决策的更深层次的理解，并有助于分析它们在临床人群中的干扰。
Learning from rewards and punishments is essential to survival and facilitates flexible human behavior. It is widely appreciated that multiple cognitive and reinforcement learning systems contribute to decision-making, but the nature of their interactions is elusive. Here, we leverage methods for extracting trial-by-trial indices of reinforcement learning (RL) and working memory (WM) in human electro-encephalography to reveal single-trial computations beyond that afforded by behavior alone. Neural dynamics confirmed that increases in neural expectation were predictive of reduced neural surprise in the following feedback period, supporting central tenets of RL models. Within-and cross-trial dynamics revealed a cooperative interplay between systems for learning, in which WM contributes expectations to guide RL, despite competition between systems during choice. Together, these results provide a deeper understanding of how multiple neural systems interact for learning and decision-making and facilitate analysis of their disruption in clinical populations.