Distinct replay signatures for prospective decision-making and memory preservation
Distinct replay signatures for prospective decision-making and memory preservation
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用于前瞻性决策和记忆保存的独特重播签名
DOI：
10.1101/2021.11.08.467745
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Wimmer G
中科院分区：
文献类型：
--
作者：
Wimmer G
神经重放理论认为，它支持一系列功能，最突出的是计划和记忆巩固。在这里，我们测试了这样的假设：同一任务中重播的不同特征与基于模型的决策（“规划”）和记忆保存有关。我们设计了一个奖励学习任务，其中参与者利用结构知识进行基于模型的评估，同时必须保持两个独立和随机交替的任务环境的知识。使用脑磁图和多变量分析，我们首先确定了时间压缩顺序再激活，或重放，之前的选择和奖励反馈。在选择之前，当基于模型的规划策略是有益的时，针对当前任务相关环境的前瞻性重放强度被增强。奖励接收后，并与记忆保存的作用，重放替代远端任务环境的增强作为一个功能，减少近的经验与该环境。重要的是，这些规划和记忆保存的关系是有选择性的前选择和后反馈期，分别。我们的研究结果提供了支持的关键理论建议的功能作用的重放，并表明，规划和记忆相关的信号的相对强度调制正在进行的计算和任务的需求。
Theories of neural replay propose that it supports a range of functions, most prominently planning and memory consolidation. Here, we test the hypothesis that distinct signatures of replay in the same task are related to model-based decision-making (“planning”) and memory preservation. We designed a reward learning task wherein participants utilized structure knowledge for model-based evaluation, while at the same time had to maintain knowledge of two independent and randomly alternating task environments. Using magnetoencephalography and multivariate analysis, we first identified temporally compressed sequential reactivation, or replay, both prior to choice and following reward feedback. Before choice, prospective replay strength was enhanced for the current task-relevant environment when a model-based planning strategy was beneficial. Following reward receipt, and consistent with a memory preservation role, replay for the alternative distal task environment was enhanced as a function of decreasing recency of experience with that environment. Critically, these planning and memory preservation relationships were selective to pre-choice and post-feedback periods, respectively. Our results provide support for key theoretical proposals regarding the functional role of replay and demonstrate that the relative strength of planning and memory-related signals are modulated by ongoing computational and task demands.
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