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不确定性改变了增量学习和情景记忆之间的平衡。

Uncertainty alters the balance between incremental learning and episodic memory.

机构信息

Department of Psychology, Columbia University, New York, United States.

Mortimer B. Zuckerman Mind, Brain, Behavior Institute, Columbia University, New York, United States.

出版信息

Elife. 2022 Dec 2;11:e81679. doi: 10.7554/eLife.81679.

Abstract

A key question in decision-making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision-making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions.

摘要

决策中的一个关键问题是人类如何在竞争的学习和记忆系统之间进行仲裁,以最大化奖励。我们通过探究增量试错学习与单个事件的情景记忆对选择的影响之间的平衡来解决这个问题。尽管有丰富的文献研究了孤立的增量学习,但情景记忆在决策中的作用最近才引起关注,而且很少有研究能区分它们的单独贡献。我们假设大脑在这两个系统之间进行理性仲裁,根据不确定性,在最适合的情况下依赖于每个系统。我们通过直接对比情景和增量影响对决策的贡献来检验这个假设,同时使用一种经过充分验证的奖励波动性操纵来操纵增量学习的相对不确定性。在两个由年轻成年人组成的大型独立样本中,参与者根据不确定性理性地权衡这些影响,当增量总结更不确定时,他们更多地依赖情景信息。这些结果支持了这样一种观点,即大脑根据相对不确定性优化不同形式的学习和记忆之间的平衡,并阐明了情景记忆影响决策的情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98b3/9810331/9b415d716140/elife-81679-fig1.jpg

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