Department of Psychology, Program in Cognition and Perception, New York University, New York, New York, United States of America.
PLoS Comput Biol. 2013;9(12):e1003387. doi: 10.1371/journal.pcbi.1003387. Epub 2013 Dec 5.
How do we use our memories of the past to guide decisions we've never had to make before? Although extensive work describes how the brain learns to repeat rewarded actions, decisions can also be influenced by associations between stimuli or events not directly involving reward - such as when planning routes using a cognitive map or chess moves using predicted countermoves - and these sorts of associations are critical when deciding among novel options. This process is known as model-based decision making. While the learning of environmental relations that might support model-based decisions is well studied, and separately this sort of information has been inferred to impact decisions, there is little evidence concerning the full cycle by which such associations are acquired and drive choices. Of particular interest is whether decisions are directly supported by the same mnemonic systems characterized for relational learning more generally, or instead rely on other, specialized representations. Here, building on our previous work, which isolated dual representations underlying sequential predictive learning, we directly demonstrate that one such representation, encoded by the hippocampal memory system and adjacent cortical structures, supports goal-directed decisions. Using interleaved learning and decision tasks, we monitor predictive learning directly and also trace its influence on decisions for reward. We quantitatively compare the learning processes underlying multiple behavioral and fMRI observables using computational model fits. Across both tasks, a quantitatively consistent learning process explains reaction times, choices, and both expectation- and surprise-related neural activity. The same hippocampal and ventral stream regions engaged in anticipating stimuli during learning are also engaged in proportion to the difficulty of decisions. These results support a role for predictive associations learned by the hippocampal memory system to be recalled during choice formation.
我们如何利用过去的记忆来指导以前从未做过的决策？尽管大量研究描述了大脑如何学会重复获得奖励的行为，但决策也可以受到刺激或事件之间关联的影响，这些关联并不直接涉及奖励——例如，当使用认知地图规划路线或使用预测的反击来下棋时——当在新颖的选项中进行选择时，这些类型的关联是至关重要的。这个过程被称为基于模型的决策。虽然学习可能支持基于模型的决策的环境关系已经得到了很好的研究，并且这种信息已经被推断会影响决策，但关于这些关联是如何被获取并驱动选择的完整循环的证据很少。特别感兴趣的是，决策是否直接由用于更一般的关系学习的相同记忆系统支持，或者是否依赖于其他专门的表示。在这里，我们基于之前的工作，该工作分离了支持序列预测学习的双重表示，直接证明了其中一种表示，由海马记忆系统和相邻的皮质结构编码，支持目标导向的决策。通过交错学习和决策任务，我们直接监测预测学习，并追踪其对奖励决策的影响。我们使用计算模型拟合来定量比较多个行为和 fMRI 可观察变量的学习过程。在这两个任务中，一个定量一致的学习过程可以解释反应时间、选择以及期望和惊讶相关的神经活动。在学习过程中，参与预测刺激的海马体和腹侧流区域也会根据决策的难度而参与其中。这些结果支持了海马记忆系统学习的预测关联在选择形成过程中被回忆的作用。
