The computational and neural mechanisms linking decision-making and memory in humans

项目总结 决策如何影响记忆，从而对人类行为产生持久的影响？回答这个问题 这个问题对于理解决策受损如何导致不适应的记忆结果至关重要， 比如对负面事件的沉思，对虚假记忆的易感性，或记忆丧失。近几年来， 强化学习(RL)框架在描述受损性决策方面尤其有效 精神障碍以及识别将决策和记忆联系起来的计算机制。这个 这个项目的主要目标是确定神经计算机制，解释学习是如何 驱动决策的过程也会影响随后的记忆。为此，建议的K99期 研究包括一种计算方法，以确定无模型强化学习信号如何影响 人类海马区和非海马区识别记忆的表现(目标1)，以及神经生物学- 确定这些RL-记忆相互作用背后的电生理机制的ICIC方法(目标2); 研究的R00阶段将采用这些方法来研究基于模型的加固的贡献 学习这些不同的记忆过程(目标3)。具体地说，AIM 1将测试无模型RL信号如何 因为预测误差可能与刺激的感知特征相互作用以增强即时(非 健康志愿者的(海马区)记忆和延迟(海马)记忆。AIM 2将利用颅内 使用癫痫监测电极从人类获得的记录，以测试额叶中心的神经活动如何 大脑皮质、海马体和非海马体内侧颞区(如海马旁回)起辅助作用。 无模型RL进程对内存性能的影响。目标1-2(K99)完成后， 候选人--一位在人类记忆的神经生物学方面有背景的神经科学家--将接受新的培训 在强化学习和决策的计算建模方面具有独到之处，多学科交叉 技能集应用于目标3(R00)，研究基于模型的RL过程如何影响助记行为和神经 活动。考虑到候选人的专业知识，候选人的导师非常适合为这些目标提供培训 在计算建模(顾小思博士)和人类神经生理学(伊格纳西奥·赛兹博士)之间架起一座桥梁 人类的决策。这些技能也将有助于候选人转变为独立研究人员 其长期目标是进行综合的行为、计算和生物学研究 人类的决策和记忆过程在精神障碍中会出现偏差。

PROJECT SUMMARY How does decision-making influence memory to leave a long-lasting impact on human behavior? Answering this question is critical to understanding how impaired decision making might lead to maladaptive memory outcomes, such as rumination on negative events, susceptibility to false memories, or memory loss. In recent years, the reinforcement learning (RL) framework has been particularly fruitful for describing impaired decision-making in psychiatric disorders as well as identifying computational mechanisms linking decision-making and memory. The overarching aim of this project is to identify the neurocomputational mechanisms that explain how the learning processes driving decision-making also influence subsequent memory. To do so, the K99 phase of the proposed study consists of a computational approach to identify how model-free reinforcement learning signals influence both hippocampal and non-hippocampal recognition memory performance in humans (Aim 1), and a neurobiolog- ical approach to identify the electrophysiological mechanisms underlying these RL-memory interactions (Aim 2); the R00 phase of the study will deploy these approaches to study the contributions of model-based reinforcement learning to these distinct memory processes (Aim 3). Specifically, Aim 1 will test how model-free RL signals such as prediction errors might interact with the perceptual features of a stimulus to enhance both immediate (non- hippocampal) memory and delayed (hippocampal) memory in healthy volunteers. Aim 2 will leverage intracranial recording obtained from humans with epilepsy monitoring electrodes to test how neural activity in the frontal cor- tex, hippocampus, and non-hippocampal medial temporal regions (such as parahippocampal gyrus) subserve the influence of model-free RL processes on memory performance. Upon completion of Aims 1-2 (K99), the candidate—a neuroscientist with a background in the neurobiology of human memory—will obtain new training in computational modeling of reinforcement learning and decision-making and have a unique, multi-disciplinary skillset to apply to Aim 3 (R00), to study how model-based RL processes influence mnemonic behavior and neural activity. The candidate’s mentors are uniquely suited to provide the training for these Aims, given their expertise in bridging computational modeling (Dr. Xiaosi Gu) and human neurophysiology (Dr. Ignacio Saez) to understand human decision-making. These skills will also facilitate the candidate’s transition into an independent researcher with the long-term goal of performing integrative behavioral, computational, and biological studies of how these human decision-making and memory processes go awry in psychiatric disorders.