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

项目摘要 决策是如何影响记忆，从而对人类行为产生长期影响的？回答这个 这个问题对于理解决策受损如何导致适应不良的记忆结果至关重要， 例如对负面事件的沉思、对错误记忆的敏感性或记忆丧失。近年来 强化学习（RL）框架在描述受损决策方面特别富有成效， 精神疾病以及识别连接决策和记忆的计算机制。的 这个项目的首要目标是确定神经计算机制，解释如何学习 驱动决策的过程也会影响随后的记忆。为此，建议的K99阶段 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 人类海马和非海马识别记忆性能（Aim 1），以及神经生物学， 一种确定这些RL-记忆相互作用的电生理机制的方法（目的2） 研究的R 00阶段将采用这些方法来研究基于模型的强化的贡献 学习这些不同的记忆过程（目标3）。具体来说，目标1将测试无模型RL如何发出信号， 因为预测错误可能会与刺激的感知特征相互作用，以增强即时（非） 海马）记忆和延迟（海马）记忆。Aim 2将利用颅内 记录从人类获得的癫痫监测电极，以测试如何在额叶皮层神经活动， tex，海马和非海马内侧颞区（如海马旁回） 无模型RL过程对记忆性能的影响。在完成目标1-2（K99）后， 一位候选人--一位具有人类记忆神经生物学背景的神经科学家--将接受新的培训 在强化学习和决策的计算建模中具有独特的、多学科的 应用于目标3（R 00）的技能，研究基于模型的强化学习过程如何影响记忆行为和神经网络 活动候选人的导师是唯一适合提供这些目标的培训，鉴于他们的专业知识 在桥梁计算建模（顾晓思博士）和人类神经生理学（伊格纳西奥赛斯博士），以了解 人类决策。这些技能也将促进候选人的过渡到一个独立的研究人员 长期目标是进行综合的行为，计算和生物学研究，研究这些 人类的决策和记忆过程在精神疾病中出错。

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.