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

连接人类决策和记忆的计算和神经机制

基本信息

批准号：
10808667
负责人：
Salman Ehtesham Qasim
金额：
$ 10.31万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-11 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10808667
关键词：
Address Affect Automobile Driving Behavior Behavioral Biological Biological Assay Biological Markers Brain Cognition Complex Computer Models Decision Making Disparate Electrodes Electrophysiology (science)Epilepsy Event Exhibits Functional Magnetic Resonance Imaging Functional disorder Goals Hippocampus Hour Human Image Impairment Intractable Epilepsy Knowledge Learning Link Measures Medial Mediating Memory Memory Loss Mental Depression Mental disorders Mentors Mentorship Midbrain structure Modeling Monitor Neurobiology Neurons Outcome Parahippocampal Gyrus Patients Performance Phase Play Predisposition Process Psychological reinforcement Research Research Personnel Reversal Learning Rewards Seizures Signal Transduction Sleep Stimulus Study models Techniques Testing Training addiction computational basis frontal lobe healthy volunteer human subject member memory consolidation memory process memory recognition multidisciplinary neural neuromechanism neurophysiology recruit reward processing rumination skills temporal measurement theories time interval

项目摘要

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.

项目摘要决策如何影响记忆以对人类行为产生长期影响？回答这个问题对于理解决策受损可能导致适应不良的记忆结果至关重要，例如对负面事件的反省，对虚假记忆的敏感性或记忆丧失。近年来，强化学习（RL）框架在描述决策受损方面尤其富有成果精神疾病以及确定连接决策和记忆的计算机制。这该项目的总体目的是确定神经计算机制，以解释学习方式推动决策的过程还会影响随后的记忆。为此，提议的K99阶段研究包括一种计算方法，以确定无模型的强化学习信号如何影响人类的海马和非海马识别记忆表现（AIM 1），以及神经生物学 - 识别这些RL内存相互作用的基础电生理机制的方法（AIM 2）；研究的R00阶段将部署这些方法来研究基于模型的强化的贡献学习这些独特的记忆过程（目标3）。具体而言，AIM 1将测试无模型的RL信号这样的由于预测错误可能与刺激的感知特征相互作用，以增强同时立即（非 - 健康志愿者中的海马记忆和延迟（海马）记忆。 AIM 2将利用颅内记录从人类中获得癫痫监测电极的记录，以测试额叶中的中性活性 Tex，海马和非海马中位临时区域（例如帕拉希帕克宫回）供应无模型RL过程对内存性能的影响。 AIMS 1-2（K99）完成后，候选人（具有人类记忆神经生物学背景的神经科学家）将获得新的培训在强化学习和决策的计算建模中，并具有独特的多学科适用于AIM 3（R00）的技能，以研究基于模型的RL过程如何影响助记符行为和神经活动。鉴于他们的专业知识，候选人的导师非常适合为这些目标提供培训在桥接计算建模（博士）和人类神经生理学（Ignacio Saez博士）中人类决策。这些技能还将促进候选人向独立研究人员的过渡长期目标是对这些方式进行综合行为，计算和生物学研究人类的决策和记忆过程在精神疾病中出现问题。