CRCNS: Computational and neural mechanisms of memory-guided decisions

CRCNS：记忆引导决策的计算和神经机制

• 批准号: 8926934
• 负责人: Nathaniel Douglass Daw
• 金额: $ 32.99万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8926934
• 关键词: Accounting; Adaptive Behaviors; Address; Affect; Algorithms; Area; Behavior; Behavioral; Brain; Brain region; Budgets; Choice Behavior; Cognitive; Complement; Computer Simulation; Corpus striatum structure; Cues; Data; Decision Making; Diagnosis; Disease; Dissociation; Drug abuse; Eating Disorders; Employee Strikes; Episodic memory; Evaluation; Family; Female; Functional Imaging; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; High School Student; Hippocampus (Brain); Human; Image; Individual; Laboratories; Learning; Light; Link; Machine Learning; Memory; Mental disorders; Midbrain structure; Minority; Modeling; Monte Carlo Method; Nature; Neuropsychology; New York; Outcome; Parkinson Disease; Patients; Pharmaceutical Preparations; Play; Population; Process; Psychological reinforcement; Publishing; Research; Resource Sharing; Rewards; Role; Schizophrenia; Schools; Science; Scientist; Sorting - Cell Movement; Specificity; Students; System; Testing; Time; Training; Training and Education; Vertebrates; Woman; base; behavior test; behavioral study; cognitive neuroscience; computational neuroscience; data management; dopamine system; experience; flexibility; human subject protection; improved; nervous system disorder; neural correlate; neuroimaging; neuromechanism; neuropsychological; novel; outreach; programs; relating to nervous system; research study; role model; statistics; tool; treatment strategy

DESCRIPTION (provided by applicant): What aspects of previous experiences guide decisions? Much research concerns how the brain computes the average, over many experiences, of rewards received for an option. But such a summary - produced by prominent models of dopaminergic incremental learning- is chiefly useful for repetitive tasks. Much less is understood about how the brain can flexibly evaluate new or changing options in more realistic tasks, which must rely on less aggregated information. This application argues that this is fundamentally a function of memory, so this project looks to the brain's memories for the most individuated experiences - episodes - to seek new computational, cognitive and neural mechanisms that could support more flexible decisions. The overarching hypothesis is that episodic memory, supported by the hippocampus, plays a central role in guiding flexible decision making and complements the wellknown role of dopaminergic and striatal systems in incremental learning of value. What is the intellectual merit of the proposed activity? By connecting the computational neuroscience of decision making with the cognitive neuroscience of memory, and bringing together collaborators from each area, this project promises to shed light on both areas. This is because the neural mechanisms supporting episodic memory are well studied, but less so their contribution to adaptive behavior. Computationally, episodic memories can support a family of learning algorithms that draw on sparse, individual experiences, such as Monte Carlo and kernel methods. These suggest novel, plausible hypotheses for how the brain solves more realistic decision problems, and in particular how it implements "goal-directed" or "model-based" choices. The proposed studies aim to differentiate the contributions of incremental and episodic learning to value-based decisions, and test to what extent episodic memories contribute to decisions previously identified as model-based. Our hypotheses are tested fitting computational models to neural activity from functional MRI experiments in humans, and also to choice behavior in healthy individuals compared to patients with isolated damage to specific neural systems. This combination of computational, neuroimaging and neuropsychological approaches permits finely tracing the trial-by-trial dynamics of learning as reflected both in brain activity nd behavior, and also testing the causal role of particular brain regions in these same processes. What are the broader impacts of the proposed activity? A striking range of psychiatric and neurological disorders, including Parkinson's disease, schizophrenia and eating disorders, are accompanied by aberrant decision-making and by dysfunction in circuitry central to this proposal, such as striatal and fronto-temporal mechanisms. But understanding such dysfunction requires a better understanding of how each of these circuits separately influences decisions. A focus on untangling multiple decision systems is particularly pertinent to disorders such as drug abuse, which is hypothesized to center on the compromise of incremental reinforcement mechanisms that may support more habitual actions and underlie the compulsive nature of such diseases. At the same time, drugs may also weaken or compromise more deliberative or goal-directed choice systems that might otherwise be able to support more advantageous decisions. Formally understanding the roles played by both of these influences, and how they interact, promises to improve the conceptualization, diagnosis, and treatment of these and other disorders. The proposed program also provides unique opportunities for training and education. By integrating multiple core tools of systems and cognitive neuroscience (computational modeling, functional imaging, patient studies, behavioral analyses), students in the labs of both PIs are trained in different approaches to a unified research question, preparing them to be effective scientists in a more interdisciplinary future. Components of this training will also be extended to undergraduate and high school student populations through existing programs at both NYU and at Columbia and through outreach to New York area schools. This project will also help promote broader representation of minorities in science, including women. As a female neuroscientist with many women trainees in her laboratory, PI Shohamy serves as a role model and the collaborative project facilitates training for women in computational neuroscience, an area in which women are particularly underrepresented. Protections for Human Subjects: Acceptable Vertebrate Animals: Not applicable Resource Sharing: Acceptable. Data management plan is reasonable. Published data will be shared upon request when practically and ethically possible. Budget and Period of Support: Recommend as Requested

描述（申请人提供）：以前的经验指导决策的哪些方面？许多研究都涉及大脑如何计算一种选择的奖励，从而计算平均水平。但是，由多巴胺能增量学习的突出模型产生的摘要主要对重复任务有用。关于大脑如何在更现实的任务中可以灵活地评估新的或不断变化的选项的理解更少，这必须依靠较少的汇总信息。该应用程序认为，这从根本上是记忆的函数，因此该项目希望大脑的记忆（情节）寻求新的计算，认知和神经机制，这些机制可以支持更灵活的决策。总体假设是，由海马支持的情节记忆在指导灵活的决策制定中起着核心作用，并补充了多巴胺能和纹状体系统在价值学习中的众所周知的作用。 拟议活动的智力优点是什么？通过将决策的计算神经科学与记忆的认知神经科学联系起来，并将各个领域的合作者汇总在一起，该项目有望阐明这两个领域。这是因为对支持情节记忆的神经机制进行了充分的研究，但对它们对适应性行为的贡献较少。在计算上，情节记忆可以支持借鉴稀疏，个人经验的学习算法，例如蒙特卡洛和内核方法。这些提出了有关大脑如何解决更现实的决策问题的新颖，合理的假设，尤其是它如何实现“目标定向”或“基于模型”的选择。拟议的研究旨在区分增量和情节学习对基于价值的决策的贡献，并测试情节记忆在多大程度上有助于先前确定为基于模型的决策。与对特定神经系统孤立损害的患者相比，我们的假设是对来自人类功能性MRI实验的神经活性的拟合计算模型的测试。计算，神经影像学和神经心理学方法的这种结合允许在大脑活动和行为中反映出学习的逐审动力，并测试特定大脑区域在这些相同过程中的因果作用。 拟议活动的更广泛影响是什么？包括帕金森氏病，精神分裂症和饮食失调在内的一系列精神病和神经系统疾病都伴有异常的决策，以及该提议中心的电路功能障碍，例如纹状体和额叶机制。但是，了解这种功能障碍需要更好地理解这些电路中的每个电路如何分别影响决策。关注解开多个决策系统的关注与诸如药物滥用之类的疾病特别相关，该疾病以逐步加强机制的妥协为中心，这些机制可能支持更多的习惯行动，并构成了此类疾病的强迫性。同时，药物还可能削弱或损害更具审议或目标的选择系统，否则可能能够支持更有利的决定。正式了解这两种影响的作用及其相互作用的方式，有望改善这些和其他疾病的概念化，诊断和治疗。拟议的计划还为培训和教育提供了独特的机会。通过整合系统和认知神经科学的多种核心工具（计算建模，功能成像，患者研究，行为分析），对两个PI的实验室中的学生进行了不同的培训，以不同的方法培训了一个统一的研究问题，这使他们成为跨学科的未来的有效科学家。这项培训的组成部分还将通过纽约大学和哥伦比亚的现有计划以及向纽约地区的学校扩展到本科和高中学生的人群。该项目还将有助于促进包括妇女在内的科学领域的更广泛代表。作为一名女性神经科学家，她在她的实验室中有许多女性学员，Pi Shohamy是榜样，协作项目促进了对计算神经科学中女性的培训，该领域的女性妇女的代表性少于代表性。 对人类受试者的保护： 可以接受 脊椎动物： 不适用 资源共享： 可以接受。数据管理计划是合理的。在实际和道德上可能会根据请求共享已发布的数据。 预算和支持期： 根据要求推荐