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（由申请人提供）：以往经验的哪些方面可以指导决策？许多研究都涉及大脑如何计算多次经历中某个选项所获得的平均奖励。但这样的总结——由著名的多巴胺能增量学习模型产生——主要对重复性任务有用。人们对大脑如何在更现实的任务中灵活评估新的或不断变化的选项了解甚少，这些任务必须依赖较少的聚合信息。该应用程序认为，这从根本上来说是记忆的功能，因此该项目着眼于大脑的记忆来寻找最个性化的经历（情节），以寻求新的计算、认知和神经机制，以支持更灵活的决策。总体假设是，由海马体支持的情景记忆在指导灵活决策方面发挥着核心作用，并补充了多巴胺能和纹状体系统在增量学习价值中的众所周知的作用。 拟议活动的智力价值是什么？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，并将来自每个领域的合作者聚集在一起，该项目有望为这两个领域带来光明。这是因为支持情景记忆的神经机制已得到充分研究，但对它们对适应性行为的贡献却研究较少。在计算上，情景记忆可以支持一系列利用稀疏的个人经验的学习算法，例如蒙特卡罗和核方法。这些对于大脑如何解决更现实的决策问题，特别是如何实现“目标导向”或“基于模型”的选择提出了新颖、合理的假设。拟议的研究旨在区分增量学习和情景学习对基于价值的决策的贡献，并测试情景记忆对先前确定为基于模型的决策的贡献程度。我们的假设经过测试，将计算模型拟合到人类功能性 MRI 实验的神经活动，以及健康个体与特定神经系统孤立损伤患者的选择行为之间。这种计算、神经影像和神经心理学方法的结合可以精细地追踪大脑活动和行为中反映的学习的逐次试验动态，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？一系列引人注目的精神和神经系统疾病，包括帕金森病、精神分裂症和饮食失调，都伴随着异常决策和该提议的核心电路功能障碍，例如纹状体和额颞叶机制。但要理解这种功能障碍，就需要更好地理解每个回路如何分别影响决策。重点关注理清多个决策系统，尤其与药物滥用等疾病相关，据推测，这主要集中在增量强化机制的妥协上，这些机制可能支持更多的习惯性行为，并成为此类疾病的强迫性质的基础。与此同时，药物也可能削弱或损害更加深思熟虑或目标导向的选择系统，否则这些系统可能能够支持更有利的决策。正式了解这两种影响所发挥的作用以及它们如何相互作用，有望改善这些疾病和其他疾病的概念化、诊断和治疗。拟议的计划还提供了独特的培训和教育机会。通过整合系统和认知神经科学的多个核心工具（计算建模、功能成像、患者研究、行为分析），两个 PI 实验室的学生都接受了针对统一研究问题的不同方法的培训，使他们成为有效的科学家更多跨学科的未来。该培训的内容还将通过纽约大学和哥伦比亚大学的现有项目以及纽约地区学校的推广扩展到本科生和高中生群体。该项目还将有助于促进包括女性在内的少数群体在科学领域的更广泛代表性。作为一名女性神经科学家，PI Shohamy 的实验室中有许多女性受训者，PI Shohamy 充当了榜样，该合作项目促进了对女性在计算神经科学领域的培训，而在这一领域，女性的代表性尤其不足。 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。数据管理计划合理。在实际和道德上可行的情况下，将根据要求共享已发布的数据。 预算和支持期限： 按要求推荐