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: 8837113
• 负责人: Nathaniel Douglass Daw
• 金额: $ 34.71万
• 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 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8837113
• 关键词: Accounting; Adaptive Behaviors; Address; Affect; Algorithms; Area; Behavior; Behavioral; Brain; Brain region; 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; Research; Rewards; Role; Schizophrenia; Schools; Science; Scientist; Sorting - Cell Movement; Specificity; Students; System; Testing; Time; Training; Training and Education; Woman; base; behavior test; cognitive neuroscience; computational neuroscience; dopamine system; experience; flexibility; improved; nervous system disorder; 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.

描述（由申请人提供）：以前的经验指导决策的哪些方面？许多研究关注的是大脑如何在多次经历中计算出一个选项所获得的平均奖励。但这样的总结--由多巴胺能增量学习的著名模型产生--主要对重复性任务有用。对于大脑如何在更现实的任务中灵活地评估新的或不断变化的选项，人们了解得更少，这些任务必须依赖较少的聚合信息。这个应用程序认为，这从根本上是记忆的一个功能，因此这个项目着眼于大脑的记忆，以寻找最个性化的经验-情节-以寻求新的计算，认知和神经机制，可以支持更灵活的决策。总体假设是，情节记忆，海马体的支持，在指导灵活的决策中发挥着核心作用，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，该项目有望在这两个领域都有所建树。这是因为支持情景记忆的神经机制已经得到了很好的研究，但它们对适应行为的贡献却很少。在计算上，情景记忆可以支持一系列学习算法，这些算法利用稀疏的个人经验，例如蒙特卡罗和核方法。这些研究提出了新的、合理的假设，说明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。拟议中的研究旨在区分增量和情景学习的价值为基础的决策的贡献，并测试情景记忆在多大程度上有助于先前确定为基于模型的决策。我们的假设进行了测试拟合计算模型的神经活动，从功能性MRI实验在人类中，也选择健康个体的行为相比，患者孤立的损害特定的神经系统。这种计算、神经成像和神经心理学方法的结合允许精细地追踪学习的一次又一次的动态，如在大脑活动和行为中所反映的，并且还测试特定大脑区域在这些相同过程中的因果作用。 拟议活动的广泛影响是什么？一系列引人注目的精神和神经障碍，包括帕金森氏病、精神分裂症和进食障碍，都伴随着异常的决策和对这一提议至关重要的回路功能障碍，如纹状体和额颞机制。但要理解这种功能障碍，需要更好地理解这些回路中的每一个是如何分别影响决策的。专注于解开多个决策系统是特别相关的疾病，如药物滥用，这是假设为中心的增量强化机制的妥协，可能支持更多的习惯性行为和这些疾病的强迫性的基础。与此同时，药物也可能削弱或损害更多的审议或目标导向的选择系统，否则可能能够支持更有利的决定。正式理解这两种影响所扮演的角色，以及它们如何相互作用，有望改善这些和其他疾病的概念化，诊断和治疗。拟议的方案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，使他们成为跨学科未来的有效科学家。还将通过纽约大学和哥伦比亚大学的现有方案，并通过对纽约地区学校的推广，将这一培训的组成部分扩大到本科生和高中生。该项目还将有助于促进少数群体，包括妇女在科学领域的更广泛代表性。作为一名女性神经科学家，她的实验室里有许多女性受训者，PI Shohamy是一个榜样，该合作项目促进了对女性的计算神经科学培训，这是一个女性代表性特别不足的领域。