CRCNS: Computational and neural mechanisms of memory-guided decisions

描述(由申请人提供)：以前经验的哪些方面指导了决定？许多研究关注的是大脑如何计算出一种选择所获得的奖励的平均值。但这样的总结--由多巴胺递增学习的突出模型产生--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的了解要少得多，因为更现实的任务必须依赖较少聚合的信息。这个应用程序认为，这基本上是记忆的一种功能，所以这个项目从大脑的记忆中寻找最个性化的经历--情节--以寻找新的计算、认知和神经机制，以支持更灵活的决定。最重要的假设是，在海马体的支持下，情节记忆在指导灵活决策方面发挥着核心作用，并补充了众所周知的多巴胺能和纹状体系统在递增学习价值中的作用。 提议的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，并将每个领域的合作者聚集在一起，这个项目有望阐明这两个领域。这是因为支持情节记忆的神经机制得到了很好的研究，但对它们对适应行为的贡献却研究得较少。在计算上，情节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，如蒙特卡罗和核方法。这为大脑如何解决更现实的决策问题，特别是它如何实现“目标导向”或“基于模型”的选择，提出了新颖、可信的假设。这些研究旨在区分渐进式学习和情景学习对基于价值的决策的贡献，并测试情景记忆在多大程度上对先前被认为是基于模型的决策做出贡献。我们的假设经过测试，使计算模型与人类功能磁共振实验中的神经活动相匹配，并与特定神经系统孤立受损的患者相比，在健康个体中选择行为。这种计算、神经成像和神经心理学方法的结合，可以精细地追踪学习的逐次尝试动态，这既反映在大脑活动和行为上，也测试特定大脑区域在这些相同过程中的因果作用。 拟议的活动有哪些更广泛的影响？一系列惊人的精神和神经障碍，包括帕金森氏症、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，如纹状体和额颞叶机制。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。对解开多个决策系统的关注与药物滥用等疾病尤其相关，人们假设药物滥用的中心是渐进强化机制的妥协，这些机制可能支持更多的习惯性行为，并构成此类疾病的强迫性质。与此同时，药物也可能削弱或损害更慎重或目标导向的选择系统，否则这些系统可能会支持更有利的决定。正式地了解这两种影响所起的作用，以及它们是如何相互作用的，有望改进这些和其他障碍的概念化、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。通过集成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)，两个PI实验室的学生都接受了关于统一研究问题的不同方法的培训，为他们在更多学科交叉的未来成为有效的科学家做好准备。这种培训的组成部分还将通过纽约大学和哥伦比亚大学的现有课程以及纽约地区学校的外联活动扩展到本科生和高中生。该项目还将有助于促进包括妇女在内的少数群体在科学界的更广泛代表性。作为一名在她的实验室里有许多女性受训人员的女性神经学家，Pi Shohamy是一个榜样，这个合作项目促进了女性在计算神经科学方面的培训，而在这个领域，女性的代表性尤其不足。

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.