CRCNS: Computational and neural mechanisms of memory-guided decisions

描述（由申请人提供）：以前的经历对决定有哪些指导作用？许多研究关注的是大脑如何在许多经历中计算一个选择所获得奖励的平均值。但这样的总结——由著名的多巴胺能增量学习模型产生——主要适用于重复性任务。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择，人们知之甚少，这必须依赖较少的汇总信息。这个应用程序认为，这基本上是记忆的功能，所以这个项目着眼于大脑记忆中最个性化的经历——情节——寻求新的计算、认知和神经机制，以支持更灵活的决策。最重要的假设是，由海马体支持的情景记忆在指导灵活的决策中起着核心作用，并补充了多巴胺能和纹状体系统在增量学习中的作用。

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.