CRCNS: Reinforcement learning in multi-dimensional action spaces

CRCNS：多维行动空间中的强化学习

基本信息

批准号：
8068884
负责人：
Nathaniel Douglass Daw
金额：
$ 37.4万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-01 至 2014-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8068884
关键词：
Address Animals Area Behavior Brain Choice Behavior Clinical Communication Decision Making Decision Theory Development Diagnosis Disease Dopamine Electrophysiology (science)Employee Strikes Experimental Designs Eye Eye Movements Functional Magnetic Resonance Imaging Functional disorder Generations Hand Harvest Human Joints Laboratories Learning Link Maps Measurement Mental disorders Methods Modeling Motor Neurons New York Outcome Parietal Parietal Lobe Patients Population Procedures Professional Education Prosencephalon Psychological reinforcement Rewards Role Saccades Schizophrenia Schools Scientist Screening procedure Signal Transduction Sorting - Cell Movement Students System Testing Training Training Programs Translating Work analytical tool clinically relevant coping design dopamine system experimental analysis high school improved innovation meetings nervous system disorder neural circuit novel outreach programs relating to nervous system research study scale up severe mental illness symposium theories

项目摘要

DESCRIPTION (provided by applicant): A striking range of mental disorders, from OCD to schizophrenia, is accompanied by aberrant decision-making and also by dysfunction in the dopamine system and its targets in the forebrain. Although celebrated computational work posits roles for this system together with the posterior parietal cortex in learning and decision-making for simple choice problems, it requires a tremendous leap of faith to imagine how these simple computational mechanisms can be "scaled up" from the laboratory to address real-world human behavior of the sort that is clinically problematic for patients with these disorders. One understudied aspect of this problem is the high dimensionality of the space of candidate actions, notably the involvement of multiple effectors such as hands and eyes. This project proposes a theoretical framework for more realistic learning and decision problems involving multiple effectors, and leverages it in experiments probing how the brain copes with learning and decision-making in these cases. The core idea is that the brain should divide-and-conquer: treating, e.g., hand and eye movements independently to simplify learning when their consequences are independent, but that it must evaluate actions jointly across effectors when this is not the case. Learning tasks manipulating this independence are used to: (1) test whether humans and animals learn to solve decision problems by separating or coordinating effector choices to efficiently harvest rewards; these tasks are combined with electrophysiological recordings and fMRI to (2) test whether separate or conjoint neural value maps are maintained for action values across effectors, as appropriate to the problem; and multiarea recordings are used to (3) test whether coordinated choices increase neural interactions between effector-specific motor maps. The work makes innovative use of computational theory for experimental design and analysis, in order to connect experimental observations across species, measurement types (spiking, local field potentials, fMRI), and scales (neuronal, systems). It also introduces a new laboratory microcosm for the computations needed to scale up existing decision theories toward clinically relevant real-world behaviors. In principle, quantitative theories of the brain's decision and learning systems hold important promise for the numerous serious mental illnesses that center around these systems, such as improved procedures for diagnosis or screening candidate treatments. This project aims to "scale up" such theories -- which are, in practice, too simple to deliver on this promise -- toward explaining the interacting neural circuits that control realistic behaviors more like those that are problematic for patients with mental illnesses.

描述（由申请人提供）：从强迫症到精神分裂症，一系列引人注目的精神障碍都伴随着异常的决策以及多巴胺系统及其前脑目标的功能障碍。尽管著名的计算工作将该系统与后顶叶皮层一起在简单选择问题的学习和决策中发挥了作用，但想象这些简单的计算机制如何从实验室“扩展到”到现实需要巨大的信念飞跃。解决现实世界中对患有这些疾病的患者存在临床问题的人类行为。该问题的一个未被充分研究的方面是候选动作空间的高维性，特别是手和眼睛等多个效应器的参与。该项目为涉及多个效应器的更现实的学习和决策问题提出了一个理论框架，并在实验中利用它来探索大脑在这些情况下如何应对学习和决策。核心思想是，大脑应该分而治之：当它们的后果是独立的时，独立地处理例如手和眼的运动，以简化学习，但当情况并非如此时，它必须联合评估效应器之间的行为。操纵这种独立性的学习任务用于：（1）测试人类和动物是否学会通过分离或协调效应器选择来有效收获奖励来解决决策问题；这些任务与电生理记录和功能磁共振成像相结合，以 (2) 测试是否根据问题的情况，针对效应器的动作值维护单独或联合的神经值图；多区域记录用于（3）测试协调选择是否会增加效应器特异性运动图之间的神经相互作用。这项工作创新地利用计算理论进行实验设计和分析，以便将跨物种、测量类型（尖峰、局部场电位、功能磁共振成像）和尺度（神经元、系统）的实验观察联系起来。它还引入了一个新的实验室微观世界，用于将现有决策理论扩展到临床相关的现实世界行为所需的计算。原则上，大脑决策和学习系统的定量理论为围绕这些系统的众多严重精神疾病带来了重要希望，例如改进诊断程序或筛选候选治疗方法。该项目旨在“扩大”此类理论（这些理论在实践中过于简单，无法兑现这一承诺），以解释控制现实行为的相互作用的神经回路，就像那些对精神疾病患者有问题的行为一样。