CRCNS: Reinforcement learning in multi-dimensional action spaces

CRCNS：多维行动空间中的强化学习

• 批准号: 7923719
• 负责人: Nathaniel Douglass Daw
• 金额: $ 36.44万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 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/7923719
• 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; Lip structure; 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; 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

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伴随着异常的决策，以及多巴胺系统及其前脑靶点的功能障碍。虽然著名的计算工作假定了这个系统与后顶叶皮层在学习和决策简单的选择问题中的作用，但要想象这些简单的计算机制如何从实验室“扩大”到解决现实世界中的人类行为，这需要巨大的信心飞跃。这个问题的一个未充分研究的方面是候选动作空间的高维性，特别是涉及多个效应器，如手和眼睛。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如何应对这些情况下的学习和决策。核心思想是大脑应该分而治之：治疗，例如，手和眼的运动是独立的，以简化学习时，他们的后果是独立的，但它必须评估行动联合跨效应器时，这是不是这样的情况。操纵这种独立性的学习任务用于：（1）测试人类和动物是否通过分离或协调效应器选择来学习解决决策问题，以有效地收获奖励;这些任务与电生理记录和fMRI相结合，以（2）测试是否为跨效应器的动作值保持单独或联合的神经值映射，以适应问题;和多区域记录用于（3）测试协调的选择是否增加效应器特异性运动图之间的神经相互作用。 这项工作创新性地利用计算理论进行实验设计和分析，以连接跨物种，测量类型（尖峰，局部场电位，fMRI）和尺度（神经元，系统）的实验观察。它还引入了一个新的实验室微观世界，用于将现有的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对以这些系统为中心的许多严重精神疾病具有重要的前景，例如改进诊断程序或筛选候选治疗方法。该项目旨在“扩大”这些理论-在实践中，这些理论过于简单，无法实现这一承诺-以解释控制现实行为的相互作用的神经回路，更像那些对精神疾病患者有问题的神经回路。