CRCNS: Reinforcement learning in multi-dimensional action spaces

描述（由申请人提供）：一系列令人震惊的精神障碍，从强迫症到精神分裂症，伴随着异常的决策，也伴随着多巴胺系统及其前脑靶点的功能障碍。尽管著名的计算工作假设了这个系统与后顶叶皮层在简单选择问题的学习和决策中的作用，但想象这些简单的计算机制如何从实验室“放大”到解决现实世界中人类行为的问题，这对患有这些疾病的患者来说是临床上的问题，这需要一个巨大的信念飞跃。这个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，特别是涉及多个效应器，如手和眼睛。该项目提出了一个涉及多个效应器的更现实的学习和决策问题的理论框架，并利用它在实验中探索大脑如何在这些情况下应对学习和决策。核心思想是，大脑应该分而治之：例如，当手和眼的运动是独立的结果时，大脑应该独立处理它们，以简化学习；但当情况并非如此时，大脑必须共同评估跨效应器的行动。使用操纵这种独立性的学习任务：(1)测试人类和动物是否通过分离或协调效应选择来学习解决决策问题，从而有效地获得奖励；这些任务与电生理记录和功能磁共振成像相结合，以(2)测试是否保留单独的或联合的神经价值图，以用于跨效应器的动作值，以适合问题；多区域记录用于(3)测试协调选择是否会增加特效特定运动图之间的神经相互作用。

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.