Modeling and mapping multiple computational processes in human reinforcement learning

项目摘要 从奖励中学习是神经系统的基本作用之一，允许有益的行为 重复，避免有害行为。最近有一点很清楚，当人类学习 从环境中的奖励，他们依赖于多个神经系统协同工作。有哪些 这些系统的心理和生物约束，以及它们在学习过程中如何相互作用？的 提出的实验旨在通过开发精确的计算模型来回答这些问题， 人类的工具学习，以及研究神经动力学，和相互作用， 个人学习过程。目标1将侧重于分离个人学习过程，进一步发展 一种新的人类工具学习模型，突出了从灵活的 执行工作记忆模块和增量强化学习模块。行为实验 和计算建模将被用来更好地描述这两个学习过程，重点是 它们是如何在瞬间和时间上相互作用的。Aim 2将使用脑刺激和 神经成像，以更好地表征支持每个学习过程的神经系统，以及 这些神经回路之间的相互作用这些结果将限制我们对神经系统的理解。 驱动人类工具性学习的机制。通过这个项目获得的知识将提供一个至关重要的 临床应用的框架，例如，在理解和治疗工作记忆相关的学习 精神分裂症的缺陷，以及帕金森病的强化学习缺陷。关键是，一个更精确的 个体学习过程的模型可以指导临床协议的开发， 当其他学习系统受到损害时，学习系统。最后，加强对 人类基于奖励的学习可以改善习惯形成的理论，这可能会进一步提供信息， 成瘾的心理学和神经生理学模型。

Project Summary Learning from rewards is one of the fundamental roles of the nervous system, allowing for beneficial behaviors to be repeated, and detrimental behaviors to be avoided. It has recently become clear that when humans learn from rewards in the environment, they rely on multiple neural systems that work in tandem. What are the psychological and biological constraints of these systems, and how do they interact during learning? The proposed experiments are designed to answer these questions by developing precise computational models of human instrumental learning, as well as investigating the neural dynamics of, and interactions between, individual learning processes. Aim 1 will focus on isolating individual learning processes, further developing a novel model of human instrumental learning that highlights contributions to learning from both a flexible executive working memory module and an incremental reinforcement learning module. Behavioral experiments and computational modeling will be used to better characterize these two learning processes, with a focus on how they interact instantaneously and over time. Aim 2 will use a combination of brain stimulation and neuroimaging to better characterize the neural systems supporting each learning process, as well as the putative interactions between these neural circuits. These results will constrain our understanding of the neural mechanisms that drive human instrumental learning. The knowledge gained by this project will provide a vital framework for clinical applications, for instance, in understanding and treating working memory related learning deficits in schizophrenia, and reinforcement learning deficits in Parkinson's disease. Critically, a more precise model of individual learning processes could guide the development of clinical protocols that leverage intact learning systems when other learning systems are compromised. Finally, an enhanced understanding of human reward-based learning could improve theories of habit formation, which may further inform psychological and neurophysiological models of addiction.