Generalized prediction errors in the human cerebellum

项目摘要 除了运动控制和学习，小脑与认知密切相关。这个项目是 旨在仔细研究小脑在非运动领域的作用，即强化学习和 统计学习我们假设，该结构的核心计算的感觉运动学习可以是 一般化到非运动情境。了解小脑如何促进非运动学习是至关重要的 - 这些知识将支持对小脑功能的新的机制和临床见解的发展， 和人类的学习。 基础理论工作已经描述了小脑是如何典型地成为监督的理想基底的。 运动学习这一理论提出了可检验的经验性预测，这些预测已在1997年的实验中得到证实。 动物使用任务，如巴甫洛夫眨眼条件反射和前庭眼反射适应，揭示 关于小脑感觉运动过程的细节。但是小脑在其他任务中的作用呢 域名？我们在这里解决这个问题。拟议的工作整合了行为，神经成像， 计算技术，以开发一个新的框架，广义小脑学习计算。 研究计划围绕三个具体目标。在目标1中，我们使用计算引导的泛函 神经影像学（fMRI）检查小脑在强化学习中的作用。我们检验了 小脑处理奖励预测和预测错误，这是强化学习的核心计算。 我们还讨论了小脑学习计算的限制，即结构只有助于 学习事件之间的时间间隔何时短暂（即，亚秒）。Aim 2采用类似的方法， 视觉统计学习领域，检查小脑中的感觉预测和预测错误 以及进一步测试所提出的定时约束。在目标1-2中，我们还测量了小脑-小脑连接 将小脑定位在更广泛的特定任务的学习网络中，并询问小脑是否 连接性与行为是协变的。在目标3中，我们研究了小脑对非运动神经元的因果作用。 学习，测试大样本的个人与小脑病理和对比他们的行为与 匹配的控制。将使用计算分析来检测和表征假设的缺陷。这 一个项目提出了一个新的框架来理解小脑对非运动学习的贡献 并将为小脑在健康和疾病中的更广泛作用提供新的见解。

Project Summary In addition to motor control and learning, the cerebellum is intimately linked to cognition. This project is designed to closely examine the cerebellum's role in nonmotor domains, namely, reinforcement learning and statistical learning. We hypothesize that the structure's core computations for sensorimotor learning can be generalized to nonmotor contexts. It is critical to understand how the cerebellum contributes to nonmotor learning – this knowledge will support the development of novel mechanistic and clinical insights into cerebellar function, and human learning in general. Foundational theoretical work has described how the cerebellum typifies an ideal substrate for supervised motor learning. This theory made testable empirical predictions that have been borne out in experiments in animals using tasks such as Pavlovian eyeblink conditioning and vestibular-ocular reflex adaptation, revealing facts about cerebellar sensorimotor processes in exquisite detail. But what about a cerebellar role in other task domains? Here we address this question. The proposed work integrates behavioral, neuroimaging, and computational techniques to develop a new framework for generalized cerebellar learning computations. The research plan centers on three Specific Aims. In Aim 1 we use computationally guided functional neuroimaging (fMRI) to examine the role of the cerebellum in reinforcement learning. We test the idea that the cerebellum processes reward predictions and prediction errors, the core computations of reinforcement learning. We also posit a constraint on cerebellar learning computations, namely that the structure only contributes to learning when the temporal interval between events is brief (i.e., subsecond). Aim 2 takes a similar approach to the domain of visual statistical learning, examining sensory predictions and prediction errors in the cerebellum and further testing the proposed timing constraint. In Aims 1-2 we also measure cerebro-cerebellar connectivity to position the cerebellum within broader task-specific learning networks, and to ask if cerebro-cerebellar connectivity covaries with behavior. In Aim 3 we examine causal contributions of the cerebellum to nonmotor learning, testing a large sample of individuals with cerebellar pathology and contrasting their behavior with matched controls. Computational analyses will be used to detect and characterize the hypothesized deficits. This project proposes a new framework for understanding the contributions of the cerebellum to nonmotor learning and will provide new insight into the broader role of the cerebellum in health and disease.