Generalized prediction errors in the human cerebellum

人类小脑的广义预测误差

• 批准号: 10715334
• 负责人: Samuel David McDougle
• 金额: $ 41.88万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10715334
• 关键词: Address; Animals; Architecture; Association Learning; Behavior; Behavioral; Brain; Brain region; Cerebellar Diseases; Cerebellum; Clinical; Cognition; Computational Technique; Computer Analysis; Computer Models; Development; Disease; Disparate; Event; Feedback; Functional Magnetic Resonance Imaging; Health; Heterogeneity; Human; Individual; Investigation; Knowledge; Language; Learning; Link; Measures; Modeling; Motor; Movement; Neurons; Outcome; Pathology; Positioning Attribute; Process; Psychological reinforcement; Reflex action; Research; Rest; Rewards; Role; Sampling; Sensory; Short-Term Memory; Signal Transduction; Stimulus; Structure; Testing; Time; Visual; Work; cognitive control; cognitive task; design; experimental study; eyeblink conditioning; insight; learning network; motor control; motor learning; multimodality; neuroimaging; novel; sensory input; social cognition; statistical learning; theories

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.

项目概要 除了运动控制和学习之外，小脑还与认知密切相关。 旨在仔细检查小脑在非运动领域的作用，即强化学习和 我们发现该结构的感觉运动学习的核心计算可以是 推广到非运动环境中，了解小脑如何促进非运动学习至关重要。 – 这些知识将支持对小脑功能的新机制和临床见解的发展， 以及人类的一般学习。 基础理论工作描述了小脑如何成为监督的理想基质。 该理论做出了可检验的经验预测，并已在实验中得到证实。 动物使用巴甫洛夫眨眼调节和前庭眼反射适应等任务，揭示了 关于小脑感觉运动过程的详细事实，但是小脑在其他任务中的作用又如何呢？ 在这里，我们解决了这个问题。 计算技术来开发广义小脑学习计算的新框架。 该研究计划以三个具体目标为中心，在目标 1 中，我们使用计算引导的函数。 神经成像（fMRI）来检查小脑在强化学习中的作用我们测试了这个想法。 小脑处理奖励预测和预测误差，这是强化学习的核心计算。 我们还对小脑学习计算提出了限制，即该结构仅有助于 当事件之间的时间间隔很短（即亚秒）时进行学习，目标 2 采用类似的方法。 视觉统计学习领域，检查小脑的感觉预测和预测误差 并进一步测试所提出的时间约束 在目标 1-2 中，我们还测量了脑-小脑连接性。 将小脑置于更广泛的特定任务学习网络中，并询问脑-小脑是否 在目标 3 中，我们研究了小脑对非运动的因果贡献。 学习、测试大量患有小脑病理学的个体样本，并将他们的行为与 匹配的控制将用于检测和描述所利用的缺陷。 该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更广泛的作用提供新的见解。