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.

项目摘要 除运动控制和学习外，小脑与认知密切相关。这个项目是 旨在密切检查小脑在非运动领域的作用，即增强学习和 统计学习。我们假设该结构的感觉运动学习的核心计算可以是 概括为非运动环境。了解小脑如何有助于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型机械和临床见解的发展， 和人类学习。 基础理论工作描述了小脑如何为监督的理想底物代表 运动学习。该理论做出了可检验的经验预测，这些预测是在实验中出生的 动物使用诸如Pavlovian Eykblink调节和前庭 - 眼反射适应的任务，揭示 有关小脑感觉运动过程的事实，可独家详细。但是小脑在其他任务中的角色呢 域？在这里，我们解决了这个问题。拟议的工作整合了行为，神经影像和 计算技术为通用小脑学习计算开发新框架。 研究计划集中在三个具体目标上。在AIM 1中，我们使用计算引导的功能 神经影像学（fMRI）检查小脑在增强学习中的作用。我们测试了 小脑过程奖励预测和预测错误，这是强化学习的核心计算。 我们还支持对小脑学习计算的限制，即该结构仅有助于 学习何时事件之间的临时间隔是简短的（即子代数）。 AIM 2采用类似的方法 视觉统计学习的领域，检查小脑的感觉预测和预测错误 并进一步测试提出的时序约束。在目标1-2中，我们还测量了脑脑连通性 将小脑放置在更广泛的特定任务学习网络中，并询问脑脑脑是否存在 连通性协变与行为。在AIM 3中，我们检查小脑对非运动的因果贡献 学习，测试大量小脑病理的个体样本，并将其行为与 匹配的控件。计算分析将用于检测和表征假设的缺陷。这 项目提案是一个新的框架，用于了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。