权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

A model system to study the interaction of multiple processes for motor learning

研究运动学习多个过程相互作用的模型系统

基本信息

批准号：
9334317
负责人：
JORDAN A TAYLOR
金额：
$ 34.1万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-16 至 2019-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9334317
关键词：
Affect Basal Ganglia Biological Models Brain Cerebellum Coffee Competence Complement Computer Analysis Computer Simulation Computing Methodologies Cues Decision Making Degenerative Disorder Development Disabled Persons Employee Strikes Employment Environment Feedback Future Goals Habits Human Hybrids Impairment Individual Individual Differences Instruction Joints Lateral Lead Learning Lesion Life Methods Modeling Motor Movement Movement Disorders Neurodegenerative Disorders Neurologic Neuropsychology Neurorehabilitation Neurosciences Outcome Pathology Patients Performance Phase Population Prefrontal Cortex Process Protocols documentation Psychological reinforcement Recruitment Activity Reporting Research Proposals Rewards Rotation Savings Sensory Shapes Space Models Stroke System Task Performances Testing Time Training Visual Work base design experimental study improved insight learning strategy motor learning motor skill learning neural circuit novel operation programs public health relevance relating to nervous system success visual motor

项目摘要

DESCRIPTION (provided by applicant): Humans possess a remarkable ability to learn motor tasks very quickly. Recent findings are beginning to discover that this ability is supported by multiple learning systems, each with their unique computational features. The goal of this research proposal is to characterize these learning processes at a computational and neural systems level. The signatures of at least three separate learning mechanisms have been identified. Reinforcement learning can relate task success with a particular action to better guide selection among competing action alternatives. A forward model can learn to make predictions about the sensory consequences of a selected action to improve motor execution. While these two processes are capable of refining performance, one based on reinforcement and the other from movement error, they entail a relatively slow, gradual process requiring extensive practice. A striking feature of human competence is the ability to employ explicit strategies that can facilitate learning at a much faster time scale. We hypothesize that these three processes are dependent on distinct neural circuits that can operate concurrently during a motor learning task. Each may operate with a significant degree of independence, yet they can, in certain circumstances, interact to converge of a common learning solution. We will exploit a simple learning task involving a visuomotor rotation to parametrically manipulate the relative contribution of these processes to motor learning. We propose that changes in task conceptualization and feedback are critical in determining the relative engagement of these processes. Empirical tests and computational modeling will provide a rigorous analysis of these hypotheses and to develop a process-based account of motor learning. The neuroanatomical substrates of these processes will be explored by testing neurological populations with degenerative disorders of the cerebellum or basal ganglia, and patients with cortical lesions affecting prefrontal regions. We assume that reinforcement learning, forward model adaptation, and strategy-based control are likely operative in nearly all motor learning tasks, but their individual contribution to learning has largely been overlooked. If learning is the weighted contribution of these processes, then differences in task information as well as individual differences in the exploitation of this information will influence the relative contribution to learning, even when the basic task remains unchanged. Ultimately, understanding how multiple systems contribute to learning should lead to the development of optimal training protocols either designed to target impaired systems or bias performance to rely on systems that are relatively intact.

描述（由申请人提供）：人类具有快速学习运动任务的非凡能力。最近的研究开始发现，这种能力得到了多个学习系统的支持，每个系统都有其独特的计算功能。本研究提案的目标是在计算和神经系统层面表征这些学习过程。至少三个独立学习机制的特征已被识别。强化学习可以将任务成功与特定行动联系起来，以更好地指导在相互竞争的行动方案中进行选择。前向模型可以学习对所选动作的感官后果进行预测，以提高运动执行力。虽然这两个过程能够提高表现，一个基于强化，另一个基于动作错误，但它们需要一个相对缓慢、渐进的过程，需要大量的练习。人类能力的一个显着特征是能够采用明确的策略来促进更快的学习。我们假设这三个过程依赖于可以在运动学习任务期间同时运行的不同神经回路。每个人都可以在很大程度上独立运作，但在某些情况下，他们可以相互作用以汇聚共同的学习解决方案。我们将利用一个涉及视觉运动旋转的简单学习任务来参数化地操纵这些过程对运动学习的相对贡献。我们认为任务概念化和反馈的变化对于确定这些流程的相对参与度至关重要。实证测试和计算模型将对这些假设进行严格分析，并开发基于过程的运动学习说明。这些过程的神经解剖学基础将通过测试患有小脑或基底神经节退行性疾病的神经群体以及影响前额叶区域的皮质病变的患者来探索。我们假设强化学习、正向模型适应和基于策略的控制可能在几乎所有运动学习任务中发挥作用，但它们对学习的个体贡献在很大程度上被忽视了。如果学习是这些过程的加权贡献，那么任务信息的差异以及利用这些信息的个体差异将影响对学习的相对贡献，即使基本任务保持不变。最终，了解多个系统如何促进学习应该有助于制定最佳的培训方案，要么旨在针对受损的系统，要么偏向依赖于相对完整的系统的表现。