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

• 批准号: 8614559
• 负责人: JORDAN A TAYLOR
• 金额: $ 34.13万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-16 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8614559
• 关键词: Accounting; 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; Individual; Individual Differences; Instruction; Joints; Lateral; Lead; Learning; Lesion; Life; Methods; Modeling; Motor; Movement; Movement Disorders; Neurodegenerative Disorders; Neurologic; Neurorehabilitation; Neurosciences; Outcome; Pathology; Patients; Performance; Phase; Population; Prefrontal Cortex; Process; Protocols documentation; Psychological reinforcement; Relative (related person); Reporting; Research Proposals; Rewards; Rotation; Savings; Sensory; Shapes; Solutions; Space Models; Stroke; System; Task Performances; Testing; Time; Training; Visual; Weight; Work; base; design; improved; insight; motor learning; motor skill learning; neural circuit; neuropsychological; novel; operation; programs; public health relevance; relating to nervous system; research study; success; visual motor

Project Summary 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.

项目概要 人类拥有快速学习运动任务的非凡能力。最近的发现开始 发现这种能力得到多个学习系统的支持，每个系统都有其独特的计算能力 特征。本研究提案的目标是在计算和分析过程中描述这些学习过程的特征。 神经系统层面。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，以更好地指导选择 竞争性行动替代方案。前向模型可以学习对感官后果进行预测 提高运动执行力的选定动作。虽然这两个过程能够精炼 表现，一种基于强化，另一种基于运动错误，它们需要相对缓慢的、 渐进的过程需要大量的练习。人类能力的一个显着特征是雇用的能力 可以促进更快时间尺度学习的明确策略。我们假设这三个 这些过程取决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 共同学习解决方案的汇聚。我们将利用涉及视觉运动旋转的简单学习任务 参数化地操纵这些过程对运动学习的相对贡献。我们建议 任务概念化和反馈的变化对于确定这些人员的相对参与度至关重要 流程。实证检验和计算模型将为这些假设提供严格的分析 并开发基于过程的运动学习帐户。这些的神经解剖学基础 将通过测试患有小脑退行性疾病的神经群体来探索过程 基底神经节，以及影响前额叶区域的皮质病变的患者。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中发挥作用 任务，但他们个人对学习的贡献在很大程度上被忽视了。如果学习是加权的 这些过程的贡献，然后任务信息的差异以及个体差异 利用这些信息，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导致 制定最佳训练方案，要么旨在针对受损系统，要么偏向性能 依赖相对完整的系统。