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: 8735203
• 负责人: JORDAN A TAYLOR
• 金额: $ 33.91万
• 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/8735203
• 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

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）：人类具有非常快速地学习运动任务的非凡能力。最近的研究结果开始发现，这种能力是由多个学习系统支持的，每个学习系统都有其独特的计算功能。这项研究计划的目标是在计算和神经系统水平上描述这些学习过程。至少有三个独立的学习机制的签名已被确定。强化学习可以将任务成功与特定动作联系起来，以更好地指导 在竞争性行为选择中进行选择。前向模型可以学习预测选定动作的感官后果，以改善运动执行。虽然这两个过程都能够改善性能，一个基于强化，另一个来自运动错误，但它们需要一个相对缓慢，渐进的过程，需要大量的练习。人类能力的一个显著特点是能够采用明确的策略，可以促进学习在更快的时间尺度。我们假设，这三个过程是依赖于不同的神经回路，可以同时在运动学习任务。每一个都可以在很大程度上独立运作，但在某些情况下，它们可以相互作用，以汇集一个共同的学习解决方案。我们将利用一个简单的学习任务，涉及视觉旋转参数操纵这些过程的相对贡献运动学习。我们建议，在任务概念化和反馈的变化是至关重要的，在确定这些过程的相对参与。实证测试和计算模型将提供这些假设的严格分析，并制定一个基于过程的运动学习帐户。这些过程的神经解剖学基础将通过测试小脑或基底神经节退行性疾病的神经系统人群，以及影响前额叶区域的皮质病变患者进行探索。我们假设强化学习、前向模型适应和基于策略的控制可能在几乎所有的运动学习任务中都有效，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，了解多个系统如何有助于学习应该会导致最佳训练协议的开发，该协议旨在针对受损的系统或使性能偏向于依赖相对完整的系统。