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

项目摘要 人类拥有快速学习运动任务的非凡能力。最近的研究结果开始 我发现这种能力是由多个学习系统支持的，每个系统都有其独特的计算能力。 功能.这项研究计划的目标是在一个计算和 神经系统水平。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，以更好地指导选择 竞争行为替代品。前向模型可以学习对感官后果做出预测 来改善运动执行力。虽然这两个过程能够提炼 性能，一个基于强化，另一个来自运动错误，它们需要相对缓慢， 这是一个渐进的过程，需要广泛的实践。人类能力的一个显著特征是能够 明确的策略，可以促进学习在更快的时间尺度。我们假设这三个人 这些过程依赖于不同的神经回路，这些神经回路可以在运动学习任务期间同时操作。 每一个组织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， 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任务的相对参与度至关重要。 流程.实证检验和计算模型将提供这些假设的严格分析 并发展出一种基于过程的运动学习理论。这些神经解剖学基础 将通过测试患有小脑退行性疾病的神经系统人群来探索这一过程， 基底神经节，以及影响前额区域的皮质病变患者。我们假设， 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中起作用 任务，但他们对学习的个人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，然后在任务信息的差异，以及个人差异， 利用这些信息，将影响学习的相对贡献，即使基本任务 保持不变。最终，了解多系统如何有助于学习应该导致 开发最佳的训练方案，旨在针对受损的系统或偏差性能， 依赖于相对完整的系统。

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.