Computations in human motor learning

人类运动学习中的计算

• 批准号: 10210632
• 负责人: Daniel Wolpert
• 金额: $ 50.08万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10210632
• 关键词: 3-Dimensional; Address; Affect; Appearance; Belief; Biological Assay; Brain; Coin; Cues; Custom; Data; Degenerative Disorder; Development; Disease; Environment; Family; Feedback; Geometry; Goals; Golf; Height; Human; Impairment; Individual; Knowledge; Laboratories; Lead; Learning; Length; Longevity; Memory; Modeling; Modification; Motion; Motor; Movement; Muscle; Nervous system structure; Outcome; Participant; Persons; Play; Probability; Process; Property; Racquet Sports; Recording of previous events; Recovery; Rehabilitation therapy; Research; Robotics; Role; Savings; Sensory; Squash; Stream; Stroke; Tennis; Testing; Texture; Time; Update; Visual; Weight; Work; arm; arm movement; base; behavioral study; computer framework; design; developmental disease; experience; experimental study; improved; innovation; memory recall; motor behavior; motor control; motor learning; nervous system disorder; novel; relating to nervous system; sensory input; statistics; tool; virtual reality

Project Summary The long-term goal of our laboratory is to understand the computations underlying human motor learning and thereby provide a framework to examine the neural underpinnings of learning, the deficits seen in neurological disorders and how learning mechanisms can be leveraged in rehabilitation. Motor learning is the fundamental process that involves changes in motor behavior arising from interaction with the environment. Humans spend a lifetime learning, storing and refining a multitude of motor memories appropriate for different contexts. Current studies of motor learning have focused almost exclusively on adaptation of individual memories in isolation. Con- sequently, the principles underlying how the brain coordinates its repertoire of memories are largely unknown. Our key hypothesis is that the process of contextual inference, estimating the probability with which each exist- ing motor memory is appropriate for the current situation, controls the creation of new memories and the degree to which different memories are expressed and updated. Our objective is to understand what leads to the cre- ation of new memories compared to the modification of existing motor memories, and how existing memories are recalled and updated. We have developed the COIN (COntextual INference) model to formalize the role of contextual inference in motor learning. The COIN model performs contextual inference in a more principled and comprehensive way than any previous model and can explain key findings traditionally attributed to adaptation as arising instead from contextual inference, such as spontaneous recovery, savings, anterograde interference and changes in learning rates. In contrast to current models, a critical feature of the COIN model is that it can determine, in a principled manner, whether a new memory should be created or existing memories adapted. To both test and develop the model, we will use behavioral studies in humans using novel robotic interfaces and virtual reality which allow us to control a participant’s sensorimotor experience during motor learning tasks. In Aim 1 we will determine the conditions under which new motor memories are created. In Aim 2 we will determine the rules by which existing motor memories are updated. While Aims 1 and 2 focus on reaching movements in the plane which make a large body of previous research comparable, Aim 3 moves towards more naturalistic tasks of manipulating objects in three-dimensions. In Aim 3 we will determine how motor memories are organized into fam- ilies to allow efficient learning and generalization for contexts that share similar properties. Voluntary movement is fundamental to human existence, yet many diseases such as stroke, degenerative disease, and developmental disorders, impair human movement over the life span. By establishing a new framework of motor learning, this project will contribute to our ultimate goal of developing assays to understand deficits in neurological disorders and develop paradigms that can control the contextual inference process so as to improve rehabilitation.

项目摘要 我们实验室的长期目标是了解人类运动学习和 因此，提供了一个框架来检查学习的神经元基础，即神经学中的缺陷 疾病以及如何在康复中利用学习机制。运动学习是基本的 过程涉及与环境相互作用引起的运动行为变化。人类花费 一生的学习，存储和完善适合不同环境的许多电机记忆。当前的 对运动学习的研究几乎完全集中在孤立的单个记忆的适应上。 con 顺便说一句，大脑如何协调其记忆曲目的原理在很大程度上是未知的。 我们的关键假设是，上下文推断的过程，估计每种存在的概率 - 电动机记忆适合当前情况，控制新记忆的创建和学位 表达和更新的不同记忆。我们的目标是了解导致Cre-的原因 与现有电动机记忆的修改以及现有记忆的修改相比 被召回和更新。我们已经开发了硬币（上下文推论）模型，以形式化 运动学习中的上下文推断。硬币模型对更校长和 全面的方式比任何以前的模型都可以解释传统上归因于适应的关键发现 而是由上下文推断产生的，例如赞助商恢复，储蓄，顺行干扰 和学习率的变化。与当前模型相反，硬币模型的关键特征是它可以 以主要的方式确定应该创建新内存还是适应现有的记忆。到 测试和开发模型，我们将使用新型的机器人界面和 虚拟现实使我们能够在运动学习任务中控制参与者的感觉运动体验。目标 1我们将确定创建新电动机记忆的条件。在AIM 2中，我们将确定 现有电动机记忆更新的规则。而目标1和2专注于在 飞机使大量以前的研究可比，目标3向更自然的任务进行 在三维中操纵对象。在AIM 3中，我们将确定如何将电机记忆组织到FAM中 - 为共享类似属性的上下文允许有效学习和概括。自愿运动 是人类生存的基础，但许多疾病，例如中风，退化性疾病和发育性 疾病，损害人类运动在整个生命周期中。通过建立一个新的运动学习框架 项目将有助于我们开发测定法以了解神经系统疾病缺陷的最终目标 并开发可以控制上下文推理过程以改善康复的范例。