Computations in Human Motor Learning

人类运动学习中的计算

• 批准号: 10560638
• 负责人: Daniel Wolpert
• 金额: $ 49.14万
• 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/10560638
• 关键词: 3-Dimensional; Address; Affect; Appearance; Belief; Biological Assay; Brain; Cues; Custom; Data; Degenerative Disorder; Development; Disease; Environment; Experimental Designs; Family; Feedback; Geometry; Goals; Golf; Height; Human; Impairment; Individual; Knowledge; Laboratories; Lead; Learning; Length; Longevity; Maps; Memory; Modeling; Modification; Motion; Motor; Movement; Muscle; Nervous System; 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; behavioral study; computer framework; developmental disease; experience; improved; innovation; motor behavior; motor control; motor learning; nervous system disorder; neural; novel; 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的基础计算， 从而提供了一个框架，以检查学习的神经基础，在神经系统中看到的缺陷， 以及如何在康复中利用学习机制。运动学习是 涉及与环境相互作用引起的运动行为变化的过程。人类花费 终生学习、储存和提炼大量适合不同情境的运动记忆。电流 运动学习的研究几乎完全集中在单独的个体记忆的适应上。逆 然而，大脑如何协调其记忆库的基本原则在很大程度上是未知的。 我们的关键假设是，上下文推理的过程，估计每一个存在的概率- ing运动记忆是适合当前的情况，控制新的记忆的创建和程度， 不同的存储器被表达和更新到其上。我们的目标是了解是什么导致了- 与现有运动记忆的修改相比，新记忆的产生，以及现有记忆如何 被召回和更新。我们已经开发了COIN（上下文推理）模型来形式化的作用， 运动学习中的语境推理COIN模型以更有原则的方式执行上下文推理， 比以往任何模型都更全面，可以解释传统上归因于适应的关键发现 而是由上下文推理引起的，如自发恢复、储蓄、顺行干扰 和学习率的变化。与当前模型相比，COIN模型的一个关键特征是，它可以 以原则性的方式确定是否应该创建新的存储器或修改现有的存储器。到 测试和开发模型，我们将使用新型机器人接口对人类进行行为研究， 虚拟现实允许我们在运动学习任务期间控制参与者的感觉运动体验。在Aim中 我们将确定新的运动记忆产生的条件。在目标2中，我们将确定 更新现有运动记忆的规则。虽然目标1和2侧重于实现 平面，使大量的先前的研究可比，目标3走向更自然的任务， 在三维空间中操纵物体。在目标3中，我们将确定运动记忆是如何组织成家族的。 从而允许对共享相似属性的上下文进行有效的学习和概括。随意运动 是人类生存的基础，但许多疾病，如中风，退行性疾病，和发育 疾病，损害人的运动寿命。通过建立一个新的运动学习框架， 该项目将有助于我们的最终目标，即开发分析方法，以了解神经系统疾病的缺陷 并发展能够控制语境推理过程的范式，以改善康复。