Sensory and motor plasticity in learning

学习中的感觉和运动可塑性

• 批准号: RGPIN-2019-06684
• 负责人: Henriques, Denise
• 金额: $ 3.42万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689571
• 关键词: Sensory; motor; plasticity; learning

The motor-control systems in our brain are remarkably good at rapidly adapting to continuous changes in our environment and changes in our body. Yet, the mechanisms that underlie such sensorimotor learning remain largely unknown. The aims of this proposal are to 1) better understand the sources of information used to flexibly learn and adapt movements, 2) to explore processes in the brain that underlie sensorimotor learning, and 3) to investigate how this extends to interacting with tools and other objects.*******Being able to adapt to changes in our environment and body relies on our ability to estimate the location of our limbs, in particular, our hand. This is called state estimation and is a key element in all models of motor control. A state estimate combines sensory feedback with information related to outgoing motor commands (efferent copies) used by forward models in the brain to predict the sensory consequences of the outgoing motor command. Yet, most models of motor control assume that adaptation exclusively results from updates in prediction, but not updates in perception. Work from my lab shows that proprioceptive estimates of the hand do change during learning and partly account for changes in motor performance. To better understand how and to what extent both proprioception and prediction contribute to motor learning, we have developed a method for isolating their distinct contributions. In the first aim of the proposed research, we will test the sensitivity of each to multiple factors known to impact motor learning, as well as measure and model their separate time courses throughout learning. In the second aim, we will complement these behavioural and modeling studies with electroencephalography (EEG) to identify brain activity reflecting ongoing processing of sensory and prediction errors and the resulting changes in state estimation and motor planning throughout motor adaptation. Motor learning does not only involve forming and updating forward models of our effectors, but also building motor-related representations of objects and tools we interact with. Yet, this aspect of learning has been largely overlooked even though the diversity and complexity of human movements involves the ability to predict and skillfully manipulate objects. Thus, for our third aim we use 3D virtual reality (VR) technology to manipulate the normal physics associated with operating familiar tools and objects to characterize tool-skill acquisition.****The proposed research will expand our understanding of human sensorimotor learning, including the underlying brain activity and skilled tool-use. This in turn could lead to improved rehabilitation, better human-machine interfaces, and better motor-skills training for all levels of performance.******

我们大脑中的运动控制系统非常擅长快速适应环境的持续变化和身体的变化。然而，这种感觉运动学习背后的机制仍然很大程度上未知。该提案的目的是 1) 更好地了解用于灵活学习和适应动作的信息来源，2) 探索大脑中感觉运动学习的过程，3) 研究如何将其扩展到与工具和其他物体的交互。 ******能够适应我们的环境和身体的变化依赖于我们估计四肢位置的能力，特别是我们的手的位置。这称为状态估计，是所有电机控制模型的关键要素。状态估计将感觉反馈与大脑中的前向模型使用的传出运动命令（传出副本）相关的信息相结合，以预测传出运动命令的感觉后果。然而，大多数运动控制模型都假设适应完全来自预测的更新，而不是感知的更新。我实验室的工作表明，手的本体感觉估计在学习过程中确实会发生变化，并在一定程度上解释了运动表现的变化。为了更好地了解本体感觉和预测如何以及在多大程度上对运动学习做出贡献，我们开发了一种方法来分离它们的独特贡献。在拟议研究的第一个目标中，我们将测试每个因素对已知影响运动学习的多个因素的敏感性，并在整个学习过程中测量和建模它们各自的时间过程。在第二个目标中，我们将用脑电图（EEG）来补充这些行为和建模研究，以识别反映感觉和预测误差持续处理的大脑活动，以及整个运动适应过程中状态估计和运动规划的变化。运动学习不仅涉及形成和更新效应器的前向模型，还涉及构建与我们交互的对象和工具的运动相关表示。然而，尽管人类运动的多样性和复杂性涉及预测和巧妙操纵物体的能力，但学习的这一方面在很大程度上被忽视了。因此，对于我们的第三个目标，我们使用 3D 虚拟现实 (VR) 技术来操纵与操作熟悉的工具和物体相关的正常物理现象，以表征工具技能的获取。****拟议的研究将扩大我们对人类感觉运动学习的理解，包括潜在的大脑活动和熟练的工具使用。这反过来又可以改善康复、更好的人机界面以及针对各个表现水平的更好的运动技能培训。******