Ranganathan Rajiv, Scheidt Robert A
Department of Kinesiology, Michigan State, 203 IM Circle, East Lansing, Michigan, 48824, USA.
Department of Biomedical Engineering, Marquette University, Olin Engineering Center, Room 206, P.O. Box 1881, Milwaukee, Wisconsin, 53201, USA.
Adv Exp Med Biol. 2016;957:327-349. doi: 10.1007/978-3-319-47313-0_18.
Understanding how the nervous system learns to coordinate the large number of degrees of freedom in the body to produce goal-directed movement is not only one of the central questions in theoretical movement neuroscience, but also has direct relevance for movement rehabilitation. In spite of the centrality of this issue, the literature on how a new coordination pattern is acquired and refined when first learning a novel task remains surprisingly small relative to studies that focus on modifications of already well-learned coordination patterns. In this chapter, we outline some of the reasons behind why the study of coordination continues to pose a serious challenge for movement neuroscience, particularly when it comes to systematically studying and testing hypotheses on how new coordination patterns are organized and reorganized with practice. We then describe a novel experimental paradigm-the body-machine interface (BoMI)-that has been developed and used over the last decade to examine this issue. The paradigm combines the control of a large number of degrees of freedom along with a linear mapping, which makes it appealing to examine how coordination of these high degrees of freedom is organized in a systematic fashion. Finally, we outline some of the new insights that this paradigm has provided into classic issues of motor learning such as the learning of high-dimensional spaces, generalization, and transfer.
理解神经系统如何学会协调身体中大量的自由度以产生目标导向的运动,不仅是理论运动神经科学的核心问题之一,而且与运动康复直接相关。尽管这个问题至关重要,但相对于专注于对已经熟练掌握的协调模式进行修改的研究而言,关于首次学习新任务时如何获得和完善新的协调模式的文献仍然少得惊人。在本章中,我们概述了为何协调研究继续对运动神经科学构成严峻挑战的一些原因,特别是在系统地研究和检验关于新的协调模式如何通过练习进行组织和重组的假设方面。然后,我们描述一种新颖的实验范式——身体-机器接口(BoMI),它是在过去十年中开发并用于研究这个问题的。该范式将对大量自由度的控制与线性映射相结合,这使得研究这些高自由度的协调如何以系统方式组织变得很有吸引力。最后,我们概述了这个范式为运动学习的经典问题,如高维空间学习、泛化和迁移等,所提供的一些新见解。