运动学习。
Motor learning.
机构信息
Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK.
出版信息
Curr Biol. 2010 Jun 8;20(11):R467-72. doi: 10.1016/j.cub.2010.04.035.
Although learning a motor skill, such as a tennis stroke, feels like a unitary experience, researchers who study motor control and learning break the processes involved into a number of interacting components. These components can be organized into four main groups. First, skilled performance requires the effective and efficient gathering of sensory information, such as deciding where and when to direct one's gaze around the court, and thus an important component of skill acquisition involves learning how best to extract task-relevant information. Second, the performer must learn key features of the task such as the geometry and mechanics of the tennis racket and ball, the properties of the court surface, and how the wind affects the ball's flight. Third, the player needs to set up different classes of control that include predictive and reactive control mechanisms that generate appropriate motor commands to achieve the task goals, as well as compliance control that specifies, for example, the stiffness with which the arm holds the racket. Finally, the successful performer can learn higher-level skills such as anticipating and countering the opponent's strategy and making effective decisions about shot selection. In this Primer we shall consider these components of motor learning using as an example how we learn to play tennis.
虽然学习一项运动技能,如网球挥拍,感觉像是一个整体的体验,但研究运动控制和学习的研究人员将涉及的过程分解为许多相互作用的组成部分。这些组件可以组织成四个主要组。首先,熟练的表现需要有效地收集感官信息,例如决定何时何地在球场上注视,因此技能获取的一个重要组成部分涉及学习如何最好地提取与任务相关的信息。其次,执行者必须学习任务的关键特征,如网球拍和球的几何形状和力学、球场表面的特性以及风如何影响球的飞行。第三,运动员需要设置不同类别的控制,包括预测和反应控制机制,这些机制生成适当的运动命令以实现任务目标,以及合规控制,例如指定手臂握持球拍的刚度。最后,成功的运动员可以学习更高层次的技能,如预测和对抗对手的策略以及做出有效的击球选择决策。在本基础读物中,我们将使用学习打网球的例子来考虑运动学习的这些组成部分。