Witney Alice G
Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK.
Hum Mov Sci. 2004 Nov;23(5):747-70. doi: 10.1016/j.humov.2004.10.009.
Co-ordinated bi-manual actions form the basis for many everyday motor skills. In this review, the internal model approach to the problem of bi-manual co-ordination is presented. Bi-manual coordinative tasks are often regarded as a hallmark of complex action. They are often associated with object manipulation, whether the holding of a single object between the two hands or holding an object in each hand. However, the task of movement and control is deceptively difficult even when we execute an action with a single hand without holding an object. The simplest voluntary action requires the problems of co-ordination, timing and interaction between neural, muscular and skeletal structures to be overcome. When we are making a movement whilst holding an object, a further requirement is that an internal model is able to predict the dynamics of the object that is being held as well as the dynamics of the motor system. There has been extensive work examining the formation of internal models when acting in novel environments. The majority of studies examine uni-lateral learning of a task generally to the participant's dominant hand. However, many everyday motor tasks are bi-manual, and the existing findings regarding the learning of internal models in uni-manual tasks and their subsequent generalization highlights the complexities that must underlie the formation of bi-manual tasks. Our ability to perform bi-manual tasks raises interesting questions about how internal models are specified for co-ordinative actions, and also for how the motor system learns to represent the properties of objects.
协调的双手动作构成了许多日常运动技能的基础。在这篇综述中,我们介绍了用于解决双手协调问题的内部模型方法。双手协调任务通常被视为复杂动作的一个标志。它们常常与物体操作相关,无论是双手握住单个物体还是每只手各持一个物体。然而,即使我们空手用单只手执行动作,运动和控制任务也极具欺骗性,十分困难。最简单的随意动作都需要克服神经、肌肉和骨骼结构之间的协调、时间安排和相互作用等问题。当我们手持物体进行运动时,还需要一个内部模型能够预测所持物体的动力学以及运动系统的动力学。在新环境中行动时,已经有大量关于内部模型形成的研究工作。大多数研究通常考察参与者优势手对任务的单侧学习。然而,许多日常运动任务是双手的,并且现有的关于单手任务中内部模型学习及其后续泛化的研究结果凸显了双手任务形成背后必然存在的复杂性。我们执行双手任务的能力引发了一些有趣的问题,比如如何为协调动作指定内部模型,以及运动系统如何学会表征物体的属性。