Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.
Ergonomics. 2013;56(4):612-22. doi: 10.1080/00140139.2012.750383. Epub 2013 Feb 5.
Fitts' law cannot be used to predict movement times (MTs) of bimanual tasks since no empirical relationships associating task difficulty and bimanual MT have been demonstrated yet. Development of a 'bimanual task difficulty index' has been challenged by the complex patterns of coordination involved in simultaneously performing two tasks, one with each hand, under a control system with limited visual and attentional resources. To address this fundamental issue in human motor performance, bimanual object transfers with the left and right hands to targets of various precision requirements and separated by different distances were studied in six healthy subjects. Visual resource allocation during task performance was used to identify 'primary' and 'secondary' hand movements in bimanual tasks. While the primary movement was similar to a unimanual movement, the secondary MT varied with its own, as well as the contralateral hand's task constraints. These results, which were stable and consistent across six subjects, provide preliminary evidence that visual behaviour, indicating closed-loop control, can be used to systematically derive bimanual MTs.
A simple extension of Fitts' law cannot be used to predict movement times (MTs) of bimanual tasks since there is no consensus on the definition of a 'bimanual task difficulty index' in the literature. In this paper, we have approached this problem by using visual resource allocation patterns to systematically derive bimanual MTs.
菲茨定律不能用于预测双手任务的运动时间 (MTs),因为尚未证明任务难度与双手 MT 之间存在经验关系。由于涉及同时用双手执行两个任务的协调复杂模式,以及在视觉和注意力资源有限的控制系统下,开发“双手任务难度指数”一直具有挑战性。为了解决人类运动表现中的这个基本问题,我们研究了 6 名健康受试者用左手和右手进行的双手物体转移,目标具有不同的精度要求,并且彼此之间的距离不同。在任务执行过程中,视觉资源分配用于识别双手任务中的“主要”和“次要”手部运动。虽然主要运动类似于单手运动,但次要 MT 会根据其自身以及对侧手的任务限制而变化。这些结果在六个受试者中是稳定且一致的,初步表明视觉行为(表示闭环控制)可用于系统地推导双手 MT。
简单扩展的菲茨定律不能用于预测双手任务的运动时间 (MTs),因为文献中对于“双手任务难度指数”的定义没有共识。在本文中,我们通过使用视觉资源分配模式来系统地推导双手 MT,从而解决了这个问题。