Zhang Wei, Scholz John P, Zatsiorsky Vladimir M, Latash Mark L
Department of Kinesiology, Pennsylvania State University, University Park, PA 16802, USA.
J Neurophysiol. 2008 Feb;99(2):500-13. doi: 10.1152/jn.01029.2007. Epub 2007 Nov 28.
We used the framework of the uncontrolled manifold (UCM) hypothesis to explore changes in the structure of variability in multifinger force-production tasks when a secondary task was introduced. Healthy young subjects produced several levels of the total force by pressing with the four fingers of the hand on force sensors. The frame with the sensors rested on the table (Stable condition) or on a narrow supporting beam (Unstable conditions) that could be placed between different finger pairs. Most variance in the finger mode space was compatible with a fixed value of the total force across all conditions, whereas the patterns of sharing of the total force among the fingers were condition dependent. Moment of force was stabilized only in the Unstable conditions. The finger mode data were projected onto the UCM computed for the total force and subjected to principal component (PC) analysis. Two PCs accounted for >90% of the variance. The directions of the PC vectors varied across subjects in the Stable condition, whereas two "default" PCs were observed under the Unstable conditions. These observations show that different persons coordinate their fingers differently in force-production tasks. They converge on similar solutions when an additional constraint is introduced. The use of variable solutions allows avoiding a loss in accuracy of performance when the same elements get involved in another task. Our results suggest a mechanism underlying the principle of superposition suggested in a variety of human and robotic studies.
我们使用非控制流形(UCM)假设的框架,来探究在引入次要任务时,多手指力产生任务中变异性结构的变化。健康的年轻受试者通过用手的四个手指按压力传感器来产生几个水平的总力。带有传感器的框架放置在桌子上(稳定条件)或放置在可置于不同手指对之间的狭窄支撑梁上(不稳定条件)。在所有条件下,手指模式空间中的大多数方差都与总力的固定值相符,而手指间总力的分配模式则取决于条件。仅在不稳定条件下力矩才得以稳定。将手指模式数据投影到为总力计算的UCM上,并进行主成分(PC)分析。两个主成分解释了超过90%的方差。在稳定条件下,主成分向量的方向因受试者而异,而在不稳定条件下观察到两个“默认”主成分。这些观察结果表明,不同的人在力产生任务中对手指的协调方式不同。当引入额外约束时,他们会趋向于类似的解决方案。当相同的元素参与另一项任务时,使用可变解决方案可以避免性能准确性的损失。我们的结果提示了在各种人类和机器人研究中所提出的叠加原理背后的一种机制。