Body-goal variability mapping in an aiming task
Body-goal variability mapping in an aiming task
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DOI：
10.1007/s00422-006-0052-1
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发表时间：
2006-05-01
影响因子：
1.9
通讯作者：
Cesari, P
中科院分区：
文献类型：
--
作者：
Cusumano, JP;Cesari, P
鉴于人体中关节和肌肉的数量，通常存在无限数量的方式来执行相同的动作，定向运动的特征被称为等性（equifinality）（伯恩斯坦，运动的协调和调节，牛津，佩加蒙，1967）。在这里，我们提出了一种新类型的性能分析的基础上的身体目标的变异映射的想法。我们展示了如何自然产生这种映射的想法，从理论上定义了一个任务的目标函数，并在存在的equifinality，确定了所有可能的任务解决方案的策略，目标等价流形（GEM）。该方法还产生估计的敏感性目标水平的错误，身体水平的扰动，我们推导出一个通用的公式表达两者之间的关系。我们将这些想法的冗余运动学数据的分析对象进行瞄准任务进行了激光指针和没有。它示出，为了表征性能必须考虑两个因素，除了身体的变异性：第一，身体的变异性和创业板之间的对齐程度;和第二，控制目标相关的身体变异性的程度放大的目标的灵敏度参数。这两个因素都可以使用估计的身体目标映射来计算。我们表明，三种条件下，涉及两种不同的标称姿势和两种不同的感官条件（激光/无激光）的性能可以通过检查与地图相关联的方向敏感性参数平面中的数据聚类进行分类。
Given the number of joints and muscles in the human body, there are typically an infinite number of ways to perform the same action, a feature of directed movements known as equifinality (Bernstein, The coordination and regulation of movements, Oxford, Pergamon, 1967). Here we present a new type of performance analysis based on the idea of a body-goal variability mapping. We show how this mapping arises naturally from the idea of a goal function that theoretically defines a task and, in the presence of equifinality, determines the set of all possible task solution strategies, the goal equivalent manifold (GEM). The approach also yields estimates of the sensitivity of goal-level errors to body-level perturbations, and we derive a general formula expressing the relationship between the two. We apply these ideas to the analysis of redundant kinematic data from subjects performing an aiming task carried out with and without a laser pointer. It is shown that in order to characterize performance one must consider two factors in addition to the body variability: first, the degree of alignment between body variability and the GEM; and second, the sensitivity parameters that control the degree to which goal-relevant body variability is amplified at the target. Both of these factors can be computed using the estimated body-goal mapping. We show that the performance for three conditions involving two different nominal postures and two different sensory conditions (laser/no laser) can be classified by examining the clustering of data in an orientation- sensitivity parameter plane associated with the map.