Cohen R G, Sternad D
Department of Psychology, The Pennsylvania State University, University Park, PA 16802, USA.
Exp Brain Res. 2009 Feb;193(1):69-83. doi: 10.1007/s00221-008-1596-1. Epub 2008 Oct 25.
Variability in motor performance decreases with practice but is never entirely eliminated, due in part to inherent motor noise. The present study develops a method that quantifies how performers can shape their performance to minimize the effects of motor noise on the result of the movement. Adopting a statistical approach on sets of data, the method quantifies three components of variability (tolerance, noise, and covariation) as costs with respect to optimal performance. T-Cost quantifies how much the result could be improved if the location of the data were optimal, N-Cost compares actual results to results with optimal dispersion at the same location, and C-Cost represents how much improvement stands to be gained if the data covaried optimally. The TNC-Cost analysis is applied to examine the learning of a throwing task that participants practiced for 6 or 15 days. Using a virtual set-up, 15 participants threw a pendular projectile in a simulated concentric force field to hit a target. Two variables, angle and velocity at release, fully determined the projectile's trajectory and thereby the accuracy of the throw. The task is redundant and the successful solutions define a nonlinear manifold. Analysis of experimental results indicated that all three components were present and that all three decreased across practice. Changes in T-Cost were considerable at the beginning of practice; C-Cost and N-Cost diminished more slowly, with N-Cost remaining the highest. These results showed that performance variability can be reduced by three routes: by tuning tolerance, covariation and noise in execution. We speculate that by exploiting T-Cost and C-Cost, participants minimize the effects of inevitable intrinsic noise.
运动表现的变异性会随着练习而降低，但由于内在的运动噪声，这种变异性永远不会完全消除。本研究开发了一种方法，该方法可以量化表演者如何塑造自己的表现，以尽量减少运动噪声对运动结果的影响。该方法采用统计方法处理数据集，将变异性的三个组成部分（容差、噪声和协变）量化为相对于最佳表现的成本。T成本量化了如果数据位置最佳，结果可以改善多少；N成本将实际结果与相同位置具有最佳离散度的结果进行比较；C成本表示如果数据协变最佳，有望获得多少改善。TNC成本分析用于检验参与者练习6天或15天的投掷任务的学习情况。使用虚拟设置，15名参与者在模拟的同心力场中投掷摆锤式抛射体以击中目标。两个变量，即释放时的角度和速度，完全决定了抛射体的轨迹，从而决定了投掷的准确性。该任务是冗余的，成功的解决方案定义了一个非线性流形。实验结果分析表明，所有三个组成部分都存在，并且在练习过程中所有三个组成部分都有所下降。练习开始时，T成本变化很大；C成本和N成本下降得较慢，其中N成本仍然最高。这些结果表明，运动表现的变异性可以通过三条途径降低：通过调整执行中的容差、协变和噪声。我们推测，通过利用T成本和C成本，参与者可以将不可避免的内在噪声的影响降至最低。
