Variability in motor learning: relocating, channeling and reducing noise.
Variability in motor learning: relocating, channeling and reducing noise.
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DOI：
10.1007/s00221-008-1596-1
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发表时间：
2009-02
影响因子：
2
通讯作者：
Sternad, D.
中科院分区：
文献类型：
--
作者：
Cohen, R. G.;Sternad, D.
马达性能的变异性随着练习而减少，但永远不会完全消除，部分原因是内在的马达噪音。本研究开发了一种方法，量化表演者如何塑造他们的表演，以最大限度地减少电机噪音对运动结果的影响。该方法采用对数据集的统计方法，将变异性的三个组成部分(容差、噪声和协变)量化为相对于最佳性能的成本。T-Cost量化了如果数据的位置是最优的，结果可以改进多少，N-Cost将实际结果与在相同位置具有最佳分散的结果进行比较，C-Cost表示如果数据以最优方式变化，将会获得多少改进。TNC-Cost分析被应用于考察参与者练习了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动弹丸击中目标。投掷时的角度和速度这两个变量完全决定了投掷的轨迹，从而决定了投掷的准确性。这项任务是多余的，成功的解决方案定义了一个非线性流形。对实验结果的分析表明，所有这三个组件都存在，并且随着实践的进行，这三个组件都在减少。T-Cost的变化在实践开始时是相当大的；C-Cost和N-Cost的下降较慢，N-Cost仍然是最高的。这些结果表明，可以通过调整执行中的容差、协变和噪声三种途径来降低性能变异性。我们推测，通过利用T-成本和C-成本，参与者可以最大限度地减少不可避免的内在噪声的影响。
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.
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