IfADo-Leibniz Research Centre for Working Environment and Human Factors, Ardeystrasse 67, 44139, Dortmund, Germany.
Exp Brain Res. 2013 Mar;225(1):55-73. doi: 10.1007/s00221-012-3348-5. Epub 2012 Nov 30.
A quantitative model of optimal transport-aperture coordination (TAC) during reach-to-grasp movements has been developed in our previous studies. The utilization of that model for data analysis allowed, for the first time, to examine the phase dependence of the precision demand specified by the CNS for neurocomputational information processing during an ongoing movement. It was shown that the CNS utilizes a two-phase strategy for movement control. That strategy consists of reducing the precision demand for neural computations during the initial phase, which decreases the cost of information processing at the expense of lower extent of control optimality. To successfully grasp the target object, the CNS increases precision demand during the final phase, resulting in higher extent of control optimality. In the present study, we generalized the model of optimal TAC to a model of optimal coordination between X and Y components of point-to-point planar movements (XYC). We investigated whether the CNS uses the two-phase control strategy for controlling those movements, and how the strategy parameters depend on the prescribed movement speed, movement amplitude and the size of the target area. The results indeed revealed a substantial similarity between the CNS's regulation of TAC and XYC. First, the variability of XYC within individual trials was minimal, meaning that execution noise during the movement was insignificant. Second, the inter-trial variability of XYC was considerable during the majority of the movement time, meaning that the precision demand for information processing was lowered, which is characteristic for the initial phase. That variability significantly decreased, indicating higher extent of control optimality, during the shorter final movement phase. The final phase was the longest (shortest) under the most (least) challenging combination of speed and accuracy requirements, fully consistent with the concept of the two-phase control strategy. This paper further discussed the relationship between motor variability and XYC variability.
在我们之前的研究中，已经开发出一种用于最优传输-孔径协调（TAC）的定量模型。该模型的使用使得我们能够首次检查在进行中的运动过程中，中枢神经系统（CNS）为神经计算信息处理指定的精度需求的相位依赖性。结果表明，CNS 采用了两阶段的运动控制策略。该策略包括在初始阶段降低神经计算的精度需求，从而以降低控制最优性的程度为代价降低信息处理的成本。为了成功抓住目标物体，CNS 在最终阶段增加精度需求，从而提高控制最优性的程度。在本研究中，我们将最优 TAC 模型推广到点到点平面运动的 X 和 Y 分量之间的最优协调（XYC）模型。我们研究了 CNS 是否使用两阶段控制策略来控制这些运动，以及策略参数如何取决于规定的运动速度、运动幅度和目标区域的大小。结果确实揭示了 CNS 对 TAC 和 XYC 的调节之间存在实质性的相似性。首先，个体试验内 XYC 的变异性最小，这意味着运动过程中的执行噪声可以忽略不计。其次，在大多数运动时间内，XYC 的试验间变异性很大，这意味着信息处理的精度需求降低，这是初始阶段的特征。在较短的最终运动阶段，这种变异性显著降低，表明控制最优性更高。在速度和准确性要求的最（最）具挑战性的组合下，最终阶段最长（最短），这与两阶段控制策略的概念完全一致。本文还进一步讨论了运动变异性与 XYC 变异性之间的关系。
