van Beers Robert J
Department of Physics of Man, Helmholtz Institute, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.
Neuron. 2009 Aug 13;63(3):406-17. doi: 10.1016/j.neuron.2009.06.025.
In motor learning, our brain uses movement errors to adjust planning of future movements. This process has traditionally been studied by examining how motor planning is adjusted in response to visuomotor or dynamic perturbations. Here, I show that the learning strategy can be better identified from the statistics of movements made in the absence of perturbations. The strategy identified this way differs from the learning mechanism assumed in mainstream models for motor learning. Crucial for this strategy is that motor noise arises partly centrally, in movement planning, and partly peripherally, in movement execution. Corrections are made by modification of central planning signals from the previous movement, which include the effects of planning but not execution noise. The size of the corrections is such that the movement variability is minimized. This physiologically plausible strategy is optimally tuned to the properties of motor noise, and likely underlies learning in many motor tasks.
在运动学习中，我们的大脑利用运动误差来调整对未来运动的规划。传统上，这个过程是通过研究运动规划如何响应视觉运动或动态扰动进行调整来进行研究的。在这里，我表明，从无扰动情况下所做运动的统计数据中可以更好地识别学习策略。通过这种方式识别出的策略与主流运动学习模型中假设的学习机制不同。该策略的关键在于，运动噪声部分源于中枢，即在运动规划中，部分源于外周，即在运动执行中。通过修改来自先前运动的中枢规划信号进行校正，这些信号包括规划的影响但不包括执行噪声。校正的大小使得运动变异性最小化。这种生理上合理的策略针对运动噪声的特性进行了优化调整，并且可能是许多运动任务中学习的基础。
