Gepshtein Sergei, Seydell Anna, Trommershäuser Julia
Laboratory for Perceptual Dynamics, Brain Science Institute, Saitama, Japan.
J Vis. 2007 Sep 28;7(5):13.1-18. doi: 10.1167/7.5.13.
Biological movements are prone to error. Different movements lead to different errors, and the distributions of errors depend on movement amplitude and direction. Movement planning would benefit from taking this variability into account, by applying appropriate corrections for movements associated with the different shapes and sizes of error distributions. Here we asked whether the human nervous system can do so. In a game-like task, participants performed rapid sequences of goal-directed pointing movements in different directions, toward stimulus configurations presented at different eccentricities on a slanted touch screen. The task was to accumulate rewards by hitting target regions and to minimize losses by avoiding penalty regions. The distributions of endpoint errors varied in size and degree of anisotropy across stimulus locations. Our participants adjusted their movements toward the different locations accordingly. We compared human behavior with the optimal behavior predicted by ideal movement planner maximizing expected gain. In most cases, human behavior was indistinguishable from optimal. This is evidence that human movement planning approaches statistical optimality by representing the task-relevant movement variability.
生物运动容易出错。不同的运动会导致不同的错误，并且错误的分布取决于运动幅度和方向。通过对与不同形状和大小的错误分布相关的运动应用适当的校正，运动规划将受益于考虑这种变异性。在这里，我们询问人类神经系统是否能够做到这一点。在一个类似游戏的任务中，参与者朝着倾斜触摸屏上不同偏心度呈现的刺激配置，在不同方向上执行快速的目标导向指向运动序列。任务是通过击中目标区域来积累奖励，并通过避开惩罚区域来最小化损失。端点误差的分布在不同刺激位置的大小和各向异性程度上有所不同。我们的参与者相应地调整了他们朝向不同位置的运动。我们将人类行为与理想运动规划器预测的最优行为进行了比较，该规划器通过最大化预期收益来实现最优。在大多数情况下，人类行为与最优行为难以区分。这证明人类运动规划通过表征与任务相关的运动变异性接近统计最优。
