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Signal-dependent noise determines motor planning (see comments)

机译：取决于信号的噪声决定了电动机的计划（请参阅注释）

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摘要

When we make saccadic eye movements or goal-directed arm movements, there is an infinite number of possible trajectories that the eye or arm could take to reach the target. However, humans show highly stereotyped trajectories in which velocity profiles of both the eye and hand are smooth and symmetric for brief movements. Here we present a unifying theory of eye and arm movements based on the single physiological assumption that the neural control signals are corrupted by noise whose variance increases with the size of the control signal. We propose that in the presence of such signal-dependent noise, the shape of a trajectory is selected to minimize the variance of the final eye or arm position. This minimum-variance theory accurately predicts the trajectories of both saccades and arm movements and the speed-accuracy trade-off described by Fitt's law. These profiles are robust to changes in the dynamics of the eye or arm, as found empirically. Moreover, the relation between path curvature and hand velocity during drawing movements reproduces the empirical 'two-thirds power law. This theory provides a simple and powerful unifying perspective for both eye and arm movement control.
机译：当我们进行眼球运动或目标定向的手臂运动时，眼睛或手臂可能会到达目标的无限数量的可能轨迹。但是，人类显示出高度定型的轨迹，其中眼睛和手的速度分布都平滑且对称，可以短暂运动。在这里，我们基于单一的生理假设提出了一种眼球和手臂运动的统一理论，即神经控制信号被噪声破坏，噪声的方差随控制信号的大小而增加。我们建议在存在此类信号相关噪声的情况下，选择轨迹的形状以最大程度地减少最终眼睛或手臂位置的变化。这种最小方差理论可以准确地预测扫视运动和手臂运动的轨迹，以及菲特定律所描述的速度精度的权衡。如经验发现，这些轮廓对于眼睛或手臂的动态变化具有鲁棒性。此外，在绘画运动过程中路径曲率和手速度之间的关系再现了经验的“三分之二”幂定律。该理论为眼睛和手臂的运动控制提供了一个简单而强大的统一视角。

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