Trajectories in human aimed movements are inherently variable. Using the concept of positional variance profiles, such trajectories are shown to be decomposable into two phases: In a first phase, the variance of the limb position over many trajectories increases rapidly; in a second phase, it then decreases steadily. A new theoretical model, where the aiming task is seen as a Shannon-like communication problem, is developed to describe the second phase: Information is transmitted from a “source” (determined by the position at the end of the first phase) to a “destination” (the movement’s end-point) over a “channel” perturbed by Gaussian noise, with the presence of a noiseless feedback link. Information-theoretic considerations show that the positional variance decreases exponentially with a rate equal to the channel capacity C. Two existing datasets for simple pointing tasks are re-analyzed and observations on real data confirm our model. The first phase has constant duration, and C is found constant across instructions and task parameters, which thus characterizes the participant’s performance. Our model provides a clear understanding of the speed-accuracy tradeoff in aimed movements: Since the participant’s capacity is fixed, a higher prescribed accuracy necessarily requires a longer second phase resulting in an increased overall movement time. The well-known Fitts’ law is also recovered using this approach.

人类目标运动的轨迹本质上是可变的。使用位置方差分布的概念，这些轨迹被证明可以分解为两个阶段：在第一阶段，肢体位置在许多轨迹上的方差迅速增加；在第二阶段，它然后稳步下降。一个新的理论模型，其中瞄准任务被视为一个类似香农的通信问题，被开发来描述第二阶段：信息从“源”（由第一阶段结束时的位置决定）传输到受高斯噪声干扰的“通道”上的“目的地”（运动的终点），存在无噪声反馈链接。信息论考虑表明位置方差以等于信道容量的速率呈指数下降Ç。重新分析了两个用于简单指向任务的现有数据集，对真实数据的观察证实了我们的模型。第一阶段具有恒定的持续时间，并且发现C在指令和任务参数中是恒定的，从而表征参与者的表现。我们的模型清楚地了解了目标运动中的速度 - 准确性权衡：由于参与者的能力是固定的，更高的规定准确性必然需要更长的第二阶段，从而导致整体运动时间增加。使用这种方法也可以恢复著名的菲茨定律。