  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.
