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Trajectories in human aimed movements are inherently variable. Using theconcept of positional variance profiles, such trajectories are shown to bedecomposable into two phases: In a first phase, the variance of the limbposition over many trajectories increases rapidly; in a second phase, it thendecreases steadily. A new theoretical model, where the aiming task is seen as aShannon-like communication problem, is developed to describe the second phase:Information is transmitted from a source (determined by the position at the endof the first phase), to a destination (the movement's end-point) over a channelperturbed by Gaussian noise, with the presence of a noiseless feedback link.Information-theoretic considerations show that the positional variancedecreases exponentially with a rate equal to the channel capacity C. Twoexisting datasets for simple pointing tasks are re-analyzed and observations onreal data confirm our model. The first phase has constant duration and C isfound constant across instructions and task parameters, which thuscharacterizes the participant's performance. Our model provides a clearunderstanding of the speed-accuracy tradeoff in aimed movements: Since theparticipant's capacity is fixed, a higher prescribed accuracy necessarilyrequires a longer second phase resulting in an increased overall movement time.The well-known Fitts' law is also recovered using this approach.

A Feedback Information-Theoretic Transmission Scheme (FITTS) for Modeling Trajectory Variability in Aimed Movements