Information entropy analysis of discrete aiming movements

Information entropy and mutual information were investigated in discrete movement aiming tasks over a wide range of spatial (20-160 mm) and temporal (250-1250 ms) constraints. Information entropy was calculated using two distinct analyses: (1) with no assumption on the nature of the data distribution; and (2) assuming the data have a normal distribution. The two analyses showed different results in the estimate of entropy that also changed as a function of task goals, indicating that the movement trajectory data were not from a normal distribution. It was also found that the information entropy of the discrete aiming movements was lower than the task defined indices of difficulty (ID) that were selected for the congruence with Fitts' law. Mutual information between time points of the trajectory was strongly influenced by the average movement velocity and the acceleration/deceleration segments of the movement. The entropy analysis revealed structure to the variability of the movement trajectory and outcome that has been masked by the traditional distributional analyses of discrete aiming movements.