Statistics predict kinematics of hand movements during everyday activity

Bayesian decision theory suggests that the statistics of an individual's actions (prior experience) play an important role in motor control and execution. To elucidate this relation, we recorded 7 million mouse movements made by a group of 20 computer users across a 50-day work period, allowing us to estimate the prior distribution of spontaneous hand movements. We found that the most frequent movements were in cardinal directions. The shape of this distribution was participant-specific but constant over time and independent of the computer that the participant used. This nonuniform directional distribution allowed us to predict systematic errors in initial movement directions, which matched well with the actual data. This shows how movement statistics can influence hand kinematics.