Estimating Human Movements Using Memory of Errors

Humans estimate their movements based on their knowledge of the dynamics of the environment, and on actual sensory data. Wolpert and colleagues have incorporated this understanding into a model that simulates this estimation using the Kalman filter [1], . Inspired by a recent study in neuroscience [2], we here introduce a new factor—memory of errors—into this simulation of movement estimation. These historical errors help humans determine the stability of the environment, which could be either steady or rapidly changing. This condition controls the rate at which a given error will be learned, so as to affect the estimates of future movements. We here apply our new model, a modified Kalman filter incorporating memory of errors, to the simulation of a hand lifting movement, and compare the simulated estimation process with its human counterpart.