Neural mechanisms of incorporating prior knowledge of movement experience into feedforward motor commands

The central nervous system (CNS) predicts the amount of force needed so that the hands can grasp and hold objects securely. How does the CNS compute the dynamics and produce the appropriate forces required to perform tasks like holding a cup or a needle? It has been proposed that the CNS combines a priori information about the properties of a movement with sensory information from the peripheral sensory receptors, to obtain optimal force estimation. We propose a novel task that requires the subjects to experience the magnitude of a first torque pulse and subsequently estimate and compensate a second torque pulse that is equal in magnitude. By varying the magnitude of the torque pulses according to a normal probability distribution with a large standard deviation, we investigate the neural mechanisms of how the CNS combines prior knowledge of movement experience with sensory feedback, to produce accurate feedforward motor commands.