Learning a coordinate transformation for a human visual feedback controller based on disturbance noise and the feedback error signal

The speed, accuracy, and adaptability of human movement depends on the brain performing an inverse kinematics transformation-that is, a transformation from visual to joint angle coordinates-based on learning from experience. In human motion control, it is important to learn a feedback controller for the hand position error in the human inverse kinematics solver. This paper proposes a novel model that uses disturbance noise and the feedback error signal to learn coordinate transformations of the human visual feedback controller. The proposed model redresses drawbacks in current models because it does not rely on complex signal switching, which does not seem neurophysiologically plausible. Numerical simulations show the effectiveness of the model.