Abstract
Although we previously modeled the aperture component of grasping movement, yet we've not explained the mechanism of the transport (reaching) component. In general, reaching movement shows symmetric bell-shaped velocity profiles, and contrarily, the peak velocity appears earlier in grasping movement. This early peak velocity is also observed in reaching movement to small-diameter goal, and has not been explained by previous optimal models for reaching. In this study, we modeled a velocity profile with a beta distribution function, and optimized distribution parameters by minimizing a cost function which superposes the predicted final reaching variability with the Kalman filter and the energy consumption of motor commands. As a result, the peak velocity was shifted earlier when the contribution of the predicted variability to the cost is larger. This result suggests that the predicted accuracy is important in the control of the transport component, as well as the aperture component in grasping movement.
