Abstract
There have been two approaches to model human movement based on the assumption that the movement is a result of minimizing the duration of the total movement with the constraint that the movement has to end within a target. In one approach, the duration of submovements were optimized (e.g., Mayer et al., 1988). In the other, the entire trajectory of a single movement, characterized by the relation between position and time was optimized (e.g., Harris & Wolpert, 1998). Both approaches can successfully explain the speed-accuracy tradeoff, but neither is complete. The former does not specify the properties of the movement trajectory, whereas the latter either does not include feedback, or the feedback is continuous. As a result, the movement does not consist of discrete submovements. We propose a new model, which combines aspects of these two approaches. Specifically, we assume that a movement consists of two submovements and a single feedback, and the trajectory of each submovement is being optimized. Each submovement is generated using a fourth order system with signal dependent noise (Harris and Wolpert, 1998). The transition from the first to the second submovement as well as the profile of each submovement are being optimized so that the total movement time is minimal and the variance at the landing position is less than some criterion. For simplicity, the jerk at the transition is assumed to be zero. The simulation results show that the optimal transition occurs at the early stage of the movement showing a sharp peak of the acceleration profile. The sharp peak of the acceleration profile, which has never been reported before, is consistent with our preliminary psychophysical data. The simulation results also show bell-shaped positional variance curve in accordance with psychophysical data.