Optimal Movement Selection

Abstract

Most physical tasks can be performed with an infinite number of movement patterns. How then are particular patterns selected? We propose that the contributions of individual limb segments depend on their own independently assessed fits to task demands. An advantage of this system is that coordination among limb segments can be achieved without explicit control of limb-segment interactions. In addition, the system allows segments that are still functioning to compensate for segments that are disabled. To test the model, we first asked subjects to oscillate the fingertip over varying distances at varying rates, using only the finger, hand, or forearm. Based on their performance, we identified the optimal amplitude and frequency of movement for each limb segment. Then we allowed the subjects to use the finger, hand, and forearm however they wished. We demonstrate that the relative contribution of each limb segment to fingertip displacement is predicted by the similarity of the optimal amplitude and frequency of that segment to the required amplitude and frequency of fingertip displacement. Because our model is similar to models proposed for learning and perception, common computational approaches appear viable for motor control and other more widely studied activities underlying information processing and behavior.