A computational theory of motor learning

In this paper we present a computational theory of human motor performance and learning. The theory is implemented as a running AI system called MAGGIE. Given a description of a desired movement as input, the system generates simulated motor behavior as output. The theory states that skills are encoded as moror s c h e m , which specify the positions and velocities of a limb at selected points in time. Moreover, there exist two natural representations for such knowledge; viewer-centered schemas describe visually perceived behavior, and joint-centered schemas are used to generate behavior. When the model acts upon these two representational formats, they exhibit quite different behavioral chamcteristics. MAGGIE performs the desired movement within a feedback control paradigm. monitoring for errors and correcting them when it detects them. Learning involves improving the joint-centered schema over many practice trials; this reduces the need for monitoring. The model accounts for a number of well-documented motor phenomena, including the speed-accuracy trade-off and the gradual improvement in performance with practice. It also makes several testable predictions. We close with a discussion of the theory’s strengths and weaknesses, along with directions for future research. Key word: machine learning, motor behavior, skill improvement, speed-accuracy trade-off, hill climbing, motor schemas, motor programs.