Michael Kositsky <email@example.com>
Motor Learning and Skill Acquisition by Sequences of Elementary Actions
Ph.D. dissertation, UMass, 2000
The work presents a computational model for motor learning and memory. The basic approach of the model is to treat complex activities as sequences of elementary actions. The model implements two major functions. First, the combination of elementary actions into sequences to produce desired complex activities, which is achieved by a search procedure involving multiscale task analysis and stochastic descent processing. Second, the utilization of past motor experience by effective memorization and retrieval, and generalizing sequences. New tasks are accomplished by combining past sequences intended for similar tasks. The generalization is based upon the clustering property combining past sequences intended for similar tasks. The generalization is based upon the clustering property of motor experience data. Specifically, the clustering property results in concentrating the data points within compact regions, allowing fast and accurate generalization of the elementary actions and consequently, enabling a robust performance of familiar tasks. A motor memory architecture is proposed that uses the clusters as the basic memory units.
The computational work is accompanied by a set of psychophysical studies aimed at examining the possible use of a cluster representation by the human motor system. The experiment examines the entire motor learning process, starting from untrained movements up to the formation of highly skilled actions.
Senior Postdoctoral Researcher
Department of Computer Science
University of Massachusetts, Amherst
Maintained by Francis F. Steen, Communication Studies, University of California Los Angeles