Time Scales and Motor Learning

Pennsylvania State Univ University Park, University Park PA

Investigators

Abstract

How do we learn to throw a ball or play an instrument, or perform any other skilled motor task? Our everyday experience tells us that practice is the key, with the anticipation that there will be ups and downs on the road to skilled performance. But researchers in the movement sciences see specific patterns in the time course of acquiring a motor skill, patterns that reveal the nature of motor learning. For decades those patterns have been interpreted as revealing a power law of learning that holds across all time scales, from gradual improvements that occur over days to rapid improvements that occur in a matter of minutes or even seconds. Recent advances however have shown the picture to be more complex that previously thought. With funding from the National Science Foundation, Dr. Newell will investigate those complexities in the time scales of motor learning from a dynamic systems perspective. On his approach, learning is formalized as the evolution of an attractor landscape, where elevation corresponds to distance from the current state of learning to the goal state. The critical hypothesis is that learning curves are governed by attractor landscape dynamics, which are punctuated by bifurcations of the attractor organization. Bifurcations provide a scientific understanding of the everyday experience in which the work of practice pays off in sudden moment, as when a child suddenly gets it when learning to ride a bicycle. The research promises to build a foundation for dynamic systems theories of learning, not only for motor skills but for learning of all kinds in biological and cognitive systems