Nonlinear dynamics of motor learning.

In this paper we review recent work from our studies of a nonlinear dynamics of motor learning that is grounded in the construct of an evolving attractor landscape. With the assumption that learning is goal-directed, we can quantify the observed performance as a score or measure of the distance to the learning goal. The structure of the dynamics of how the goal is approached has been traditionally studied through an analysis of learning curves. Recent years have seen a gradual paradigm shift from a 'universal power law of practice' to an analysis of performance dynamics that reveals multiple processes that include adaption and learning as well as changes in performance due to factors such as fatigue. Evidence has also been found for nonlinear phenomena such as bifurcations, hysteresis and even a form of self-organized criticality. Finally, we present a quantitative measure for the dual concepts of skill and difficulty that allows us to unfold a learning process in order to study universal properties of learning transitions.