The learning of movement skills is characterized by persistent change in behavior over time. There are many indices of change in motor behavior and many time scales (rates of change and time periods) over which the change in behavior occurs. A central proposition of this research is that time scales are fundamental in the description and prediction of the change in behavior that we infer as learning. The research stems from a theoretical framework based upon the concepts and tools of nonlinear dynamical systems; this framework was devised to account for both the persistent (relatively long-term, such as days, weeks, months) and transitory (relatively short-term, such as trial to trial) changes traditionally shown for the learning of motor skills. The last 100 years of research on the learning of motor skills has shown that a number of different functions of change are revealed in learning curves, such as an exponential, power law, S-shaped, logistic, sudden "discontinuous". Typically, different theories of learning make different assumptions about the time scales of change that are inherent in the mathematical equations used to fit learning data. Moreover, theories of learning tend to focus on one function of learning rather accommodate the complete set of learning functions. This research will test the proposition that a small set of principles from nonlinear dynamics can produce all of the standard functions of change observed in motor learning. A series of experiments conducted within a dynamical systems framework will examine, in a range of motor tasks, the time scales of change in motor learning. This research will provide a first test of the notion that a unified and parsimonious dynamical account of time scales of change can derive the established set of short- and long-term learning functions in motor learning. The promise of this theoretical and experimental approach to the time scales of motor learning is that it will lead us beyond traditional descriptions of learning toward a predictive science of human motor learning, that links theoretically to neural net approaches to human cognition and artificial system learning.
