By 1967, motor control and learning researchers had adopted an information processing (IP) approach. Central to that research was understanding how movement information was processed, coded, stored, and represented in memory. It also was centered on understanding motor control and learning in terms of Fitts’ law, closed-loop and schema theories, motor programs, contextual interference, modeling, mental practice, attentional focus, and how practice and augmented feedback could be organized to optimize learning. Our constraints-based research from the 1980s into the 2000s searched for principles of “self-organization”, and answers to the degrees-of-freedom problem, that is, how the human motor system with so many independent parts could be controlled without the need for an executive decision maker as proposed by the IP approach. By 2007 we were thinking about where the IP and constraints-based views were divergent and complementary, and whether neural-based models could bring together the behavior and biological mechanisms underlying the processes of motor control and learning.
Scott Kretchmar and Mark L. Latash
performed by psychologists, one of the established laws is the Fitts’ law ( Fitts, 1954 ), which links movement time (MT) to an index of task difficulty (ID) computed as the ratio of movement distance to target size: MT = a + b ·log(ID), where a and b are constants. Fitts’ law has been confirmed