A Developmental Approach Aids Motor Learning

Bernstein (1967) suggested that people attempting to learn to perform a difficult motor task try to ameliorate the degrees-of-freedom problem through the use of a developmental progression. Early in training, people maintain a subset of their control parameters (e.g., joint positions) at constant settings and attempt to learn to perform the task by varying the values of the remaining parameters. With practice, people refine and improve this early-learned control strategy by also varying those parameters that were initially held constant. We evaluated Bernstein's proposed developmental progression using six neural network systems and found that a network whose training included developmental progressions of both its trajectory and its feedback gains outperformed all other systems. These progressions, however, yielded performance benefits only on motor tasks that were relatively difficult to learn. We conclude that development can indeed aid motor learning.

[1]  B. Vereijken,et al.  Free(z)ing Degrees of Freedom in Skill Acquisition , 1992 .

[2]  R. Emmerik,et al.  The effects of practice on limb kinematics in a throwing task. , 1989, Journal of motor behavior.

[3]  Alistair J. Bray,et al.  Foundations of cognitive science ☆: Michael I. Posner, ed. , 1991 .

[4]  N. Berthier,et al.  Proximodistal structure of early reaching in human infants , 1999, Experimental Brain Research.

[5]  Wayne Christensen,et al.  Self-directedness, integration and higher cognition , 2004 .

[6]  M. Kawato,et al.  A hierarchical neural-network model for control and learning of voluntary movement , 2004, Biological Cybernetics.

[7]  Terence D. Sanger,et al.  Neural network learning control of robot manipulators using gradually increasing task difficulty , 1994, IEEE Trans. Robotics Autom..

[8]  M. Posner Foundations of cognitive science , 1989 .

[9]  N. A. Bernshteĭn The co-ordination and regulation of movements , 1967 .

[10]  Peter I. Corke,et al.  A robotics toolbox for MATLAB , 1996, IEEE Robotics Autom. Mag..

[11]  J. van Leeuwen,et al.  Neural Networks: Tricks of the Trade , 2002, Lecture Notes in Computer Science.

[12]  K. Newell,et al.  Dimensional change in motor learning. , 2001, Human movement science.

[13]  Elliot Saltzman,et al.  Levels of sensorimotor representation , 1979 .

[14]  M. Gazzaniga The new cognitive neurosciences, 2nd ed. , 2000 .

[15]  E. Todorov Optimality principles in sensorimotor control , 2004, Nature Neuroscience.

[16]  Zoubin Ghahramani,et al.  Computational motor control , 2004 .