Learning arm kinematics and dynamics.

Abstract

In this review I have discussed how the form of representation used in internal models of the motor apparatus affects how and what a system can learn. Tabular models and structured models have benefits and drawbacks. Structured models incorporate knowledge of the structure of the controlled motor apparatus. If that knowledge is correct, or close to the actual system structure, the structured models will support global generalization and rapid, efficient learning. Tabular models can play an important role in learning to control systems when either the system structure is not known or only known approximately. Tabular models are general and flexible. Techniques for combining these different representations to attain the benefits of both are currently under investigation. In the control of multijoint systems such as the human arm, internal models of the motor apparatus are necessary to interpret performance errors. In the study of movements restricted to one joint, the problem of interpreting performance errors is greatly simplified and often overlooked, as performance errors can usually be related to command corrections by a single gain. When multijoint movements of the same motor systems are examined, however, the complex nature of the control and coordination problems faced by the nervous system become evident, as well as the sophistication of the brain's solutions to these problems. Recent progress in the understanding of adaptive control of eye movements provides a good example of this (Berthoz & Melvill-Jones 1985). Experimental studies of the psychophysics of motor learning can play an important role in bridging the gap between computational theories of how abstract motor systems might learn and physiological exploration of how actual nervous systems implement learning. Quantitative analyses of the patterns of motor learning of biological systems may help distinguish alternative hypotheses about the representations used for motor control and learning. What a system can and cannot learn, the amount of generalization, and the rate of learning give clues as to the underlying performance architecture. It is also important to know the actual performance level of the motor system (Loeb 1983). Different proposed control strategies will be able to attain different performance levels, and the use of simplifying control strategies may be evident in the control and learning performance of motor systems.