Neural models for flexible control of redundant systems

Abstract

Abstract This chapter discusses the explanation of a class of human motor equivalence competencies put forth by the DIVA and DIRECT models of motor skill acquisition and performance. It is suggested that experimental data indicating approximate postural invariance for reaches do not imply that the motor system is utilizing postural targets. Instead, an inverse kinematics transformation utilizing a directional mapping with a “postural relaxation” component is shown to be consistent with these data while also providing motor equivalent capabilities not possessed by models that use postural targets. This transformation is related to robotics techniques utilizing a Jacobian pseudoinverse and to the motor control models of Cruse and colleagues. A self-organizing neural network architecture that learns such a directional mapping is presented, including simulations verifying its ability to explain the approximate postural invariance seen in the experimental data. Side effects of the model’s learning process suggest two sources that may contribute to the gentle curvature seen in human reaches: a bias toward movements along the long axis of the manipulability ellipsoid, and a tendency toward more comfortable postures.