The bliss (not the problem) of motor abundance (not redundancy)

Abstract

Motor control is an area of natural science exploring how the nervous system interacts with other body parts and the environment to produce purposeful, coordinated actions. A central problem of motor control-the problem of motor redundancy-was formulated by Nikolai Bernstein as the problem of elimination of redundant degrees-of-freedom. Traditionally, this problem has been addressed using optimization methods based on a variety of cost functions. This review draws attention to a body of recent findings suggesting that the problem has been formulated incorrectly. An alternative view has been suggested as the principle of abundance, which considers the apparently redundant degrees-of-freedom as useful and even vital for many aspects of motor behavior. Over the past 10 years, dozens of publications have provided support for this view based on the ideas of synergic control, computational apparatus of the uncontrolled manifold hypothesis, and the equilibrium-point (referent configuration) hypothesis. In particular, large amounts of "good variance"-variance in the space of elements that has no effect on the overall performance-have been documented across a variety of natural actions. "Good variance" helps an abundant system to deal with secondary tasks and unexpected perturbations; its amount shows adaptive modulation across a variety of conditions. These data support the view that there is no problem of motor redundancy; there is bliss of motor abundance.

Figure 1

A: An illustration of the notions of “good” and “bad” variance for the task of producing a magnitude of total force by pressing with two index fingers on individual force sensors. The dashed line shows the sub-space corresponding to perfect task execution. B: For the same task different data distributions are illustrated corresponding to different sharing patterns (different locations of the centers of the distributions, cf. D1 and D2) and different co-variation (different relative amounts of “good” and “bad” variance, cf. D2 and D3).