Open Questions in Computational Motor Control

Abstract

Computational motor control covers all applications of quantitative tools for the study of the biological movement control system. This paper provides a review of this field in the form of a list of open questions. After an introduction in which we define computational motor control, we describe: a Turing-like test for motor intelligence; internal models, inverse model, forward model, feedback error learning and distal teacher; time representation, and adaptation to delay; intermittence control strategies; equilibrium hypotheses and threshold control; the spatiotemporal hierarchy of wide sense adaptation, i.e., feedback, learning, adaptation, and evolution; optimization based models for trajectory formation and optimal feedback control; motor memory, the past and the future; and conclude with the virtue of redundancy. Each section in this paper starts with a review of the relevant literature and a few more specific studies addressing the open question, and ends with speculations about the possible answer and its implications to motor neuroscience. This review is aimed at concisely covering the topic from the author's perspective with emphasis on learning mechanisms and the various structures and limitations of internal models.

Mini-max feedback control as a computational theory of sensorimotor control in the presence of structural uncertainty

“…Feedback, adaptation, learning, and evolution have been identified as instances of wide sense adaptation, where sensory information is integrated and employed to change the control signals in various techniques and timescales (Karniel, 2011). Adaptive control is the change in the parameters of the control systems generated after the observation of previous control and sensory signals, and learning control is a structural change in the control system to generate a new type of behavior.…”

State-Based Delay Representation and Its Transfer from a Game of Pong to Reaching and Tracking

“…It is unclear whether the brain represents time explicitly (Karniel, 2011) using “neural clocks” (Ivry, 1996; Spencer et al 2003; Ivry and Schlerf, 2008). Evidence suggests that no such clock is involved in the control of movement: humans can adapt to force perturbations that depend on the state of the arm (position, velocity, etc.…”

Passive Motion Paradigm: An Alternative to Optimal Control

“…On the other hand, it is still an open question whether or not the motor system represents equilibrium trajectories (Karniel, 2011). Many motor adaptation studies, starting with the seminal paper by Shadmehr and Mussa-Ivaldi (1994), demonstrate that equilibrium points or equilibrium trajectories per se are not sufficient to account for adaptive motor behavior, but this is not sufficient to rule out the existence of neural mechanisms or internal models capable of generating equilibrium trajectories.…”

“…Many motor adaptation studies, starting with the seminal paper by Shadmehr and Mussa-Ivaldi (1994), demonstrate that equilibrium points or equilibrium trajectories per se are not sufficient to account for adaptive motor behavior, but this is not sufficient to rule out the existence of neural mechanisms or internal models capable of generating equilibrium trajectories. Rather, as suggested by Karniel (2011), such findings should induce the research to shift from the lower level analysis of reflex loops and muscle properties to the level of internal representations and the structure of internal models. This is indeed the motivation and the purpose of our proposal: to model the posited internal models in terms of an extension of the EPH.…”

“…However, a central issue that still remains to be understood is how the brain uses different solutions under different circumstances (Karniel, 2011). Multiple internal models as proposed by different authors (Wolpert and Kawato, 1998; Haruno et al, 2001; Demiris and Khadhouri, 2006) might be the key to represent multiple solutions to the same goal.…”