Limb motion dictates how motor learning arises from arbitrary environmental dynamics

Abstract

A key idea in motor learning is that internal models of environmental dynamics are internally represented as functions of spatial variables including position, velocity, and acceleration of body motion. We refer to such a representation as motion dependent. The evidence for a motion-dependent representation is, however, primarily based on examination of the adaptation to motion-dependent dynamic environments. To more rigorously test this idea, we examined the adaptive response to perturbations that cannot be well approximated by motion-state: force-impulses--brief, high-amplitude pulses of force. The induced adaptation characterizes the impulse response of the system--a widely used technique for probing system dynamics in engineering systems identification. Here we examined the adaptive responses to two different force-impulse perturbations during human voluntary reaching movements. We found that although neither could be well approximated by motion-state (R(2) < 0.18 in both cases), both perturbations induced single-trial adaptive responses that were (R(2) > 0.87). Moreover, these responses were similar in shape to those induced by low-fidelity motion-based approximations of the force-impulses (r > 0.88). Remarkably, we found that the motion dependence of the adaptive responses to force-impulses persisted, even after prolonged exposure (R(2) > 0.95). During a 300-trial training period, trial-to-trial fluctuations in the position, velocity, and acceleration of motion accurately predicted trial-to-trial fluctuations in the adaptive response, and the adaptation gradually became more specific to the perturbation, but only via reorganization of the structure of the motion-dependent representation. These results indicate that internal models of environmental dynamics represent these dynamics in a motion-dependent manner, regardless of the nature of the dynamics encountered.

Environmental Consistency Determines the Rate of Motor Adaptation

“…To accomplish this, we estimated the single-trial adaptation rate induced by the first FF trial of each cycle by placing error-clamp (EC) measurement trials (black bars in Figure 1B) before and after the FF trial in order to determine the change in motor output associated with the FF exposure (blowout in Figure 1B) [14, 15]. We refer to this three-trial sequence as a measurement triplet and used the difference between the lateral force output recorded on the two EC trials in each measurement triplet to measure of the change in feedforward motor output induced by exposure to the intervening FF trial.…”

The training schedule affects the stability, not the magnitude, of the interlimb transfer of learned dynamics

“…Note that the value of b determines the orientation (clockwise or counterclockwise) and the magnitude of the force field exerted by the manipulandum and has units of N/(m/s). During error-clamp trials, the robot motors were used to constrain movements in a straight line toward the target by counteracting any motion perpendicular to the target direction (Gonzalez Castro et al 2011a;Joiner et al 2011;Joiner and Smith 2008;Scheidt et al 2000;Sing et al 2013;Smith et al 2006;Wagner and Smith 2008). This was achieved by applying a stiff one-dimensional spring (6 kN/m) and damper (150 Ns/m) in the axis perpendicular to the target direction.…”

“…During these error-clamp trials, lateral errors were kept small (Ͻ0.6 mm), so lateral force profiles essentially reflected adaptive compensation of the force-field perturbations. Since full compensation of the force-field perturbation on a particular trial required a lateral force profile proportional to the speed profile on that same trial (and this speed profile varied from one trial to another), we assessed the amount of adaptation on each error-clamp trial by computing a force-field compensation factor found by linear regression of the measured lateral force profile on each error-clamp trial onto the ideal force profile required for full force-field compensation on that trial after subtracting out the baseline force pattern (Gonzalez Castro et al 2011a;Joiner et al 2011;Joiner and Smith 2008;Sing et al 2013;Sing and Smith 2010;Smith et al 2006;Wagner and Smith 2008). Each regression coefficient characterizes the overall amount of force-field compensation in a given trial.…”

“…Since the 50-trial transfer period consisted entirely of error-clamp trials, the decay of interlimb generalization could be examined under conditions in which error signals that could promote or wash out adaptation were essentially absent (Joiner et al 2011;Scheidt et al 2000;Sing et al 2013;Smith et al 2006). The decay curves shown in Fig.…”

Skilled forelimb movements and internal copy motor circuits

“…Recent studies from Smith and colleagues revealed that adaptation emerges from the actual movement experienced rather than the original motor plan [24], and that even when limb perturbation is not well approximated by spatial variables (e.g. position and velocity), internal models nevertheless represent limb dynamics in a spatial framework [25]. Exploring the role of sensory error signals, Izawa and Shadmehr provide evidence that while both sensory and reward prediction errors drive movement correction, only sensory errors elicit remapping of the forward model of the limb [26].…”