Feedforward and Feedback Control Share an Internal Model of the Arm's Dynamics

Abstract

Recent work has shown that, when countering external forces, the nervous system adjusts not only predictive (i.e., feedforward) control of reaching but also reflex (i.e., feedback) responses to mechanical perturbations. Here we show that altering the physical properties of the arm (i.e., intersegmental dynamics) causes the nervous system to adjust feedforward control and that this learning transfers to feedback responses even though the latter were never directly trained. Forty-five human participants (30 females) performed a single-joint elbow reaching task and countered mechanical perturbations that created pure elbow motion. In our first experiment, we altered intersegmental dynamics by asking participants to generate pure elbow movements when the shoulder joint was either free to rotate or locked by the robotic manipulandum. With the shoulder unlocked, we found robust activation of shoulder flexor muscles for pure elbow flexion trials, as required to counter the interaction torques that arise at the shoulder because of forearm rotation. After locking the shoulder joint, which cancels these interaction torques, we found a substantial reduction in shoulder muscle activity over many trials. In our second experiment, we tested whether such learning transfers to feedback control. Mechanical perturbations applied to the arm with the shoulder unlocked revealed that feedback responses also account for intersegmental dynamics. After locking the shoulder joint, we found a substantial reduction in shoulder feedback responses, as appropriate for the altered intersegmental dynamics. Our work suggests that feedforward and feedback control share an internal model of the arm's dynamics.SIGNIFICANCE STATEMENT Here we show that altering the physical properties of the arm causes people to learn new motor commands and that this learning transfers to their reflex responses to unexpected mechanical perturbations, even though the reflex responses were never directly trained. Our results suggest that feedforward motor commands and reflex responses share an internal model of the arm's dynamics.

Keywords: feedback control; internal model; intersegmental dynamics; motor learning; reflex; voluntary movements.

Copyright © 2018 the authors 0270-6474/18/3810505-10$15.00/0.

Figures

Figure 1.

Experimental setup. In Experiments 1 and 2, participants were presented with a peripheral target that could be achieved with 20° of elbow flexion rotation. Participants were instructed to perform fast and accurate reaching movements to this peripheral target and did so with their shoulder joint unlocked and locked (top left column). In Experiment 2, in addition to reaching trials, mechanical perturbations were sometimes applied (probes) to test the sensitivity of feedback responses over the course of learning (bottom left column). Red and blue arrows represent the direction of the multi-joint step-torques applied to the shoulder and elbow joints. Illustrations of the protocols for Experiments 1 and 2 are shown on the right. In Experiments 1 and 2, participants performed 300 baseline trials with the shoulder joint unlocked, 1100 adaptation trials with the shoulder joint locked, and 300 post-adaptation trials with the shoulder joint unlocked. Multi-joint perturbations (probes, red and blue tick marks) were applied in 15% of all trials in Experiments 2.

Figure 2.

Compensating for intersegmental dynamics during self-initiated elbow reaches. A, Average kinematics of the shoulder (black) and elbow (blue) joints for elbow flexion movements with the shoulder joint free to move (i.e., baseline trials). Shaded areas represent the standard error of the mean (SEM). Data are aligned on movement onset. B, Solid and dashed lines represent average agonist (PEC) and antagonist (PD) muscle activity during flexion movements, respectively. Shaded areas represent the SEM. EMG data are normalized as described in Materials and Methods. Data are aligned on movement onset.

Figure 3.

Learning novel intersegmental dynamics following shoulder fixation. A, Average PEC muscle activity in a fixed time window (−100 to 100 ms relative to movement onset). Each data bin is the average of five trials. Shaded areas represent the SEM. EMG data are normalized as described in Materials and Methods. Error bars plotted between the epochs represent the mean and SE of the last five bins of trials in each phase contrasted with the respective bins in the control experiment in gray. B, Time series of PEC normalized muscle activity averaged over the last 25 baseline and adaptation trials. Data are aligned on movement onset. Shaded areas represent the SEM. C, Average PEC muscle activity in a fixed time window (−100 to 100 ms relative to movement onset) associated with these trials late in the baseline, adaptation and post-adaptation phases. Each dot represents data from a single participant. Asterisks indicate reliable effects (p < 0.05; see main text). D–F, Data for elbow BR muscle are shown using the same format as A–C. G–I,Data for BB muscle are shown using the same format as A–C.

Figure 4.

Movement trajectories after adapting elbow reaches. A, Average hand trajectories late in the baseline (25 trials) and early in the adaptation trials (first 3 trials). Each dot represents data from a single participant. B, Average error between hand position at movement offset to the center of the target in the last 25 trials in the baseline, first 3 trials early in the post-adaptation, and last 25 trials late in post-adaptation phases (p < 0.05; see main text). Asterisks indicate reliable effects. Each dot represent data from a single participant.

Figure 5.

Compensating for intersegmental dynamics during self-initiated reaches and when responding to perturbations. A, Average kinematics of the shoulder (dashed) and elbow (solid) joints for elbow flexion trials. Shaded areas represent the SEM. Data are aligned on movement onset. B, Dashed and solid lines represent average agonist (PEC) and antagonist (PD) muscle activity associated with the movement in A. Data are aligned on movement onset. Shaded areas represent SEM. C, Average kinematics of the shoulder (dashed) and elbow (solid) joints following mechanical perturbations. Red and blue traces are from the shoulder/elbow extensor torque and shoulder/elbow flexor torque conditions, respectively. Shaded areas represent SEM. Inset, The amount of shoulder and elbow displacement 50 ms post-perturbation (data are shown for all subjects). D, Normalized shoulder muscle activity associated with C. Shaded region indicates the Long-Latency epoch (LLR). Shaded areas represent SEM.

Figure 6.

Rapid feedback responses following learning during self-initiated reaching with shoulder fixation. A, Average shoulder EMG (filtered and rectified) in the long latency epoch (50–100 ms) across trials. Red and blue traces indicate the shoulder/elbow extensor torque (excitatory), and shoulder/elbow flexor torque conditions (inhibitory), respectively. Vertical dashed line separates perturbation trials that happened in the baseline, adaptation and post-adaptation phases. B, Five perturbation trials of the difference of PEC muscle activity (excitatory-inhibitory) averaged in late baseline and late adaptation trials. Shaded areas represent SEM. Baseline pre-activity in these trials is shown in the inset. Each data represent a single participant. C, Long-latency epoch of the difference of PEC muscle activity (excitatory-inhibitory) in the baseline, adaptation, and post-adaptation phases. (p < 0.05; see main text). Each data point represents a single participant. D–F, Data for elbow BR muscle are shown using the same format as A–C. Asterisks indicate reliable effects.

Figure 7.

Correlates of reach adaptation and feedback responses. Vertical axis is the percentage change in the long-latency epoch responses (50–100 ms) between baseline and late adaptation phase. Horizontal axis is the percentage change in muscle activity during elbow reaching trials (−100 to 100 ms relative to movement onset). Each data point represents a single participant.