Shared internal models for feedforward and feedback control of arm dynamics in non-human primates

Previous work has shown that humans account for and learn novel properties or the arm’s dynamics, and that such learning causes changes in both the predictive (i.e., feedforward) control of reaching and reflex (i.e., feedback) responses to mechanical perturbations. Here we show that similar observations hold in old-world monkeys (macaca fascicularis). Two monkeys were trained to use an exoskeleton to perform a single-joint elbow reaching and to respond to mechanical perturbations that created pure elbow motion. Both of these tasks engaged robust shoulder muscle activity as required to account for the torques that typically arise at the shoulder when the forearm rotates around the elbow joint (i.e., intersegmental dynamics). We altered these intersegmental arm dynamics by having the monkeys generate the same elbow movements with the shoulder joint either free to rotate, as normal, or fixed by the robotic manipulandum, which eliminates the shoulder torques caused by forearm rotation. After fixing the shoulder joint, we found a systematic reduction in shoulder muscle activity. In addition, after releasing the shoulder joint again, we found evidence of kinematic aftereffects (i.e., reach errors) in the direction predicted if failing to compensate for normal arm dynamics. We also tested whether such learning transfers to feedback responses evoked by mechanical perturbations and found a reduction in shoulder feedback responses, as appropriate for these altered arm intersegmental dynamics. Demonstrating this learning and transfer in non-human primates will allow the investigation of the neural mechanisms involved in feedforward and feedback control of the arm’s dynamics.

Explicit feedback and instruction do not change shoulder muscle activity reduction after shoulder fixation

Generating pure elbow rotation requires contracting muscles at both the shoulder and elbow joints to counter torques that arise at the shoulder when the forearm rotates (i.e., intersegmental dynamics). Previous work has shown that human participants learn to reduce their shoulder muscle activity if the same elbow movement is performed after the shoulder joint is mechanically locked, which is appropriate because locking the shoulder joint eliminates the torques that arise at the shoulder when the forearm rotates. However, this learning is slow (i.e., it unfolds over hundreds of trials) and incomplete (i.e., shoulder activity is not fully eliminated). Here we investigated whether and how the addition of explicit strategies and biofeedback modulate this type of learning. Three groups of human participants (N = 55) performed voluntary pure elbow rotations using a robotic exoskeleton that permits shoulder and elbow rotation in a horizontal plane. Participants did the task with the shoulder free to move (baseline), then with the shoulder joint locked by the robotic manipulandum (adaptation), and then with the shoulder free to move again (post-adaptation). The first group of participants performed this protocol and received no instructions about what to do after their shoulder was locked. The second group of participants received visual feedback about their shoulder muscle activity after each movement and was instructed to reduce their shoulder activity to zero. The third group of participants also received visual biofeedback, but it was removed part way through the experiment. We found that, although all groups learned, the rate and magnitude of learning was not reliably different across the groups. Taken together, our results suggest that learning new arm dynamics, unlike other motor learning paradigms, unfolds independent of explicit instructions, biofeedback and task instructions.

Generalizing movement patterns following shoulder fixation

Generalizing newly learned movement patterns beyond the training context is challenging for most motor learning situations. Here we tested whether learning of a new physical property of the arm during self-initiated reaching generalizes to new arm configurations. Human participants performed a single-joint elbow reaching task and/or countered mechanical perturbations that created pure elbow motion with the shoulder joint free to rotate or locked by the manipulandum. With the shoulder free, we found activation of shoulder extensor muscles for pure elbow extension trials, appropriate for countering torques that arise at the shoulder due to forearm rotation. After locking the shoulder joint, we found a partial reduction in shoulder muscle activity, appropriate because locking the shoulder joint cancels the torques that arise at the shoulder due to forearm rotation. In our first three experiments, we tested whether and to what extent this partial reduction in shoulder muscle activity generalizes when reaching in different situations: 1) different initial shoulder orientation, 2) different initial elbow orientation, and 3) different reach distance/speed. We found generalization for the different shoulder orientation and reach distance/speed as measured by a reliable reduction in shoulder activity in these situations but no generalization for the different elbow orientation. In our fourth experiment, we found that generalization is also transferred to feedback control by applying mechanical perturbations and observing reflex responses in a distinct shoulder orientation. These results indicate that partial learning of new intersegmental dynamics is not sufficient for modifying a general internal model of arm dynamics. NEW & NOTEWORTHY Here we show that partially learning to reduce shoulder muscle activity following shoulder fixation generalizes to other movement conditions, but it does not generalize globally. These findings suggest that the partial learning of new intersegmental dynamics is not sufficient for modifying a general internal model of the arm’s dynamics.

The role of intersegmental dynamics in coordination of the forelimb joints during unperturbed and perturbed skilled locomotion

Joint coordination during locomotion and how this coordination changes in response to perturbations remains poorly understood. We investigated coordination among forelimb joints during the swing phase of skilled locomotion in the cat. While cats walked on a horizontal ladder, one of the cross-pieces moved before the cat reached it, requiring the cat to alter step size. Direction and timing of the cross-piece displacement were manipulated. We found that the paw was transported in space through body translation and shoulder and elbow rotations, whereas the wrist provided paw orientation required to step on cross-pieces. Kinetic analysis revealed a consistent joint control pattern in all conditions. Although passive interaction and gravitational torques were the main sources of shoulder and elbow motions for most of the movement time, shoulder muscle torque influenced movement of the entire limb at the end of the swing phase, accelerating the shoulder and causing interaction torque that determined elbow motion. At the wrist, muscle and passive torques predominantly compensated for each other. In all perturbed conditions, although all joints and the body slightly contributed to changes in the step length throughout the entire movement, the major adjustment was produced by the shoulder at the movement end. We conclude that joint coordination during the swing phase is produced mainly passively, by exploiting gravity and the limb’s intersegmental dynamics, which may simplify the neural control of locomotion. The use of shoulder musculature at the movement end enables flexible responses to environmental disturbances. NEW & NOTEWORTHY This is the first study to investigate joint control during the swing phase of skilled, accuracy-dependent locomotion in the cat and how this control is altered to adapt to known and unexpected perturbations. We demonstrate that a pattern of joint control that exploits gravitational and interaction torques is used in all conditions and that movement modifications are produced mainly by shoulder muscle torque during the last portion of the movement.

Feedforward and feedback control share an internal model of the arm’s dynamics

Humans have a remarkable capacity to learn novel movement patterns in a wide variety of contexts. Recent work has shown that, when countering external forces, the nervous system adjusts not only voluntary (ie. feedforward) control but also reflex (ie. feedback) responses. Here we show that directly altering the physical properties of the arm (i.e. intersegmental dynamics) causes the nervous system to adjust feedforward control and that this learning also transfers to feedback responses even though they were never directly trained. In our first experiment, we altered intersegmental dynamics by asking participants to generate pure elbow movements with the shoulder joint either free to rotate or locked. Locking the shoulder joint cancels the interaction forces that arise at the shoulder during forearm rotation and thus removes the need to activate shoulder muscles to prevent shoulder joint rotation. We first asked whether the nervous system learns this altered mapping of intersegmental dynamics. In the baseline phase, we found robust activation of shoulder flexor muscles for pure elbow flexion trials prior to movement onset – as required to counter the intersegmental dynamics. After locking the shoulder joint in the adaptation phase, we found a substantial reduction in shoulder muscle activity over many trials. After unlocking the shoulder joint in the post-adaptation phase, we observed after-effects, as participants made systematic hand path errors. In our second experiment, we investigated whether such learning transfers to feedback control. Mechanical perturbations applied to the limb in the baseline phase revealed that feedback responses, like feedforward control, also appropriately countered intersegmental dynamics. In the adaptation phase, we found a substantial reduction in shoulder feedback responses – as appropriate for the altered intersegmental dynamics. We also found that this decay in shoulder feedback responses correlated across subjects with the amount of decay during feedforward control. Our work adds to the growing evidence that feedforward and feedback control share an internal model of the arm’s dynamics.

Compensating for intersegmental dynamics across the shoulder, elbow, and wrist joints during feedforward and feedback control

Moving the arm is complicated by mechanical interactions that arise between limb segments. Such intersegmental dynamics cause torques applied at one joint to produce movement at multiple joints, and in turn, the only way to create single joint movement is by applying torques at multiple joints. We investigated whether the nervous system accounts for intersegmental limb dynamics across the shoulder, elbow, and wrist joints during self-initiated planar reaching and when countering external mechanical perturbations. Our first experiment tested whether the timing and amplitude of shoulder muscle activity account for interaction torques produced during single-joint elbow movements from different elbow initial orientations and over a range of movement speeds. We found that shoulder muscle activity reliably preceded movement onset and elbow agonist activity, and was scaled to compensate for the magnitude of interaction torques arising because of forearm rotation. Our second experiment tested whether elbow muscles compensate for interaction torques introduced by single-joint wrist movements. We found that elbow muscle activity preceded movement onset and wrist agonist muscle activity, and thus the nervous system predicted interaction torques arising because of hand rotation. Our third and fourth experiments tested whether shoulder muscles compensate for interaction torques introduced by different hand orientations during self-initiated elbow movements and to counter mechanical perturbations that caused pure elbow motion. We found that the nervous system predicted the amplitude and direction of interaction torques, appropriately scaling the amplitude of shoulder muscle activity during self-initiated elbow movements and rapid feedback control. Taken together, our results demonstrate that the nervous system robustly accounts for intersegmental dynamics and that the process is similar across the proximal to distal musculature of the arm as well as between feedforward (i.e., self-initiated) and feedback (i.e., reflexive) control. NEW & NOTEWORTHY Intersegmental dynamics complicate the mapping between applied joint torques and the resulting joint motions. We provide evidence that the nervous system robustly predicts these intersegmental limb dynamics across the shoulder, elbow, and wrist joints during reaching and when countering external perturbations.

Compensating for intersegmental dynamics across the shoulder, elbow and wrist joints during feedforward and feedback control

ABSTRACTMoving the arm is complicated by mechanical interactions that arise between limb segments. Such intersegmental dynamics cause torques applied at one joint to produce movement at multiple joints and, in turn, the only way to create single joint movement is by applying torques at multiple joints. Here, we investigated whether the nervous system accounts for intersegmental limb dynamics across the shoulder, elbow, and wrist joints during self-initiated reaching and when countering external mechanical perturbations. Our first experiment tested whether the timing and amplitude of shoulder muscle activity accounts for interaction torques produced during single-joint elbow movements from different elbow initial orientations and over a range of movement speeds. We found that shoulder muscle activity reliably preceded movement onset and elbow agonist activity, and was scaled to compensate for the magnitude of interaction torques arising because of forearm rotation. Our second experiment tested whether elbow muscles compensate for interaction torques introduced by single-joint wrist movements. We found that elbow muscle activity preceded movement onset and wrist agonist muscle activity, and thus the nervous system predicted interaction torques arising because of hand rotation. Our third and fourth experiments tested whether shoulder muscles compensate for interaction torques introduced by different hand orientations during self-initiated elbow movements and when countering mechanical perturbations that caused pure elbow motion. We found that the nervous system predicted the amplitude and direction of interaction torques, appropriately scaling the amplitude of shoulder muscle activity during self-initiated elbow movements and rapid feedback control. Taken together, our results demonstrate that the nervous system robustly accounts for intersegmental dynamics, and that the process is similar across the proximal to distal musculature of the arm as well as between feedforward (i.e., self-initiated) and feedback (i.e., reflexive) control.NEW & NOTEWORTHYIntersegmental dynamics complicate the mapping between applied joint torques and the resulting joint motions. Here, we provide evidence that the nervous system robustly predicts these intersegmental limb dynamics across the shoulder, elbow and wrist joints during reaching and when countering external perturbations.

Coordinating long-latency stretch responses across the shoulder, elbow, and wrist during goal-directed reaching

The long-latency stretch response (muscle activity 50–100 ms after a mechanical perturbation) can be coordinated across multiple joints to support goal-directed actions. Here we assessed the flexibility of such coordination and whether it serves to counteract intersegmental dynamics and exploit kinematic redundancy. In three experiments, participants made planar reaches to visual targets after elbow perturbations and we assessed the coordination of long-latency stretch responses across shoulder, elbow, and wrist muscles. Importantly, targets were placed such that elbow and wrist (but not shoulder) rotations could help transport the hand to the target—a simple form of kinematic redundancy. In experiment 1 we applied perturbations of different magnitudes to the elbow and found that long-latency stretch responses in shoulder, elbow, and wrist muscles scaled with perturbation magnitude. In experiment 2 we examined the trial-by-trial relationship between long-latency stretch responses at adjacent joints and found that the magnitudes of the responses in shoulder and elbow muscles, as well as elbow and wrist muscles, were positively correlated. In experiment 3 we explicitly instructed participants how to use their wrist to move their hand to the target after the perturbation. We found that long-latency stretch responses in wrist muscles were not sensitive to our instructions, despite the fact that participants incorporated these instructions into their voluntary behavior. Taken together, our results indicate that, during reaching, the coordination of long-latency stretch responses across multiple joints counteracts intersegmental dynamics but may not be able to exploit kinematic redundancy.

Long-latency reflexes of elbow and shoulder muscles suggest reciprocal excitation of flexors, reciprocal excitation of extensors, and reciprocal inhibition between flexors and extensors

Postural corrections of the upper limb are required in tasks ranging from handling an umbrella in the changing wind to securing a wriggling baby. One complication in this process is the mechanical interaction between the different segments of the arm where torque applied at one joint induces motion at multiple joints. Previous studies have shown the long-latency reflexes of shoulder muscles (50–100 ms after a limb perturbation) account for these mechanical interactions by integrating information about motion of both the shoulder and elbow. It is less clear whether long-latency reflexes of elbow muscles exhibit a similar capability and what is the relation between the responses of shoulder and elbow muscles. The present study utilized joint-based loads tailored to the subjects' arm dynamics to induce well-controlled displacements of their shoulder and elbow. Our results demonstrate that the long-latency reflexes of shoulder and elbow muscles integrate motion from both joints: the shoulder and elbow flexors respond to extension at both joints, whereas the shoulder and elbow extensors respond to flexion at both joints. This general pattern accounts for the inherent flexion-extension coupling of the two joints arising from the arm's intersegmental dynamics and is consistent with spindle-based reciprocal excitation of shoulder and elbow flexors, reciprocal excitation of shoulder and elbow extensors, and across-joint inhibition between the flexors and extensors.

Goal-dependent modulation of the long-latency stretch response at the shoulder, elbow, and wrist

Many studies have demonstrated that muscle activity 50–100 ms after a mechanical perturbation (i.e., the long-latency stretch response) can be modulated in a manner that reflects voluntary motor control. These previous studies typically assessed modulation of the long-latency stretch response from individual muscles rather than how this response is concurrently modulated across multiple muscles. Here we investigated such concurrent modulation by having participants execute goal-directed reaches to visual targets after mechanical perturbations of the shoulder, elbow, or wrist while measuring activity from six muscles that articulate these joints. We found that shoulder, elbow, and wrist muscles displayed goal-dependent modulation of the long-latency stretch response, that the relative magnitude of participants' goal-dependent activity was similar across muscles, that the temporal onset of goal-dependent muscle activity was not reliably different across the three joints, and that shoulder muscles displayed goal-dependent activity appropriate for counteracting intersegmental dynamics. We also observed that the long-latency stretch response of wrist muscles displayed goal-dependent modulation after elbow perturbations and that the long-latency stretch response of elbow muscles displayed goal-dependent modulation after wrist perturbations. This pattern likely arises because motion at either joint could bring the hand to the visual target and suggests that the nervous system rapidly exploits such simple kinematic redundancy when processing sensory feedback to support goal-directed actions.