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.

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

10.1101/2020.04.05.026757 ◽

2020 ◽

Author(s):

Rodrigo S. Maeda ◽

Rhonda Kersten ◽

J. Andrew Pruszynski

Keyword(s):

Feedback Control ◽

Muscle Activity ◽

Shoulder Joint ◽

Feedforward Control ◽

Neural Mechanisms ◽

Shoulder Muscle ◽

Intersegmental Dynamics ◽

Systematic Reduction ◽

Elbow Motion ◽

Feedback Responses

AbstractPrevious 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.

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

10.1101/2020.03.25.008466 ◽

2020 ◽

Cited By ~ 1

Author(s):

Rodrigo S. Maeda ◽

Julia M. Zdybal ◽

Paul L. Gribble ◽

J. Andrew Pruszynski

Keyword(s):

Muscle Activity ◽

Shoulder Joint ◽

The Third ◽

Task Instructions ◽

Visual Biofeedback ◽

Shoulder Muscle ◽

Intersegmental Dynamics ◽

Elbow Movement ◽

Human Participants ◽

And Task

AbstractGenerating 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.

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Generalizing movement patterns following shoulder fixation

10.1101/678623 ◽

2019 ◽

Cited By ~ 1

Author(s):

Rodrigo S. Maeda ◽

Julia M. Zdybal ◽

Paul L. Gribble ◽

J. Andrew Pruszynski

Keyword(s):

Feedback Control ◽

Shoulder Joint ◽

Substantial Reduction ◽

Physical Property ◽

Movement Patterns ◽

Forearm Rotation ◽

Reaching Task ◽

General Internal ◽

Shoulder Muscles ◽

Interaction Torques

AbstractA common goal of motor learning is generalizing newly learned movement patterns beyond the training context. Here we tested whether learning a new physical property of the arm during self-initiated reaching generalizes to new arm configurations. One hundred human participants performed a single-joint elbow reaching task and/or countered mechanical perturbations that created pure elbow motion. Participants did so with the shoulder joint either free to rotate or locked by the robotic manipulandum. With the shoulder free, we found activation of shoulder extensor muscles for pure elbow extension trials, as required to counter the interaction torques that arise at the shoulder due to forearm rotation. After locking the shoulder joint, we found a substantial reduction in shoulder muscle activity that developed slowly over many trials. This reduction is appropriate because locking the shoulder joint cancels the interaction torques that arise at the shoulder to do forearm rotation and thus removes the need to activate shoulder muscles. In our first three experiments, we tested whether this reduction generalizes when reaching is self-initiated in (1) a different initial shoulder orientation, (2) a different initial elbow orientation and (3) for a different reach distance/speed. We found reliable generalization across initial shoulder orientation and reach distance/speed but not for initial elbow orientation. In our fourth experiment, we tested whether generalization is also transferred to feedback control by applying mechanical perturbations and observing reflex responses in a distinct shoulder orientation. We found robust transfer to feedback control.New & NoteworthyHere we show that learning to reduce shoulder muscles activity following shoulder fixation generalizes to other movement conditions but does not generalize globally, indicating that the nervous system does not implement such learning by modifying a general internal model of arm dynamics.DisclosuresThe authors declare no conflict of interest.

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Motor learning and transfer: from feedback to feedforward control

10.1101/845933 ◽

2019 ◽

Author(s):

Rodrigo S. Maeda ◽

Paul L. Gribble ◽

J. Andrew Pruszynski

Keyword(s):

Motor Learning ◽

Muscle Activity ◽

Shoulder Joint ◽

Stretch Reflex ◽

Feedforward Control ◽

Motor Task ◽

Joint Torques ◽

Motor Commands ◽

Shoulder Muscle ◽

Long Latency

AbstractPrevious work has demonstrated that when learning a new motor task, the nervous system modifies feedforward (ie. voluntary) motor commands and that such learning transfers to fast feedback (ie. reflex) responses evoked by mechanical perturbations. Here we show the inverse, that learning new feedback responses transfers to feedforward motor commands. Sixty human participants (34 females) used a robotic exoskeleton and either 1) received short duration mechanical perturbations (20 ms) that created pure elbow rotation or 2) generated self-initiated pure elbow rotations. They did so with the shoulder joint free to rotate (normal arm dynamics) or locked (altered arm dynamics) by the robotic manipulandum. With the shoulder unlocked, the perturbation evoked clear shoulder muscle activity in the long-latency stretch reflex epoch (50-100ms post-perturbation), as required for countering the imposed joint torques, but little muscle activity thereafter in the so-called voluntary response. After locking the shoulder joint, which alters the required joint torques to counter pure elbow rotation, we found a reliable reduction in the long-latency stretch reflex over many trials. This reduction transferred to feedforward control as we observed 1) a reduction in shoulder muscle activity during self-initiated pure elbow rotation trials and 2) kinematic errors (ie. aftereffects) in the direction predicted when failing to compensate for normal arm dynamics, even though participants never practiced self-initiated movements with the shoulder locked. Taken together, our work shows that transfer between feedforward and feedback control is bidirectional, furthering the notion that these processes share common neural circuits that underlie motor learning and transfer.

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

10.1101/362699 ◽

2018 ◽

Author(s):

Rodrigo S. Maeda ◽

Tyler Cluff ◽

Paul L. Gribble ◽

J. Andrew Pruszynski

Keyword(s):

Nervous System ◽

Feedback Control ◽

Shoulder Joint ◽

Internal Model ◽

Feedforward Control ◽

Substantial Reduction ◽

Baseline Phase ◽

Intersegmental Dynamics ◽

Adaptation Phase ◽

Feedback Responses

AbstractHumans 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.

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

10.1101/115675 ◽

2017 ◽

Author(s):

Rodrigo S. Maeda ◽

Tyler Cluff ◽

Paul L. Gribble ◽

J. Andrew Pruszynski

Keyword(s):

Nervous System ◽

Feedback Control ◽

Muscle Activity ◽

Joint Movement ◽

Movement Onset ◽

Shoulder Muscle ◽

Limb Dynamics ◽

Intersegmental Dynamics ◽

Interaction Torques ◽

Wrist Joints

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.

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

Journal of Neurophysiology ◽

10.1152/jn.00178.2017 ◽

2017 ◽

Vol 118 (4) ◽

pp. 1984-1997 ◽

Cited By ~ 16

Author(s):

Rodrigo S. Maeda ◽

Tyler Cluff ◽

Paul L. Gribble ◽

J. Andrew Pruszynski

Keyword(s):

Nervous System ◽

Feedback Control ◽

Muscle Activity ◽

Joint Movement ◽

Movement Onset ◽

Shoulder Muscle ◽

Limb Dynamics ◽

Intersegmental Dynamics ◽

Interaction Torques ◽

Wrist Joints

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.

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