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Generalizing movement patterns following shoulder fixation

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Abstract A 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 & Noteworthy Here 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. Disclosures The authors declare no conflict of interest.
Title: Generalizing movement patterns following shoulder fixation
Description:
Abstract A 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 & Noteworthy Here 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.
Disclosures The authors declare no conflict of interest.

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