Motor Learning

Krakauer, John W.; Hadjiosif, Alkis M.; Xu, Jing; Wong, Aaron L.; Haith, Adrian M.

doi:10.1002/cphy.c170043

Cited by 580 publications

(703 citation statements)

References 465 publications

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“…In this context, learning to reduce shoulder muscle activity is efficient because fixing the shoulder joint eliminates the torques that arise at the shoulder because of forearm rotation. Such learning takes place even when participants are not informed about the manipulation and without substantial kinematic errors in the task, which are two key pieces of information known to drive motor learning (Krakauer et al 2019) . Importantly, such learning unfolds very slowly, on a timescale of many hundreds of trials, and is incomplete even in that timeframe.…”

Section: Introductionmentioning

confidence: 99%

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

Maeda

Zdybal

Gribble

et al. 2020

Preprint

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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 incomple te (i.e., shoulder activity is not fully eliminated). Here we investigated whether and how the addit ion 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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Section: Introductionmentioning

confidence: 99%

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

Maeda

Zdybal

Gribble

et al. 2020

Preprint

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“…Much of what we do in our daily lives -be it tying our shoelaces or playing sports -relies on our brain's ability to learn and execute stereotyped task-specific motor sequences 1 . The basal ganglia (BG), a collection of phylogenetically conserved midbrain structures [2][3][4] , have been implicated in their acquisition and proper execution [5][6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

The basal ganglia can control learned motor sequences independently of motor cortex

Dhawale

Wolff

et al. 2019

Preprint

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How the basal ganglia contribute to the execution of learned motor skills has been thoroughly investigated. The two dominant models that have emerged posit roles for the basal ganglia in action selection and in the modulation of movement vigor. Here we test these models in rats trained to execute highly stereotyped and idiosyncratic task-specific motor sequences. Recordings and manipulations of neural activity in the striatum were not well explained by either model, and suggested that the basal ganglia, in particular its sensorimotor arm, are crucial for controlling the detailed kinematic structure of the learned behaviors. Importantly, the neural representations in the striatum, and the control functions they subserve, did not depend on the motor cortex. Taken together, these results extend our understanding of basal ganglia function, by suggesting that they can control and modulate lower-level subcortical motor circuits on a moment-by-moment basis to generate stereotyped learned motor sequences.

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“…This manipulation alters normal arm dynamics by eliminating the interaction torques that arise at the shoulder due to forearm rotation. Here we investigated how this type of learning is represented by looking at whether and how it generalizes to movements made in different joint configurations and thus parts of the workspace (Krakauer et al 2019;Shadmehr 2004) . Given that this type of learning unfolds slowly, in the absence of kinematic errors and that it is incomplete, we previously suggested that the nervous system may attribute our shoulder fixation manipulation to a physical change of the body as opposed to a changing environment or the feature of some hand-held tool (Maeda et al 2018) .…”

Section: Generalization Across the Workpacementioning

confidence: 99%

“…Given that generalization of motor learning is often studied in movement tasks in which movement errors are present (ie. force field learning) (Krakauer et al 2019) and that there is evidence that generalization happens as a function of the nervous system identifying the source of errors (Berniker and Kording 2008) , it was not clear whether the nervous system would generalize following shoulder fixation. Here we found that learning generalized despite the task not introducing explicit errors indicating that such errors are not required to drive generalization and posing the question about whether these types of learning share underlying neural circuits (Maeda et al 2018;Vaswani and Shadmehr 2013) .…”

Section: Generalization Across the Workpacementioning

confidence: 99%

“…Learning to reduce shoulder muscle activity is efficient in this scenario because mechanically fixing the shoulder joint cancels the interaction torques that arise at the sh oulder when the forearm rotates and thus negates the need to recruit shoulder muscles. Here we begin to address how this learning arises by investigating whether, and to what extent, learning generalizes to other experimental conditions -a well-established approach for gaining insight into the structure of motor learning (for review, see Krakauer et al 2019;Shadmehr 2004) . Given that the predictive shoulder contraction itself likely relies on an internal model of the arm's dynamics, one possibility is that learning to reduce shoulder muscle activity after fixing the shoulder joint arises because the nervous system is updating an internal model of the arm.…”

Section: Introductionmentioning

confidence: 99%

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Generalizing movement patterns following shoulder fixation

Maeda

Zdybal

Gribble

et al. 2019

Preprint

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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 & 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.

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Motor Learning

Cited by 580 publications

References 465 publications

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

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

The basal ganglia can control learned motor sequences independently of motor cortex

Generalizing movement patterns following shoulder fixation

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