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Temporal structure of motor variability is dynamically regulated and predicts motor learning ability.

ABSTRACT: Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning.

SUBMITTER: Wu HG

PROVIDER: S-EPMC4442489 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Temporal structure of motor variability is dynamically regulated and predicts motor learning ability.

Wu Howard G HG Miyamoto Yohsuke R YR Gonzalez Castro Luis Nicolas LN Ölveczky Bence P BP Smith Maurice A MA

Nature neuroscience 20140112 2

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward ...[more]

PMID: 24413700

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Project description:Previous motor learning studies based on adapting movements of the hemiparetic arm in stroke subjects have not accounted for spasticity occurring in specific joint ranges (spasticity zones), resulting in equivocal conclusions about learning capacity. We compared the ability of participants with stroke to rapidly adapt elbow extension movements to changing external load conditions outside and inside spasticity zones. Participants with stroke ( n = 12, aged 57.8 ± 9.6 yr) and healthy age-matched controls ( n = 8, 63.5 ± 9.1 yr) made rapid 40°-50° horizontal elbow extension movements from an initial (3°) to a final (6°) target. Sixteen blocks (6-10 trials/block) consisting of alternating loaded (30% maximal voluntary contraction) and nonloaded trials were made in one (controls) or two sessions (stroke; 1 wk apart). For the stroke group, the tonic stretch reflex threshold angle at which elbow flexors began to be activated during passive elbow extension was used to identify the beginning of the spasticity zone. The task was repeated in joint ranges that did or did not include the spasticity zone. Error correction strategies were identified by the angular positions before correction and compared between groups and sessions. Changes in load condition from no load to load and vice versa resulted in undershoot and overshoot errors, respectively. Stroke subjects corrected errors in 1-4 trials compared with 1-2 trials in controls. When movements did not include the spasticity zone, there was an immediate decrease in the number of trials needed to restore accuracy, suggesting that the capacity to learn may be preserved after stroke but masked by the presence of spasticity. NEW & NOTEWORTHY When arm movements were made outside, instead of inside, the range affected by spasticity, there was an immediate decrease in the number of trials needed to restore accuracy in response to a change in the external load. This suggests that motor learning processes may be preserved in patients with stroke but masked by the presence of spasticity in specific joint ranges. This has important implications for designing rehabilitation interventions predicated on motor learning principles.

Project description:Despite recent advances in robot-assisted training, the benefits of haptic guidance on motor (re)learning are still limited. While haptic guidance may increase task performance during training, it may also decrease participants' effort and interfere with the perception of the environment dynamics, hindering somatosensory information crucial for motor learning. Importantly, haptic guidance limits motor variability, a factor considered essential for learning. We propose that Model Predictive Controllers (MPC) might be good alternatives to haptic guidance since they minimize the assisting forces and promote motor variability during training. We conducted a study with 40 healthy participants to investigate the effectiveness of MPCs on learning a dynamic task. The task consisted of swinging a virtual pendulum to hit incoming targets with the pendulum ball. The environment was haptically rendered using a Delta robot. We designed two MPCs: the first MPC-end-effector MPC-applied the optimal assisting forces on the end-effector. A second MPC-ball MPC-applied its forces on the virtual pendulum ball to further reduce the assisting forces. The participants' performance during training and learning at short- and long-term retention tests were compared to a control group who trained without assistance, and a group that trained with conventional haptic guidance. We hypothesized that the end-effector MPC would promote motor variability and minimize the assisting forces during training, and thus, promote learning. Moreover, we hypothesized that the ball MPC would enhance the performance and motivation during training but limit the motor variability and sense of agency (i.e., the feeling of having control over their movements), and therefore, limit learning. We found that the MPCs reduce the assisting forces compared to haptic guidance. Training with the end-effector MPC increases the movement variability and does not hinder the pendulum swing variability during training, ultimately enhancing the learning of the task dynamics compared to the other groups. Finally, we observed that increases in the sense of agency seemed to be associated with learning when training with the end-effector MPC. In conclusion, training with MPCs enhances motor learning of tasks with complex dynamics and are promising strategies to improve robotic training outcomes in neurological patients.

Dataset Information

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability.

Publications

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability.

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