Somatosensory Contribution to the Initial Stages of Human Motor Learning

Abstract

The early stages of motor skill acquisition are often marked by uncertainty about the sensory and motor goals of the task, as is the case in learning to speak or learning the feel of a good tennis serve. Here we present an experimental model of this early learning process, in which targets are acquired by exploration and reinforcement rather than sensory error. We use this model to investigate the relative contribution of motor and sensory factors to human motor learning. Participants make active reaching movements or matched passive movements to an unseen target using a robot arm. We find that learning through passive movements paired with reinforcement is comparable with learning associated with active movement, both in terms of magnitude and durability, with improvements due to training still observable at a 1 week retest. Motor learning is also accompanied by changes in somatosensory perceptual acuity. No stable changes in motor performance are observed for participants that train, actively or passively, in the absence of reinforcement, or for participants who are given explicit information about target position in the absence of somatosensory experience. These findings indicate that the somatosensory system dominates learning in the early stages of motor skill acquisition.

Significance statement: The research focuses on the initial stages of human motor learning, introducing a new experimental model that closely approximates the key features of motor learning outside of the laboratory. The finding indicates that it is the somatosensory system rather than the motor system that dominates learning in the early stages of motor skill acquisition. This is important given that most of our computational models of motor learning are based on the idea that learning is motoric in origin. This is also a valuable finding for rehabilitation of patients with limited mobility as it shows that reinforcement in conjunction with passive movement results in benefits to motor learning that are as great as those observed for active movement training.

Keywords: motor learning; motor skill learning; passive movements; reinforcement; somatosensory perception.

Copyright © 2015 the authors 0270-6474/15/3514316-11$15.00/0.

Figures

Figure 1.

Experimental setup and motor tasks. a, Reaching movements were aimed at a green stripe and were performed without vision of the arm. A thin yellow line provided the subject with visual information about the distance to the stripe, without indicating the lateral position of the hand. b, The experiment began with 15 active movements without feedback, followed by a baseline test of somatosensory acuity. Participants were then assigned to four different training groups: active versus passive movements, with versus without reinforcement. An additional control group received visual training with reinforcement but did not produce or experience any movements. Following training, participants repeated the motor and perceptual tests, in the absence of feedback. c, In one version of the experiment (n = 80), the stripe was horizontal. An unseen target area (red shaded area, 5 mm width) lay within the stripe, 15 mm to the right of the actual midline. During the training, participants in the active reinforcement condition received positive feedback (an explosion displayed on screen) whenever their movement ended within the desired target. Participants in the passive reinforced condition experienced the same movement trajectories of active participants, replayed under robot position servo-control, and also received reinforcement when the movement ended in the target area. Active and passive control participants did not receive any feedback during training. Participants in the visual reinforcement condition did not perform any movement, and instead they were shown the endpoint positions of the movements of active reinforced participants, coupled with reinforcement for successful movements. d, In a second version (n = 114), the stripe was tilted at 135°. To obtain reinforcement, movements had to end within an unseen target area (8 mm width), centered at 135°.

Figure 2.

Description of the somatosensory classification tasks. a, The robot passively moved the subjects' unseen arm through each of 10 fan-shaped trajectories. Subjects judged whether the arm was moved to the right or left. No feedback was provided. This was used for subjects in the motor task described in Figure 1c. b, For half of the subjects who participated in the task described in Figure 1d, the fan-shaped trajectories were distributed equally to the right or left of 135°. Subjects judged whether the arm was moved above or below the 135° direction. c, The other half of the participants had to judge whether the second passive movement (X), in a set of three, felt closer to the first (A) or to the third (B).

Figure 3.

Passive and active movements with reinforcement result in similar amounts of motor learning. The figure shows the sequence of motor tasks for the straight-ahead version of the experiment (a), for the version with the target at 135° (b), and for the entire dataset combined (c; training trials 151–200 from the 135° version are not shown). Dots represent the change relative to baseline movements in the absolute lateral distance of the hand from the target at movement end. Negative values indicate improvement. The pre-training and post-training blocks involved 15 active movements in the absence of reinforcement. Participants that received reinforcement during the training (Reinforcement groups) show reduced error in post-training movements, regardless of whether the training involves self-generated active movement or passive arm displacement. Participants who did not receive reinforcement (control groups) do not show learning. The pattern of results is similar for the straight-ahead and 135° versions of the experiment. Dots represent mean ± SE.

Figure 4.

a, The percentage of reinforced trials in the Active reinforcement group increases over the course of training. The blue trace represents the average proportion of reinforced trials (±SE). b, The averaged force applied to the handle during the passive training procedure was ∼1.8 N. The applied force was comparable for the Passive reinforcement group (red trace) and for the Passive control group (cyan trace). c, Results of the GAM analysis applied to the movement error change scores for the straight-ahead and 135° datasets combined (see also Fig. 3, bottom right). Shaded areas represent Bayesian CIs. Movement error in the passive control group varied according to a nonlinear smooth function, with a significant improvement compared with baseline for the first three trials following the exposure to nonreinforced passive movements, and a progressive washout of motor learning in the subsequent trials. d, Participants that receive visual information about the target position during training, but do not perform active or passive movements, do not show evidence of learning, relative to baseline (captions and conventions are as in Fig. 3).

Figure 5.

Motor learning following active and passive reinforced training is retained at 1 week interval. No learning or retention is seen following nonreinforced passive movements. Bars represent the change in motor performance, relative to the initial baseline, measured immediately following training (yellow bars) and after a 1 week interval during which no further training occurred (purple bars). Negative numbers indicate improvement in motor performance relative to the baseline. a, Results for the perpendicular deviation at movement peak velocity. b, Deviation at movement endpoint. *p < 0.05.

Figure 6.

Participants who received reinforcement during training improved somatosensory classification accuracy compared with baseline. No systematic changes were seen for control participants who did not receive reinforcement. Changes in somatosensory classification accuracy are shown separately for the straight-ahead version of the experiment (a), for the version with the target at 135° (b), and for the combined dataset (c).