Proprioceptive short-term memory in passive motor learning

Abstract

A physical trainer often physically guides a learner's limbs to teach an ideal movement, giving the learner proprioceptive information about the movement to be reproduced later. This instruction requires the learner to perceive kinesthetic information and store the instructed information temporarily. Therefore, (1) proprioceptive acuity to accurately perceive the taught kinesthetics and (2) short-term memory to store the perceived information are two critical functions for reproducing the taught movement. While the importance of proprioceptive acuity and short-term memory has been suggested for active motor learning, little is known about passive motor learning. Twenty-one healthy adults (mean age 25.6 years, range 19-38 years) participated in this study to investigate whether individual learning efficiency in passively guided learning is related to these two functions. Consequently, learning efficiency was significantly associated with short-term memory capacity. In particular, individuals who could recall older sensory stimuli showed better learning efficiency. However, no significant relationship was observed between learning efficiency and proprioceptive acuity. A causal graph model found a direct influence of memory on learning and an indirect effect of proprioceptive acuity on learning via memory. Our findings suggest the importance of a learner's short-term memory for effective passive motor learning.

Figure 1

Example of passive physical guidance of movement. (a) A sports instructor grasps the learner’s body and guides movements to teach a swing form through proprioception. (b) The learner then actively swings the racket while recalling the sensory experiences provided by the instructor.

Figure 2

Experimental setup and tasks to measure participants’ proprioceptive performance. (a) An exoskeleton robot guided the participant’s elbow joint in flexion and extension directions in the horizontal plane. (b) Photographs of the exoskeleton robot from the frontal view. (c) Proprioceptive short-term memory task. (d) Proprioceptive judgment task.

Figure 3

Passive physical guidance task. (a) Task procedure (b) Target trajectory of elbow joint, along which the exoskeleton robot moved the participant’s forearm (inset) during the instruction periods.

Figure 4

Improvement in trajectory learning and its relationship to proprioceptive task performance. (a) Reproduced trajectories of 10-s elbow movements in the pre-learning (left) and post-learning (right) tests. The mean (dark gray) and 95% CI (light gray) across participants are shown. Green dotted lines indicate the target trajectory (reference). (b) Mean cross-correlation coefficients between the target and reproduced trajectories in the pre-learning and post-learning tests. A circle indicates the performance of each participant. (c) Relationship between proprioceptive short-term memory and improvement in trajectory reproduction (Post–Pre). (d) Relationship between proprioceptive judgment and improvement in trajectory reproduction. Circles represent individual participants. Lines represent the regression line, and dotted lines represent the 95% CI.

Figure 5

Chronological change in the relationship between short-term memory performance and improvement in trajectory reproduction. (a) Scatter plots show the relationship between short-term memory performance (sensitivity) and improvement in trajectory reproduction. Three panels show the relationships for the three memory angles chronologically. (b) A scatterplot showing the relationship between the memory preference for the temporal order of sensory experiences (sensitivity for the most recent item [angle 3] − sensitivity for the oldest item [angle 1]) and improvement in trajectory reproduction. Participants who remembered the old item more significantly improved trajectory reproduction. Circles represent individual participants. Lines represent the regression line, and dotted lines represent the 95% CI.

Figure 6

Effective acyclic graph estimated by DirectLiNGAM. (a) Causal structure of the baseline model. (b) Effective acyclic graph and causal effects estimated by DirectLiNGAM. Red frames indicate exogenous variables not determined by other variables within the estimated model.