Individual differences in proprioception predict the extent of implicit sensorimotor adaptation

Abstract

Recent studies have revealed an upper bound in motor adaptation, beyond which other learning systems may be recruited. The factors determining this upper bound are poorly understood. The multisensory integration hypothesis states that this limit arises from opposing responses to visual and proprioceptive feedback. As individuals adapt to a visual perturbation, they experience an increasing proprioceptive error in the opposite direction, and the upper bound is the point where these two error signals reach an equilibrium. Assuming that visual and proprioceptive feedback are weighted according to their variability, there should be a correlation between proprioceptive variability and the limits of adaptation. Alternatively, the proprioceptive realignment hypothesis states that the upper bound arises when the (visually biased) sensed hand position realigns with the expected sensed position (target). When a visuo-proprioceptive discrepancy is introduced, the sensed hand position is biased toward the visual cursor, and the adaptive system counteracts this discrepancy by driving the hand away from the target. This hypothesis predicts a correlation between the size of the proprioceptive shift and the upper bound of adaptation. We tested these two hypotheses by considering natural variation in proprioception and motor adaptation across individuals. We observed a modest, yet reliable correlation between the upper bound of adaptation with both proprioceptive measures (variability and shift). Although the results do not clearly favor one hypothesis over the other, they underscore the critical role of proprioception in sensorimotor adaptation.NEW & NOTEWORTHY Although the sensorimotor system uses sensory feedback to remain calibrated, this learning process is constrained, limited by the maximum degree of plasticity. The factors determining this limit remain elusive. Guided by two hypotheses, we show that individual differences in the upper bound of adaptation in response to a visual perturbation can be predicted by the bias and variability in proprioception. These results underscore the critical, but often neglected role of proprioception in human motor learning.

Keywords: cross-sensory calibration; error-based learning; motor learning; proprioception; sensorimotor adaptation.

Graphical abstract

Figure 1.

Two hypotheses concerning constraints on the upper bound of implicit adaptation. A: by the multisensory integration hypothesis, the upper bound of adaptation is the point of equilibrium between the visual SPE and the proprioceptive SPE. Since there is typically more variability in proprioception compared with vision, the proprioceptive SPE may be weighted less, requiring a greater proprioceptive SPE to offset a visual SPE. B: by the proprioceptive realignment hypothesis, the upper bound of adaptation occurs when the participant’s sensed hand position is at the target. Sensed hand position is a composite of visual-based inputs underlying the proprioceptive shift (target and cursor) and proprioception from the actual hand position. SPE, sensory prediction error.

Figure 2.

Experimental overview. A: experimental setup for proprioceptive probe trials. The experimenter (top, with their hand labeled with an “E”) sat opposite the participant (bottom) and moved their hand from the start position to a specified location. The location (e.g. 110°) was signaled to the experimenter via text that appeared on the corner of the horizontal monitor, behind a cloth which prevented the participant from seeing the text. B: after the participant’s hand was passively moved to the probe location, a cursor appeared at a random position on the screen. The participant used their left hand to move the cursor to the sensed hand position. C: in experiment 1, a rotation was applied to the cursor. The task error introduced by the rotation is nullified if the participant moves in the opposite direction of the rotation. D: in experiment 2, the cursor was clamped, independent of hand position. Participants were told to ignore the error clamp and aim straight for the target. The depicted trials in C and D provide examples of performance late in the adaptation block.

Figure 3.

Performance on adaptation and proprioception probe tasks in experiment 1. A: group means across test session (left—day 1, right—day 2). After a period of baseline trials, participants were exposed to a gradually increasing visuomotor rotation up to 30°, where it was then held constant. Participants performed blocks of visuomotor rotation trials (hand angle shown in green), no feedback aftereffect trials (hand angle shown in yellow), and proprioceptive probe trials (shift in perceived position shown in purple). Vertical dotted lines indicate block breaks. Shaded trials indicate reaching trials either with no feedback (dark gray) or with veridical feedback (light gray). Shaded regions indicate ±SE. Baseline blocks for reaching hand angle (AE0) and proprioceptive probes (PB0) are labeled. B: hand angle during no feedback aftereffect blocks. C: proprioceptive errors for each proprioceptive block. D: variability of proprioceptive judgments for each proprioceptive probe block. Thin lines indicate individual subjects. Box plots indicate min, max, median, and the 1st/3rd interquartile range. Black dots indicate the mean.

Figure 4.

Interindividual differences analyses in experiment 1. Test–retest reliability, measured across days, for aftereffect from adaptation (yellow; A), proprioceptive variability (blue; B), and proprioceptive shift (purple; C). Correlations between different dependent variables: proprioceptive variability vs. aftereffect (D), proprioceptive shift vs. aftereffect (E), and proprioceptive variability vs. proprioceptive shift (F). Black line denotes the best fit regresion line, and the shaded region indicates the 95% confidence interval.

Figure 5.

Performance on adaptation and proprioception probe tasks in experiment 2. A: group means across test session. After a period of no feedback (dark gray region) and veridical feedback (light gray region) baseline trials, participants were exposed to a visual clamp in which the feedback was offset by 15° from the target. Participants performed blocks of reaching trials (hand angle shown in green) and proprioceptive probe trials (shift in perceived position shown in purple). Vertical dotted lines indicate block breaks. Shaded regions indicate ±SE. Baseline blocks for reaching hand angle (CB0) and proprioceptive probes (PB0) are labeled. B: mean hand angle averaged over the last three clamped feedback blocks. C: proprioceptive error for each proprioceptive block. D: variability of proprioceptive judgments for each proprioceptive probe block. Thin lines indicate individual subjects. Box plots indicate min, max, median, and the 1st/3rd interquartile range. Black dots indicate the mean.

Figure 6.

Interindividual differences analyses in experiment 2. A: proprioceptive variability vs. asymptote in response to the visual clamp. B: proprioceptive shift vs. asymptote. A second correlation was performed on nonoutlier data points contained in the red rectangle, also shown in the inset. C: proprioceptive variability vs. proprioceptive shift. Black line denotes the best fit regresion line, and the shaded region indicates the 95% confidence interval.