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 towards 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). While these results do not favor one hypothesis over the other, they underscore the critical role of proprioception in sensorimotor adaptation, and moreover, motivate a novel perspective on how these proprioceptive constraints drive implicit changes in motor behavior.

Contextual effects in sensorimotor adaptation adhere to associative learning rules

“…As such, this method eliminates the potentially confounding effects of strategic processes (McDougle et al, 2016; Kim et al, 2020). Nonetheless, participants’ reach angles gradually shift in the direction opposite to the clamp and shows the cardinal signatures of implicit motor adaptation without awareness (Morehead et al, 2017; Kim et al, 2018, 2019; Parvin et al, 2018; Tsay et al, 2020a, 2020b, 2021a, 2021b; Avraham et al, 2021; Poh et al, 2021; Vandevoorde and Orban de Xivry, 2019; Yin and Wei, 2020).…”

Interactions between sensory prediction error and task error during implicit motor learning

“…The transient sensory events associated with a target jump or flash might siphon attention away from the visual feedback, thereby weakening the overall learning signal. Alternatively, a transient distraction may have increased the likelihood that visual feedback is mis-localized, thus attenuating the motor system’s reliance on this uncertain feedback (Burge et al, 2008; Konrad P. Körding & Wolpert, 2004; Tsay, Avraham, et al, 2020; Tsay, Kim, Parvin, Stover, & Ivry, 2021; Wei & Körding, 2010). Regardless of the mechanism, our results underscore the importance of considering the distractive effect of a target jump manipulation and the consequences of this on implicit recalibration.…”

“…With this approach, we were able to increase our sample size in an efficient manner, providing greater power to detect subtle differences between target jump conditions. We used an motor learning platform (OnPoint) (Tsay, Lee, et al, 2020, 2021) and recruited participants using Amazon Mechanical Turk. Despite substantial differences between in-person and online sensorimotor learning experiments (e.g., in-person: dark room to occlude vision of the hand; online: full visibility of the hand for trackpad users), we have found that the results obtained online are quite similar to those obtained in-person (Tsay, Lee, et al, 2021).…”

Moving outside the lab: The viability of conducting sensorimotor learning studies online

“…Second, within-participant designs may result in possible transfer/order effects, which can be difficult to nullify even with counterbalancing. For instance, adaptation that incorporates explicit strategies is faster in a second session (also known as savings) [47,35,85,86,42], and implicit adaptation has been shown to attenuate upon re-learning [47,87,88,89].…”