The Effect of Visual Uncertainty on Implicit Motor Adaptation

Abstract

Sensorimotor adaptation is driven by sensory prediction errors, the difference between the predicted and actual feedback. When the position of the feedback is made uncertain, adaptation is attenuated. This effect, in the context of optimal sensory integration models, has been attributed to a weakening of the error signal driving adaptation. Here we consider an alternative hypothesis, namely that uncertainty alters the perceived location of the feedback. We present two visuomotor adaptation experiments to compare these hypotheses, varying the size and uncertainty of a visual error signal. Uncertainty attenuated learning when the error size was small but had no effect when the error size was large. This pattern of results favors the hypothesis that uncertainty does not impact the strength of the error signal, but rather, leads to mis-localization of the error. We formalize these ideas to offer a novel perspective on the effect of visual uncertainty on implicit sensorimotor adaptation.SIGNIFICANCE STATEMENTCurrent models of sensorimotor adaptation assume that the rate of learning will be related to properties of the error signal (e.g., size, consistency, relevance). Recent evidence has challenged this view, pointing to a rigid, modular system, one that automatically recalibrates the sensorimotor map in response to movement errors, with minimal constraint. In light of these developments, this study revisits the influence of feedback uncertainty on sensorimotor adaptation. Adaptation was attenuated in response to a noisy feedback signal, but the effect was only manifest for small errors and not for large errors. This interaction suggests that uncertainty does not weaken the error signal. Rather, it may influence the perceived location of the feedback and thus the change in the sensorimotor map induced by that error. These ideas are formalized to show how the motor system remains exquisitely calibrated, even if adaptation is largely insensitive to the statistics of error signals.

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

“…Following the in-person method used in Tsay et al (2020) (Jonathan Sanching Tsay, Haith, Ivry, & Kim, 2020), we varied the size of the non-contingent clamped feedback across trials. Each participant was exposed to a set of eight rotation sizes between 0 - 60°, with four of these involving clockwise rotations and the other four involving counterclockwise rotations of the same size.…”

Low vision impairs implicit sensorimotor adaptation in response to small errors, but not large errors

“…However, recent studies using novel, more advanced behavioral methods suggest that implicit adaptation is driven by an inflexible system (Kim, Morehead, Parvin, Moazzezi, & Ivry, 2018;, one that saturates when sensory prediction errors are large. Consistent with this notion of inflexibility (and contrary to the optimal integration hypothesis), we recently found that implicit adaptation was only affected by visual uncertainty when the error was small and not when the error was large (Tsay, Avraham, et al, 2020). This error size interaction motivated a new hypothesis centered on how uncertainty impacts the distribution of the perceived error location rather than the system's overall sensitivity to the error.…”

Individual differences in proprioception predict the extent of implicit sensorimotor adaptation

“…Verbal reports of sensed hand position obtained in a continuous manner during adaptation provide converging evidence of the dynamics predicted by the proprioceptive realignment hypothesis. The report data followed a striking non-monotonic function, initially biased towards the clamped cursor (away from the target), and then reversing direction (Tsay et al, 2020). However, the asymptotic value of the reports was not at the target.…”

“…Previous tests of the multisensory integration account of implicit adaptation have focused exclusively on manipulations of the visual feedback. Increasing visual variability, either by replacing a small cursor with a cloud of dots or a Gaussian blur, has been shown to decrease the rate and extent of implicit adaptation (Burge et al, 2008;Körding & Wolpert, 2004;Tsay, Avraham, et al, 2020;Wei & Körding, 2010). Surprisingly, the sensory integration models put forth to account for these effects have not measured proprioception; rather, this component has either been estimated as a free parameter or ignored entirely.…”

“…J. van Beers, Sittig, & Denier van der Gon, 1998;Robert J. van Beers, 2012;Robert J. van Beers, Wolpert, & Haggard, 2002). Consistent with this hypothesis, in the context of visuomotor adaptation, the response to a visual perturbation is reduced when noise is added to the visual feedback (Burge et al, 2008;Körding & Wolpert, 2004;Tsay, Avraham, et al, 2020;Robert J. van Beers, 2012;Wei & Körding, 2010). The corollary prediction, namely that the response to a visual perturbation should increase as a function of noise (i.e., variability) within the proprioceptive system, has not been tested.…”