Did I do that? Detecting a perturbation to visual feedback in a reaching task

Abstract

The motor system executes actions in a highly stereotyped manner despite the high number of degrees of freedom available. Studies of motor adaptation leverage this fact by disrupting, or perturbing, visual feedback to measure how the motor system compensates. To elicit detectable effects, perturbations are often large compared to trial-to-trial reach endpoint variability. However, awareness of large perturbations can elicit qualitatively different compensation processes than unnoticeable ones can. The current experiment measures the perturbation detection threshold, and investigates how humans combine proprioception and vision to decide whether displayed reach endpoint errors are self-generated only, or are due to experimenter-imposed perturbation. We scaled or rotated the position of the visual feedback of center-out reaches to targets and asked subjects to indicate whether visual feedback was perturbed. Subjects detected perturbations when they were at least 1.5 times the standard deviation of trial-to-trial endpoint variability. In contrast to previous studies, subjects suboptimally combined vision and proprioception. Instead of using proprioceptive input, they responded based on the final (possibly perturbed) visual feedback. These results inform methodology in motor system experimentation, and more broadly highlight the ability to attribute errors to one's own motor output and combine visual and proprioceptive feedback to make decisions.

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

“…By including both clockwise and counterclockwise rotations, there is no cumulative measure of learning; rather, the analysis focuses on trial-to-trial changes in heading angle (Figure 3a) (Avraham et al, 2019; Hutter & Taylor, 2018; Körding & Wolpert, 2004; Marko et al, 2012; Jonathan S. Tsay, Avraham, et al, 2020; Wei & Körding, 2009, 2010). Even if the feedback is contingent on hand position, learning with this method is assumed to be entirely implicit since these trial-by-trial perturbations, if relatively small, fall within the window of variation that arises from motor noise (Avraham et al, 2019; Gaffin-Cahn, Hudson, & Landy, 2019). Variable perturbations can also be employed with non-contingent clamped feedback, with the instructions providing a way to ensure that the behavioral changes are automatic and implicit.…”

“…Even if the feedback is contingent on hand position, learning with this method is assumed to be entirely implicit since these trial-by-trial perturbations, if relatively small, fall within the window of variation that arises from motor noise (Avraham et al, 2019;Gaffin-Cahn, Hudson, & Landy, 2019). Variable perturbations can also be employed with non-contingent clamped feedback, with the instructions providing a way to ensure that the behavioral changes are automatic and implicit.…”

Assessing explicit strategies in force field adaptation

“…However, we also note that more simple target arrangements are not uncommon (Brennan and Smith 2015;Heald et al 2018b;Hirashima and Nozaki 2012;Howard and Franklin 2016;Keisler and Shadmehr 2010;Nozaki et al 2016;Sarwary et al 2015;Stockinger et al 2015;Vaswani and Shadmehr 2013). Third, we used rather large field constants in experiment 3; lower field constants would likely spur less explicit learning, similar to the way smaller or gradually introduced cursor rotations do (Gaffin-Cahn et al 2019;Kagerer et al 2006;Malfait and Ostry 2004;Werner et al 2014). Overall, it seems likely that the explicit component plays less of a role in force field paradigms that use small field constants, multiple targets, and incentives to reduce strategy use.…”

A Revised Computational Neuroanatomy for Motor Control

“…This connects to the familiar credit assignment problem (Wolpert and Landy, 2012), as an error can be assigned to different representations of the body and the task. The system relies on various cues, priors and heuristics to resolve the source of its errors (Berniker and Kording, 2008;Wei and Körding, 2009), although the computational details are still being explored (Gaffin-Cahn et al, 2019;McDougle et al, 2016;Parvin et al, 2018).…”