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

Tsay, Jonathan S; Haith, Adrian M.; Ivry, Richard B; Kim, Hyosub

doi:10.1371/journal.pcbi.1010005

Cited by 67 publications

(89 citation statements)

References 79 publications

(91 reference statements)

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“…While, implicit adaptation has been thought to be elicited by sensory prediction error (Mazzoni and Krakauer, 2006 ; Shadmehr et al, 2010 ). Additionally, it has been shown that a visuomotor rotation task without sensory prediction error but with only task error does not induce trial-by-trial implicit adaptation (Tsay et al, 2022 ). Further studies are required to specifically investigate implicit adaptation without sensory prediction error.…”

Section: Discussionmentioning

confidence: 99%

Visuomotor Adaptation of Lower Extremity Movements During Virtual Ball-Kicking Task

Moriyama

Kouzaki²,

Hagio³

2022

Front. Sports Act. Living

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Sophisticated soccer players can skillfully manipulate a ball with their feet depending on the external environment. This ability of goal-directed control in the lower limbs has not been fully elucidated, although upper limb movements have been studied extensively using motor adaptation tasks. The purpose of this study was to clarify how the goal-directed movements of the lower limbs is acquired by conducting an experiment of visuomotor adaptation in ball-kicking movements. In this study, healthy young participants with and without experience playing soccer or futsal performed ball-kicking movements. They were instructed to move a cursor representing the right foot position and shoot a virtual ball to a target on a display in front of them. During the learning trials, the trajectories of the virtual ball were rotated by 15° either clockwise or counterclockwise relative to the actual ball direction. As a result, participants adapted their lower limb movements to novel visuomotor perturbation regardless of the soccer playing experience, and changed their whole trajectories not just the kicking position during adaptation. These results indicate that the goal-directed lower limb movements can be adapted to the novel environment. Moreover, it was suggested that fundamental structure of visuomotor adaptation is common between goal-directed movements in the upper and lower limbs.

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Section: Discussionmentioning

confidence: 99%

Visuomotor Adaptation of Lower Extremity Movements During Virtual Ball-Kicking Task

Moriyama

Kouzaki²,

Hagio³

2022

Front. Sports Act. Living

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“…Because of the COVID-19 pandemic, which restricted in-person experiments, the experiments were implemented on an online platform (OnPoint) that participants accessed remotely using a laptop computer [37]. This online platform has been shown to produce similar results to in-person experiments in studies of adaptation to visuomotor transformations [37][38][39]. Participants were instructed to place the laptop on a table or desk and to sit comfortably in front of it with the laptop centered at the body midline (Figure 2a).…”

Section: Experimental Methodsmentioning

confidence: 99%

“…Due to restrictions arising from the COVID-19 pandemic, we conducted the experiments using an online platform [37]. Although the results obtained from the online platform are similar to in-person results for VMR tasks [37][38][39], the arm posture in our experiment should be controlled, as it is related to the shape of the manipulability ellipse of the arm because the posture determines the arm Jacobian [25]. We instructed the participants to adopt a similar posture across all experimental conditions, but did not control for the posture.…”

Section: Limitationsmentioning

confidence: 99%

Theoretical limits on the speed of learning inverse models explain the rate of adaptation in arm reaching tasks

Barradas

Koike

Schweighofer

2022

Preprint

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An essential aspect of human motor learning is the formation of inverse models, which map desired actions to motor commands. Inverse models can be learned by adjusting parameters in neural circuits to minimize errors in the performance of motor tasks through gradient descent. However, the theory of gradient descent establishes an upper limit on the learning speed, above which learning becomes unstable. Specifically, the eigenvalues of the Hessian of the error surface around a minimum determine the maximum speed of learning in a given task. Here, we use this theoretical framework to analyze the speed of learning in different inverse model learning architectures in a set of isometric arm-reaching tasks. We show theoretically that, for these isometric tasks, the error surface and, thus the speed of learning, are determined by the shapes of 1) the force manipulability ellipsoid of the arm and 2) the distribution of targets in the task. In particular, rounder manipulability ellipsoids generate a rounder error surface, allowing for faster learning of the inverse model. Rounder target distributions have a similar effect. We tested these predictions experimentally in a virtual quasi-isometric reaching task with a visuomotor transformation. The experimental results were consistent with our theoretical predictions. Furthermore, our analysis accounts for the speed of learning in previous experiments with incompatible and compatible virtual surgery tasks, and in visuomotor rotation tasks with different numbers of targets. By identifying aspects of a task that influence the speed of learning, our results provide theoretical principles for the design of motor tasks that allow for faster learning.

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“…It is possible that the Gaussian cloud led to lower adaptation because the added noise induced more ‘successful’ trials, given previous work showing that implicit adaptation is attenuated when the visual cursor intersects the target ( Leow et al, 2018 ; Leow et al, 2020 ; Tsay et al, 2022a ). This concern was one of the reasons why we blanked the target at reach onset in Tsay et al, 2020a .…”

Section: Empirical Support For the Proprioceptive Re-alignment Modelmentioning

confidence: 99%

Understanding implicit sensorimotor adaptation as a process of proprioceptive re-alignment

Kim

Haith

Ivry

2022

eLife

Self Cite

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Multiple learning processes contribute to successful goal-directed actions in the face of changing physiological states, biomechanical constraints, and environmental contexts. Amongst these processes, implicit sensorimotor adaptation is of primary importance, ensuring that movements remain well-calibrated and accurate. A large body of work on reaching movements has emphasized how adaptation centers on an iterative process designed to minimize visual errors. The role of proprioception has been largely neglected, thought to play a passive role in which proprioception is affected by the visual error but does not directly contribute to adaptation. Here, we present an alternative to this visuo-centric framework, outlining a model in which implicit adaptation acts to minimize a proprioceptive error, the distance between the perceived hand position and its intended goal. This proprioceptive re-alignment model (PReMo) is consistent with many phenomena that have previously been interpreted in terms of learning from visual errors, and offers a parsimonious account of numerous unexplained phenomena. Cognizant that the evidence for PReMo rests on correlational studies, we highlight core predictions to be tested in future experiments, as well as note potential challenges for a proprioceptive-based perspective on implicit adaptation.

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

Cited by 67 publications

References 79 publications

Visuomotor Adaptation of Lower Extremity Movements During Virtual Ball-Kicking Task

Visuomotor Adaptation of Lower Extremity Movements During Virtual Ball-Kicking Task

Theoretical limits on the speed of learning inverse models explain the rate of adaptation in arm reaching tasks

Understanding implicit sensorimotor adaptation as a process of proprioceptive re-alignment

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