The effect of visual uncertainty on implicit motor adaptation

Tsay, Jonathan S; Avraham, Guy; Kim, Hyosub; Parvin, Darius E.; Wang, Zixuan; Ivry, Richard B.

doi:10.1152/jn.00493.2020

Cited by 58 publications

(102 citation statements)

References 52 publications

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“…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.…”

Section: Resultsmentioning

confidence: 99%

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

Lee

Ivry

Avraham

2021

Preprint

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Collecting data online via crowdsourcing platforms has proven to be a very efficient way to recruit individuals from a large diverse sample. While many fields in psychology have embraced online studies, the field of motor learning has lagged behind. We suspect this is because of an implicit assumption that the loss of experimental control with online data collection will be problematic for kinematic data. As a first foray to bring motor learning online, we developed a web-based platform to collect kinematic data, serving as a template for researchers to create their own online sensorimotor control and learning experiments. As a proof-of-concept, we present three visuomotor rotation experiments conducted with this platform, asking if fundamental motor learning phenomena discovered in the lab could be reproduced online. In all experiments, there was a close correspondence between the results obtained online with those previously reported from research conducted in the laboratory. As such, our web-based motor learning platform can serve as a powerful tool to exploit the benefits of crowdsourcing approaches and extend research on motor learning beyond the confines of the traditional laboratory.

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Section: Resultsmentioning

confidence: 99%

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

Lee

Ivry

Avraham

2021

Preprint

Self Cite

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“…Moreover, participants are told that their task is to ignore this motion stimulus and move their unseen hand directly to the location of the target. Despite instructions to ignore this feedback, and an explicit understanding that this feedback is not related to their performance in the task, participants show a robust implicit adaptation that is indistinguishable from implicit adaptation observed in response to conventional visuomotor rotations (Avraham et al 2021; Kim et al 2018, 2019; Morehead et al 2017; Poh et al 2021; Tsay et al 2020b, 2020c, 2020a, 2021; Vandevoorde and Orban de Xivry 2019, 2020b). In these other tasks, rotated feedback has been shown to drive implicit adaptation independent of performance error (Kim et al 2018; Leow et al 2018; Mazzoni and Krakauer 2006; Miyamoto et al 2020; Morehead et al 2017; Taylor and Ivry 2011; Vandevoorde and Orban de Xivry 2019; Wolpert et al 1995).…”

Section: Visuomotor Adaptation Is Driven By Different Error Signalsmentioning

confidence: 94%

A Synthesis of the Many Errors and Learning Processes of Visuomotor Adaptation

Morehead

Xivry

2021

Preprint

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Visuomotor adaptation has one of the oldest experimental histories in psychology and neuroscience, yet its precise nature has always been a topic of debate. Here we offer a survey and synthesis of recent work on visuomotor adaptation that we hope will prove illuminating for this ongoing dialogue. We discuss three types of error signals that drive learning in adaptation tasks: task performance error, sensory prediction-error, and a binary target hitting error. Each of these errors has been shown to drive distinct learning processes. Namely, both target hitting errors and putative sensory prediction-errors drive an implicit change in visuomotor maps, while task performance error drives learning of explicit strategy use and non-motor decision-making. Each of these learning processes contributes to the overall learning that takes place in visuomotor adaptation tasks, and although the learning processes and error signals are independent, they interact in a complex manner. We outline many task contexts where the operation of these processes is counter-intuitive and offer general guidelines for their control, measurement and interpretation. We believe this new framework unifies several disparate threads of research in sensorimotor adaptation that often seem in conflict. We conclude by explaining how this more nuanced understanding of errors and learning processes could lend itself to the analysis of other types of sensorimotor adaptation, of motor skill learning, of the neural processing underlying sensorimotor adaptation in humans, of animal models and of brain computer interfaces.

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“…The same models that adjust parameters of a state update equation over experience can account for attenuation in relearning; in the state-space model, attenuation would result if the retention factor or learning rate were reduced. Indeed, a number of factors that attenuate learning have been modeled this way, including the effect of visual feedback uncertainty [70][71][72] and task success [47,73,74].…”

Section: Possible Mechanisms For Attenuation Upon Relearningmentioning

confidence: 99%

Reexposure to a sensorimotor perturbation produces opposite effects on explicit and implicit learning processes

et al. 2021

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The motor system demonstrates an exquisite ability to adapt to changes in the environment and to quickly reset when these changes prove transient. If similar environmental changes are encountered in the future, learning may be faster, a phenomenon known as savings. In studies of sensorimotor learning, a central component of savings is attributed to the explicit recall of the task structure and appropriate compensatory strategies. Whether implicit adaptation also contributes to savings remains subject to debate. We tackled this question by measuring, in parallel, explicit and implicit adaptive responses in a visuomotor rotation task, employing a protocol that typically elicits savings. While the initial rate of learning was faster in the second exposure to the perturbation, an analysis decomposing the 2 processes showed the benefit to be solely associated with explicit re-aiming. Surprisingly, we found a significant decrease after relearning in aftereffect magnitudes during no-feedback trials, a direct measure of implicit adaptation. In a second experiment, we isolated implicit adaptation using clamped visual feedback, a method known to eliminate the contribution of explicit learning processes. Consistent with the results of the first experiment, participants exhibited a marked reduction in the adaptation function, as well as an attenuated aftereffect when relearning from the clamped feedback. Motivated by these results, we reanalyzed data from prior studies and observed a consistent, yet unappreciated pattern of attenuation of implicit adaptation during relearning. These results indicate that explicit and implicit sensorimotor processes exhibit opposite effects upon relearning: Explicit learning shows savings, while implicit adaptation becomes attenuated

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The effect of visual uncertainty on implicit motor adaptation

Cited by 58 publications

References 52 publications

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

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

A Synthesis of the Many Errors and Learning Processes of Visuomotor Adaptation

Reexposure to a sensorimotor perturbation produces opposite effects on explicit and implicit learning processes

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