Literature DB >> 33236937

The effect of visual uncertainty on implicit motor adaptation.

Jonathan S Tsay1,2, Guy Avraham1,2, Hyosub E Kim3, Darius E Parvin1,2, Zixuan Wang1, Richard B Ivry1,2.   

Abstract

Sensorimotor adaptation is influenced by both the size and variance of error information. In the present study, we varied visual uncertainty and error size in a factorial manner and evaluated their joint effect on adaptation, using a feedback method that avoids inherent limitations with standard visuomotor tasks. Uncertainty attenuated adaptation, but only when the error was small. This striking interaction highlights a novel constraint for models of sensorimotor adaptation. 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, motor adaptation is attenuated. This effect, in the context of optimal sensory integration models, has been attributed to the motor system discounting noisy feedback and thus reducing the learning rate. In its simplest form, optimal integration predicts that uncertainty would result in reduced learning for all error sizes. However, these predictions remain untested since manipulations of error size in standard visuomotor tasks introduce confounds in the degree to which performance is influenced by other learning processes such as strategy use. Here, we used a novel visuomotor task that isolates the contribution of implicit adaptation, independent of error size. In two experiments, we varied feedback uncertainty and error size in a factorial manner. At odds with the basic predictions derived from the optimal integration theory, the results show that uncertainty attenuated learning only when the error size was small but had no effect when the error size was large. We discuss possible mechanisms that may account for this interaction, considering how uncertainty may interact with the relevance assigned to the error signal or how the output of the adaptation system in terms of recalibrating the sensorimotor map may be modified by uncertainty.NEW & NOTEWORTHY Sensorimotor adaptation is influenced by both the size and variance of error information. In the present study, we varied visual uncertainty and error size in a factorial manner and evaluated their joint effect on adaptation, using a feedback method that avoids inherent limitations with standard visuomotor tasks. Uncertainty attenuated adaptation but only when the error was small. This striking interaction highlights a novel constraint for models of sensorimotor adaptation.

Entities:  

Keywords:  cerebellum; error-based learning; sensorimotor adaptation; sensory integration

Mesh:

Year:  2020        PMID: 33236937      PMCID: PMC8087384          DOI: 10.1152/jn.00493.2020

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  47 in total

1.  Bayesian integration in sensorimotor learning.

Authors:  Konrad P Körding; Daniel M Wolpert
Journal:  Nature       Date:  2004-01-15       Impact factor: 49.962

2.  Implicit and explicit components of dual adaptation to visuomotor rotations.

Authors:  Mathias Hegele; Herbert Heuer
Journal:  Conscious Cogn       Date:  2010-12

3.  Sensitivity to prediction error in reach adaptation.

Authors:  Mollie K Marko; Adrian M Haith; Michelle D Harran; Reza Shadmehr
Journal:  J Neurophysiol       Date:  2012-07-05       Impact factor: 2.714

4.  An implicit plan overrides an explicit strategy during visuomotor adaptation.

Authors:  Pietro Mazzoni; John W Krakauer
Journal:  J Neurosci       Date:  2006-04-05       Impact factor: 6.167

5.  Characteristics of Implicit Sensorimotor Adaptation Revealed by Task-irrelevant Clamped Feedback.

Authors:  J Ryan Morehead; Jordan A Taylor; Darius E Parvin; Richard B Ivry
Journal:  J Cogn Neurosci       Date:  2017-02-14       Impact factor: 3.225

6.  The influence of movement preparation time on the expression of visuomotor learning and savings.

Authors:  Adrian M Haith; David M Huberdeau; John W Krakauer
Journal:  J Neurosci       Date:  2015-04-01       Impact factor: 6.167

7.  Prism adaptation with delayed visual error signals in the monkey.

Authors:  Shigeru Kitazawa; Ping-Bo Yin
Journal:  Exp Brain Res       Date:  2002-04-10       Impact factor: 1.972

8.  Uncertainty of feedback and state estimation determines the speed of motor adaptation.

Authors:  Kunlin Wei; Konrad Körding
Journal:  Front Comput Neurosci       Date:  2010-05-11       Impact factor: 2.380

9.  Trial-by-trial transformation of error into sensorimotor adaptation changes with environmental dynamics.

Authors:  Michael S Fine; Kurt A Thoroughman
Journal:  J Neurophysiol       Date:  2007-07-05       Impact factor: 2.714

10.  How does our motor system determine its learning rate?

Authors:  Robert J van Beers
Journal:  PLoS One       Date:  2012-11-12       Impact factor: 3.240

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  7 in total

1.  The role of motor memory dynamics in structuring bodily self-consciousness.

Authors:  Ryota Ishikawa; Saho Ayabe-Kanamura; Jun Izawa
Journal:  iScience       Date:  2021-11-26

2.  Individual differences in proprioception predict the extent of implicit sensorimotor adaptation.

Authors:  Jonathan S Tsay; Hyosub E Kim; Darius E Parvin; Alissa R Stover; Richard B Ivry
Journal:  J Neurophysiol       Date:  2021-03-03       Impact factor: 2.974

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

Authors:  Guy Avraham; J Ryan Morehead; Hyosub E Kim; Richard B Ivry
Journal:  PLoS Biol       Date:  2021-03-05       Impact factor: 8.029

4.  Dissociable use-dependent processes for volitional goal-directed reaching.

Authors:  Jonathan S Tsay; Hyosub E Kim; Arohi Saxena; Darius E Parvin; Timothy Verstynen; Richard B Ivry
Journal:  Proc Biol Sci       Date:  2022-04-27       Impact factor: 5.530

Review 5.  Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks.

Authors:  Koenraad Vandevoorde; Lukas Vollenkemper; Constanze Schwan; Martin Kohlhase; Wolfram Schenck
Journal:  Sensors (Basel)       Date:  2022-03-23       Impact factor: 3.576

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

Authors:  Jonathan S Tsay; Hyosub Kim; Adrian M Haith; Richard B Ivry
Journal:  Elife       Date:  2022-08-15       Impact factor: 8.713

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

Authors:  Jonathan S Tsay; Adrian M Haith; Richard B Ivry; Hyosub E Kim
Journal:  PLoS Comput Biol       Date:  2022-03-23       Impact factor: 4.779

  7 in total

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