Literature DB >> 29995600

Using gaze behavior to parcellate the explicit and implicit contributions to visuomotor learning.

Anouk J de Brouwer1, Mohammed Albaghdadi2, J Randall Flanagan1,2, Jason P Gallivan1,2,3.   

Abstract

Successful motor performance relies on our ability to adapt to changes in the environment by learning novel mappings between motor commands and sensory outcomes. Such adaptation is thought to involve two distinct mechanisms: an implicit, error-based component linked to slow learning and an explicit, strategic component linked to fast learning and savings (i.e., faster relearning). Because behavior, at any given moment, is the resultant combination of these two processes, it has remained a challenge to parcellate their relative contributions to performance. The explicit component to visuomotor rotation (VMR) learning has recently been measured by having participants verbally report their aiming strategy used to counteract the rotation. However, this procedure has been shown to magnify the explicit component. Here we tested whether task-specific eye movements, a natural component of reach planning, but poorly studied in motor learning tasks, can provide a direct readout of the state of the explicit component during VMR learning. We show, by placing targets on a visible ring and including a delay between target presentation and reach onset, that individual differences in gaze patterns during sensorimotor learning are linked to participants' rates of learning and their expression of savings. Specifically, we find that participants who, during reach planning, naturally fixate an aimpoint rotated away from the target location, show faster initial adaptation and readaptation 24 h later. Our results demonstrate that gaze behavior cannot only uniquely identify individuals who implement cognitive strategies during learning but also how their implementation is linked to differences in learning. NEW & NOTEWORTHY Although it is increasingly well appreciated that sensorimotor learning is driven by two separate components, an error-based process and a strategic process, it has remained a challenge to identify their relative contributions to performance. Here we demonstrate that task-specific eye movements provide a direct read-out of explicit strategies during sensorimotor learning in the presence of visual landmarks. We further show that individual differences in gaze behavior are linked to learning rate and savings.

Entities:  

Keywords:  eye movements; motor adaptation; motor learning; reaching visuomotor rotation

Mesh:

Year:  2018        PMID: 29995600      PMCID: PMC6230798          DOI: 10.1152/jn.00113.2018

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


  55 in total

Review 1.  The posterior parietal cortex: sensorimotor interface for the planning and online control of visually guided movements.

Authors:  Christopher A Buneo; Richard A Andersen
Journal:  Neuropsychologia       Date:  2005-11-21       Impact factor: 3.139

2.  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

3.  Cerebellar contributions to locomotor adaptations during splitbelt treadmill walking.

Authors:  Susanne M Morton; Amy J Bastian
Journal:  J Neurosci       Date:  2006-09-06       Impact factor: 6.167

Review 4.  Fast and slow feedback loops for the visual correction of spatial errors in a pointing task: a reappraisal.

Authors:  J Paillard
Journal:  Can J Physiol Pharmacol       Date:  1996-04       Impact factor: 2.273

5.  Gaze locations affect explicit process but not implicit process during visuomotor adaptation.

Authors:  Miya K Rand; Sebastian Rentsch
Journal:  J Neurophysiol       Date:  2014-09-24       Impact factor: 2.714

6.  Mental rotation of three-dimensional objects.

Authors:  R N Shepard; J Metzler
Journal:  Science       Date:  1971-02-19       Impact factor: 47.728

7.  Adaptation to visuomotor transformations: consolidation, interference, and forgetting.

Authors:  John W Krakauer; Claude Ghez; M Felice Ghilardi
Journal:  J Neurosci       Date:  2005-01-12       Impact factor: 6.167

8.  Humans use continuous visual feedback from the hand to control fast reaching movements.

Authors:  Jeffrey A Saunders; David C Knill
Journal:  Exp Brain Res       Date:  2003-08-06       Impact factor: 1.972

9.  Flexible cognitive strategies during motor learning.

Authors:  Jordan A Taylor; Richard B Ivry
Journal:  PLoS Comput Biol       Date:  2011-03-03       Impact factor: 4.475

10.  Parallel Specification of Visuomotor Feedback Gains during Bimanual Reaching to Independent Goals.

Authors:  Anouk J de Brouwer; Tayler Jarvis; Jason P Gallivan; J Randall Flanagan
Journal:  eNeuro       Date:  2017-03-10
View more
  13 in total

1.  Task Errors Drive Memories That Improve Sensorimotor Adaptation.

Authors:  Li-Ann Leow; Welber Marinovic; Aymar de Rugy; Timothy J Carroll
Journal:  J Neurosci       Date:  2020-02-06       Impact factor: 6.167

2.  A rapid visuomotor response on the human upper limb is selectively influenced by implicit motor learning.

Authors:  Chao Gu; J Andrew Pruszynski; Paul L Gribble; Brian D Corneil
Journal:  J Neurophysiol       Date:  2018-11-14       Impact factor: 2.714

3.  Did We Get Sensorimotor Adaptation Wrong? Implicit Adaptation as Direct Policy Updating Rather than Forward-Model-Based Learning.

Authors:  Alkis M Hadjiosif; John W Krakauer; Adrian M Haith
Journal:  J Neurosci       Date:  2021-02-08       Impact factor: 6.167

4.  Assessing and defining explicit processes in visuomotor adaptation.

Authors:  S Heirani Moghaddam; R Chua; E K Cressman
Journal:  Exp Brain Res       Date:  2021-04-28       Impact factor: 1.972

5.  Delay of gaze fixation during reaching movement with the non-dominant hand to a distant target.

Authors:  Miya K Rand; Shannon D R Ringenbach
Journal:  Exp Brain Res       Date:  2022-04-02       Impact factor: 1.972

6.  Competition between parallel sensorimotor learning systems.

Authors:  Scott T Albert; Jihoon Jang; Shanaathanan Modchalingam; Bernard Marius 't Hart; Denise Henriques; Gonzalo Lerner; Valeria Della-Maggiore; Adrian M Haith; John W Krakauer; Reza Shadmehr
Journal:  Elife       Date:  2022-02-28       Impact factor: 8.713

7.  Human Variation in Error-Based and Reinforcement Motor Learning Is Associated With Entorhinal Volume.

Authors:  Anouk J de Brouwer; Corson N Areshenkoff; Mohammad R Rashid; J Randall Flanagan; Jordan Poppenk; Jason P Gallivan
Journal:  Cereb Cortex       Date:  2022-08-03       Impact factor: 4.861

8.  Human decision making anticipates future performance in motor learning.

Authors:  Joshua B Moskowitz; Daniel J Gale; Jason P Gallivan; Daniel M Wolpert; J Randall Flanagan
Journal:  PLoS Comput Biol       Date:  2020-02-28       Impact factor: 4.475

9.  Eye Movements during Visuomotor Adaptation Represent Only Part of the Explicit Learning.

Authors:  Zohar Bromberg; Opher Donchin; Shlomi Haar
Journal:  eNeuro       Date:  2019-12-23

10.  Dissociable cognitive strategies for sensorimotor learning.

Authors:  Samuel D McDougle; Jordan A Taylor
Journal:  Nat Commun       Date:  2019-01-03       Impact factor: 14.919

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.