Literature DB >> 28356476

Transfer of dynamic motor skills acquired during isometric training to free motion.

Alejandro Melendez-Calderon1,2, Michael Tan2,3, Moria Fisher Bittmann2,3, Etienne Burdet4, James L Patton5,2,3.   

Abstract

Recent studies have explored the prospects of learning to move without moving, by displaying virtual arm movement related to exerted force. However, it has yet to be tested whether learning the dynamics of moving can transfer to the corresponding movement. Here we present a series of experiments that investigate this isometric training paradigm. Subjects were asked to hold a handle and generate forces as their arms were constrained to a static position. A precise simulation of reaching was used to make a graphic rendering of an arm moving realistically in response to the measured interaction forces and simulated environmental forces. Such graphic rendering was displayed on a horizontal display that blocked their view to their actual (statically constrained) arm and encouraged them to believe they were moving. We studied adaptation of horizontal, planar, goal-directed arm movements in a velocity-dependent force field. Our results show that individuals can learn to compensate for such a force field in a virtual environment and transfer their new skills to the actual free motion condition, with performance comparable to practice while moving. Such nonmoving techniques should impact various training conditions when moving may not be possible.NEW & NOTEWORTHY This study provided early evidence supporting that training movement skills without moving is possible. In contrast to previous studies, our study involves 1) exploiting cross-modal sensory interactions between vision and proprioception in a motionless setting to teach motor skills that could be transferable to a corresponding physical task, and 2) evaluates the movement skill of controlling muscle-generated forces to execute arm movements in the presence of external forces that were only virtually present during training.
Copyright © 2017 the American Physiological Society.

Entities:  

Keywords:  isometric; motor adaptation; motor learning; visual feedback

Mesh:

Year:  2017        PMID: 28356476      PMCID: PMC5498735          DOI: 10.1152/jn.00614.2016

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


  71 in total

1.  Composition and decomposition of internal models in motor learning under altered kinematic and dynamic environments.

Authors:  J R Flanagan; E Nakano; H Imamizu; R Osu; T Yoshioka; M Kawato
Journal:  J Neurosci       Date:  1999-10-15       Impact factor: 6.167

2.  Bayesian integration in sensorimotor learning.

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

3.  Neural representations involved in observed, imagined, and imitated actions are dissociable and hierarchically organized.

Authors:  Kristen L Macuga; Scott H Frey
Journal:  Neuroimage       Date:  2011-10-08       Impact factor: 6.556

4.  Concurrent adaptation of force and impedance in the redundant muscle system.

Authors:  Keng Peng Tee; David W Franklin; Mitsuo Kawato; Theodore E Milner; Etienne Burdet
Journal:  Biol Cybern       Date:  2009-11-21       Impact factor: 2.086

5.  Internal models in the cerebellum.

Authors:  D M Wolpert; R C Miall; M Kawato
Journal:  Trends Cogn Sci       Date:  1998-09-01       Impact factor: 20.229

Review 6.  The mammalian muscle spindle and its central control.

Authors:  M Hulliger
Journal:  Rev Physiol Biochem Pharmacol       Date:  1984       Impact factor: 5.545

7.  Visuo-proprioceptive interactions during adaptation of the human reach.

Authors:  Timothy Judkins; Robert A Scheidt
Journal:  J Neurophysiol       Date:  2013-11-20       Impact factor: 2.714

8.  Impairments of reaching movements in patients without proprioception. I. Spatial errors.

Authors:  J Gordon; M F Ghilardi; C Ghez
Journal:  J Neurophysiol       Date:  1995-01       Impact factor: 2.714

9.  Convergence of descending and various peripheral inputs onto common propriospinal-like neurones in man.

Authors:  D Burke; J M Gracies; D Mazevet; S Meunier; E Pierrot-Deseilligny
Journal:  J Physiol       Date:  1992-04       Impact factor: 5.182

10.  Estimating the sources of motor errors for adaptation and generalization.

Authors:  Max Berniker; Konrad Kording
Journal:  Nat Neurosci       Date:  2008-11-16       Impact factor: 24.884

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Review 4.  Elucidating Sensorimotor Control Principles with Myoelectric Musculoskeletal Models.

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