Literature DB >> 10526344

Independent learning of internal models for kinematic and dynamic control of reaching.

J W Krakauer1, M F Ghilardi, C Ghez.   

Abstract

Psychophysical studies of reaching movements suggest that hand kinematics are learned from errors in extent and direction in an extrinsic coordinate system, whereas dynamics are learned from proprioceptive errors in an intrinsic coordinate system. We examined consolidation and interference to determine if these two forms of learning were independent. Learning and consolidation of two novel transformations, a rotated spatial reference frame and altered intersegmental dynamics, did not interfere with each other and consolidated in parallel. Thus separate kinematic and dynamic models were constructed simultaneously based on errors computed in different coordinate frames, and possibly, in different sensory modalities, using separate working-memory systems. These results suggest that computational approaches to motor learning should include two separate performance errors rather than one.

Mesh:

Year:  1999        PMID: 10526344     DOI: 10.1038/14826

Source DB:  PubMed          Journal:  Nat Neurosci        ISSN: 1097-6256            Impact factor:   24.884


  291 in total

1.  Cortical correlates of learning in monkeys adapting to a new dynamical environment.

Authors:  F Gandolfo; C Li; B J Benda; C P Schioppa; E Bizzi
Journal:  Proc Natl Acad Sci U S A       Date:  2000-02-29       Impact factor: 11.205

2.  Functional networks in motor sequence learning: abnormal topographies in Parkinson's disease.

Authors:  T Nakamura; M F Ghilardi; M Mentis; V Dhawan; M Fukuda; A Hacking; J R Moeller; C Ghez; D Eidelberg
Journal:  Hum Brain Mapp       Date:  2001-01       Impact factor: 5.038

3.  Spatial generalization from learning dynamics of reaching movements.

Authors:  R Shadmehr; Z M Moussavi
Journal:  J Neurosci       Date:  2000-10-15       Impact factor: 6.167

4.  Learning of visuomotor transformations for vectorial planning of reaching trajectories.

Authors:  J W Krakauer; Z M Pine; M F Ghilardi; C Ghez
Journal:  J Neurosci       Date:  2000-12-01       Impact factor: 6.167

5.  Learning motor synergies makes use of information on muscular load.

Authors:  J Fernández-Ruiz; C Hall-Haro; R Díaz; J Mischner; P Vergara; J C Lopez-Garcia
Journal:  Learn Mem       Date:  2000 Jul-Aug       Impact factor: 2.460

6.  Kinematics and dynamics are not represented independently in motor working memory: evidence from an interference study.

Authors:  Christine Tong; Daniel M Wolpert; J Randall Flanagan
Journal:  J Neurosci       Date:  2002-02-01       Impact factor: 6.167

7.  Changes in muscle directional tuning parallel feedforward adaptation to a visuomotor rotation.

Authors:  Aymar de Rugy; Timothy J Carroll
Journal:  Exp Brain Res       Date:  2010-05-09       Impact factor: 1.972

8.  Environmental experience within and across testing days determines the strength of human visuomotor adaptation.

Authors:  Jennifer A Semrau; Amy L Daitch; Kurt A Thoroughman
Journal:  Exp Brain Res       Date:  2011-12-06       Impact factor: 1.972

9.  It's Not (Only) the Mean that Matters: Variability, Noise and Exploration in Skill Learning.

Authors:  Dagmar Sternad
Journal:  Curr Opin Behav Sci       Date:  2018-03-01

Review 10.  The many facets of motor learning and their relevance for Parkinson's disease.

Authors:  Lucio Marinelli; Angelo Quartarone; Mark Hallett; Giuseppe Frazzitta; Maria Felice Ghilardi
Journal:  Clin Neurophysiol       Date:  2017-04-09       Impact factor: 3.708

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