Literature DB >> 26169102

The influence of proprioceptive state on learning control of reach dynamics.

Andrea M Green1, Jean-Philippe Labelle2.   

Abstract

The motor system shows a remarkable capacity to generalize learned behavior to new contexts while simultaneously permitting learning of multiple and sometimes conflicting skills. To examine the influence of proprioceptive state on this capacity, we compared the effectiveness of changes in workspace location and limb orientation (horizontal vs. parasagittal plane posture) in facilitating learning of opposing dynamic force-field perturbations. When opposing fields were encountered in similar workspace positions and limb orientations, subjects failed to learn the two tasks. In contrast, differences in initial limb proprioceptive state were sufficient for significant learning to take place. The extent of learning was similar when the two fields were encountered in different arm orientations in a similar workspace location as compared to when learning took place in spatially separated workspace locations, consistent with the generalization of learning mainly in intrinsic joint coordinates. In keeping with these observations, examination of how trial-to-trial adaptation generalized showed that generalization tended to be greater across similar limb postures. However, when the two fields were encountered in distinct spatial locations, the extent of generalization of adaptation to one field depended on the limb orientation in which the other field was encountered. These results suggest that three-dimensional proprioceptive limb state plays an important role in modulating generalization patterns so as to permit the best compromise between broad generalization and the simultaneous learning of conflicting skills.

Entities:  

Keywords:  Adaptation; Generalization; Interference; Motor learning; Reaching

Mesh:

Year:  2015        PMID: 26169102     DOI: 10.1007/s00221-015-4366-x

Source DB:  PubMed          Journal:  Exp Brain Res        ISSN: 0014-4819            Impact factor:   1.972


  56 in total

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

Authors:  J W Krakauer; M F Ghilardi; C Ghez
Journal:  Nat Neurosci       Date:  1999-11       Impact factor: 24.884

2.  Is interlimb transfer of force-field adaptation a cognitive response to the sudden introduction of load?

Authors:  Nicole Malfait; David J Ostry
Journal:  J Neurosci       Date:  2004-09-15       Impact factor: 6.167

3.  Distinct motor plans form and retrieve distinct motor memories for physically identical movements.

Authors:  Masaya Hirashima; Daichi Nozaki
Journal:  Curr Biol       Date:  2012-02-08       Impact factor: 10.834

4.  Neuronal correlates of memory formation in motor cortex after adaptation to force field.

Authors:  Fritzie Arce; Itai Novick; Yael Mandelblat-Cerf; Eilon Vaadia
Journal:  J Neurosci       Date:  2010-07-07       Impact factor: 6.167

5.  Colored context cues can facilitate the ability to learn and to switch between multiple dynamical force fields.

Authors:  Touria Addou; Nedialko Krouchev; John F Kalaska
Journal:  J Neurophysiol       Date:  2011-04-13       Impact factor: 2.714

6.  Primary motor cortical discharge during force field adaptation reflects muscle-like dynamics.

Authors:  Anil Cherian; Hugo L Fernandes; Lee E Miller
Journal:  J Neurophysiol       Date:  2013-05-08       Impact factor: 2.714

7.  Gain field encoding of the kinematics of both arms in the internal model enables flexible bimanual action.

Authors:  Atsushi Yokoi; Masaya Hirashima; Daichi Nozaki
Journal:  J Neurosci       Date:  2011-11-23       Impact factor: 6.167

8.  Moving effortlessly in three dimensions: does Donders' law apply to arm movement?

Authors:  J F Soechting; C A Buneo; U Herrmann; M Flanders
Journal:  J Neurosci       Date:  1995-09       Impact factor: 6.167

9.  Rapid reshaping of human motor generalization.

Authors:  Kurt A Thoroughman; Jordan A Taylor
Journal:  J Neurosci       Date:  2005-09-28       Impact factor: 6.167

10.  Motor memory is encoded as a gain-field combination of intrinsic and extrinsic action representations.

Authors:  Jordan B Brayanov; Daniel Z Press; Maurice A Smith
Journal:  J Neurosci       Date:  2012-10-24       Impact factor: 6.167

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  3 in total

1.  Learning to Predict and Control the Physics of Our Movements.

Authors:  Reza Shadmehr
Journal:  J Neurosci       Date:  2017-02-15       Impact factor: 6.167

2.  Context-dependent concurrent adaptation to static and moving targets.

Authors:  Maria N Ayala; Denise Y P Henriques
Journal:  PLoS One       Date:  2018-02-08       Impact factor: 3.240

3.  Feedback Adaptation to Unpredictable Force Fields in 250 ms.

Authors:  Frédéric Crevecoeur; James Mathew; Marie Bastin; Philippe Lefèvre
Journal:  eNeuro       Date:  2020-04-29
  3 in total

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