Literature DB >> 35601906

Random Practice Enhances Retention and Spatial Transfer in Force Field Adaptation.

Michael Herzog1, Anne Focke1, Philipp Maurus2, Benjamin Thürer1, Thorsten Stein1.   

Abstract

The contextual-interference effect is a frequently examined phenomenon in motor skill learning but has not been extensively investigated in motor adaptation. Here, we first tested experimentally if the contextual-interference effect is detectable in force field adaptation regarding retention and spatial transfer, and then fitted state-space models to the data to relate the findings to the "forgetting-and-reconstruction hypothesis". Thirty-two participants were divided into two groups with either a random or a blocked practice schedule. They practiced reaching to four targets and were tested 10 min and 24 h afterward for motor retention and spatial transfer on an interpolation and an extrapolation target, and on targets which were shifted 10 cm away. The adaptation progress was participant-specifically fitted with 4-slow-1-fast state-space models accounting for generalization and set breaks. The blocked group adapted faster (p = 0.007) but did not reach a better adaptation at practice end. We found better retention (10 min), interpolation transfer (10 min), and transfer to shifted targets (10 min and 24 h) for the random group (each p < 0.05). However, no differences were found for retention or for the interpolation target after 24 h. Neither group showed transfer to the extrapolation target. The extended state-space model could replicate the behavioral results with some exceptions. The study shows that the contextual-interference effect is partially detectable in practice, short-term retention, and spatial transfer in force field adaptation; and that state-space models provide explanatory descriptions for the contextual-interference effect in force field adaptation.
Copyright © 2022 Herzog, Focke, Maurus, Thürer and Stein.

Entities:  

Keywords:  contextual-interference effect; motor adaptation; motor retention; reaching movements; sensorimotor learning; spatial generalization; state-space model (SSM); variability of practice

Year:  2022        PMID: 35601906      PMCID: PMC9116228          DOI: 10.3389/fnhum.2022.816197

Source DB:  PubMed          Journal:  Front Hum Neurosci        ISSN: 1662-5161            Impact factor:   3.473


  91 in total

1.  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

2.  Linear hypergeneralization of learned dynamics across movement speeds reveals anisotropic, gain-encoding primitives for motor adaptation.

Authors:  Wilsaan M Joiner; Obafunso Ajayi; Gary C Sing; Maurice A Smith
Journal:  J Neurophysiol       Date:  2010-09-29       Impact factor: 2.714

3.  Mechanisms of the contextual interference effect in individuals poststroke.

Authors:  Nicolas Schweighofer; Jeong-Yoon Lee; Hui-Ting Goh; Youggeun Choi; Sung Shin Kim; Jill Campbell Stewart; Rebecca Lewthwaite; Carolee J Winstein
Journal:  J Neurophysiol       Date:  2011-08-10       Impact factor: 2.714

4.  Primitives for motor adaptation reflect correlated neural tuning to position and velocity.

Authors:  Gary C Sing; Wilsaan M Joiner; Thrishantha Nanayakkara; Jordan B Brayanov; Maurice A Smith
Journal:  Neuron       Date:  2009-11-25       Impact factor: 17.173

5.  Functional stages in the formation of human long-term motor memory.

Authors:  R Shadmehr; T Brashers-Krug
Journal:  J Neurosci       Date:  1997-01-01       Impact factor: 6.167

6.  Motor learning: the great rate debate.

Authors:  Adrian M Haith; John W Krakauer
Journal:  Curr Biol       Date:  2014-05-19       Impact factor: 10.834

7.  Electromyographic correlates of learning an internal model of reaching movements.

Authors:  K A Thoroughman; R Shadmehr
Journal:  J Neurosci       Date:  1999-10-01       Impact factor: 6.167

8.  Neural Tuning Functions Underlie Both Generalization and Interference.

Authors:  Ian S Howard; David W Franklin
Journal:  PLoS One       Date:  2015-06-25       Impact factor: 3.240

9.  Motor learning of novel dynamics is not represented in a single global coordinate system: evaluation of mixed coordinate representations and local learning.

Authors:  Max Berniker; David W Franklin; J Randall Flanagan; Daniel M Wolpert; Konrad Kording
Journal:  J Neurophysiol       Date:  2013-12-18       Impact factor: 2.714

10.  The Sensorimotor System Can Sculpt Behaviorally Relevant Representations for Motor Learning.

Authors:  David W Franklin; Alexandra V Batchelor; Daniel M Wolpert
Journal:  eNeuro       Date:  2016-08-23
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.