Literature DB >> 29684760

Unifying practice schedules in the timescales of motor learning and performance.

F Martijn Verhoeven1, Karl M Newell2.   

Abstract

In this article, we elaborate from a multiple time scales model of motor learning to examine the independent and integrated effects of massed and distributed practice schedules within- and between-sessions on the persistent (learning) and transient (warm-up, fatigue) processes of performance change. The timescales framework reveals the influence of practice distribution on four learning-related processes: the persistent processes of learning and forgetting, and the transient processes of warm-up decrement and fatigue. The superposition of the different processes of practice leads to a unified set of effects for massed and distributed practice within- and between-sessions in learning motor tasks. This analysis of the interaction between the duration of the interval of practice trials or sessions and parameters of the introduced time scale model captures the unified influence of the between trial and session scheduling of practice on learning and performance. It provides a starting point for new theoretically based hypotheses, and the scheduling of practice that minimizes the negative effects of warm-up decrement, fatigue and forgetting while exploiting the positive effects of learning and retention.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Massed and distributed practice; Motor learning; Practice schedules; Time scales

Mesh:

Year:  2018        PMID: 29684760     DOI: 10.1016/j.humov.2018.04.004

Source DB:  PubMed          Journal:  Hum Mov Sci        ISSN: 0167-9457            Impact factor:   2.161


  4 in total

1.  Online and offline contributions to motor learning change with practice, but are similar across development.

Authors:  Mei-Hua Lee
Journal:  Exp Brain Res       Date:  2019-08-29       Impact factor: 1.972

2.  The neural substrate of spatial memory stabilization depends on the distribution of the training sessions.

Authors:  Valentina Mastrorilli; Eleonora Centofante; Federica Antonelli; Arianna Rinaldi; Andrea Mele
Journal:  Proc Natl Acad Sci U S A       Date:  2022-03-29       Impact factor: 12.779

3.  On Learning to Anticipate in Youth Sport.

Authors:  Tim Buszard
Journal:  Sports Med       Date:  2022-05-27       Impact factor: 11.928

4.  Comparing models of learning and relearning in large-scale cognitive training data sets.

Authors:  Aakriti Kumar; Aaron S Benjamin; Andrew Heathcote; Mark Steyvers
Journal:  NPJ Sci Learn       Date:  2022-10-04
  4 in total

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