Time scales in motor learning and development.
Abstract
A theoretical framework based on the concepts and tools of nonlinear dynamical systems is advanced to account for both the persistent and transitory changes traditionally shown for the learning and development of motor skills. The multiple time scales of change in task outcome over time are interpreted as originating from the system's trajectory on an evolving attractor landscape. Different bifurcations between attractor organizations and transient phenomena can lead to exponential, power law, or S-shaped learning curves. This unified dynamical account of the functions and time scales in motor learning and development offers several new hypotheses for future research on the nature of change in learning theory.
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Cited by (10)
- A sensorimotor paradigm for Bayesian model selectionGenewein, Tim, Braun, Daniel A. · 2012 · Frontiers in Human Neuroscience
- Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot ExperimentYamashita, Yuichi, Tani, Jun · 2008 · PLoS Computational Biology
- Expert performance in sport and the dynamics of talent developmentPhillips, Elissa, Davids, Keith, Renshaw, Ian, Portus, Marc · 2010 · Sports Medicine
- Examination of gaze behaviors under in situ and video simulation task constraints reveals differences in information pickup for perception and actionDicks, Matt, Button, Chris, Davids, Keith · 2010 · Attention, Perception, & Psychophysics
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Time scales in motor learning and development. https://app.cochat.ai/papers/10.1037//0033-295x.108.1.57
- The paper
- Newell, K. M., Liu, Y.-T., & Mayer‐Kress, G. (2001). Time scales in motor learning and development. Psychological Review. https://doi.org/10.1037//0033-295x.108.1.57
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.