Quantifying the functional role of discrete movement variability: Links to adaptation and learning

Thesis


Taylor, Paul Geoffrey. (2016). Quantifying the functional role of discrete movement variability: Links to adaptation and learning [Thesis]. https://doi.org/10.4226/66/5a9dbae633620
AuthorsTaylor, Paul Geoffrey
Qualification nameDoctor of Philosophy (PhD)
Abstract

Introduction: Movement variability can be defined as the variance in human movement from one trial or cycle to the next, often when attempting to maintain dynamic equilibrium (in the case of continuous skills) or achieve consistent movement outcome (for discrete skills). Some theoretical perspectives of motor control consider movement variability to be deleterious. However, the dynamical systems perspective proposes beneficial and functional roles for movement variability. Within this view variability has developed as an independent theme of research that has gained momentum over the past 25 years, attracting focus from various sub-disciplines within the field with a major contribution from sports biomechanics. The previous research within the field of movement variability has proposed that these functional roles include reducing the risk of injury, enabling coordination change and facilitating adaptation to varying task or environmental constraints. This thesis is primarily constituted of four sequential studies designed to further the method-related approach to, and theoretical understanding of, the interaction between variability in discrete movement and adaptation.

Year2016
PublisherAustralian Catholic University
Digital Object Identifier (DOI)https://doi.org/10.4226/66/5a9dbae633620
Research GroupSports Performance, Recovery, Injury and New Technologies (SPRINT) Research Centre
Final version
Publication dates01 Sep 2016
Permalink -

https://acuresearchbank.acu.edu.au/item/884wq/quantifying-the-functional-role-of-discrete-movement-variability-links-to-adaptation-and-learning

  • 80
    total views
  • 131
    total downloads
  • 3
    views this month
  • 2
    downloads this month
These values are for the period from 19th October 2020, when this repository was created.

Export as