Visual error amplification showed no benefit for non-naïve subjects in trunk-arm rowing
OPEN ACCESS
Loading...
Author / Producer
Date
2019-02-04
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Abstract
Motor learning is assumed to be a partly error driven process. Motor learning studies on simple movements have shown that skilled subjects benefit from training with error amplification. Findings of studies with simple movements do not necessarily transfer to complex sport movements. The goal of this work was to determine the benefit of visual error amplification for non-naïve subjects in learning a fast rowing movement.
We conducted a study comparing non-naïve subjects receiving a fading, visual feedback with visual error amplification against a control group receiving the same visual feedback without error amplification. Separate outcome metrics were applied for the domains of spatial and velocity magnitude errors. Besides error metrics, variability metrics were evaluated for both domains, such that they could be interpreted in quantitative relation to each other.
The implemented error amplification did not cause group differences in any variable. Subjects with or without error amplification reached similar absolute levels in error and variability. Possible reasons remain speculative. For implementing error amplification to the training of complex movements design decisions must be made for which an informative basis is missing, e.g. the error amplification gains.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
3
Pages / Article No.
13
Publisher
University of Innsbruck
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Motor Learning; Variability; Error Augmentation; Robot-Assisted Training; Augmented Feedback
Organisational unit
03654 - Riener, Robert / Riener, Robert
03654 - Riener, Robert / Riener, Robert
Notes
Funding
152817 - Acceleration of complex motor learning by skill level-dependent feedback design and automatic selection (SNF)