Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Cutting through the noise: reducing bias in motor adaptation analysis

View ORCID ProfileDaniel H. Blustein, Ahmed W. Shehata, Erin S. Kuylenstierna, Kevin B. Englehart, Jonathon W. Sensinger
doi: https://doi.org/10.1101/2020.11.25.397992
Daniel H. Blustein
1Department of Psychology and Neuroscience Program, Rhodes College, Memphis, TN, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Daniel H. Blustein
  • For correspondence: blusteind@rhodes.edu
Ahmed W. Shehata
2Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Erin S. Kuylenstierna
1Department of Psychology and Neuroscience Program, Rhodes College, Memphis, TN, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kevin B. Englehart
3Institute of Biomedical Engineering and Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jonathon W. Sensinger
3Institute of Biomedical Engineering and Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

During goal-directed movements, the magnitude of error correction by a person on a subsequent movement provides important insight into a person’s motor learning dynamics. Observed differences in trial-by-trial adaptation rates might indicate different relative weighting placed on the various sources of information that inform a movement, e.g. sensory feedback, control predictions, or internal model expectations. Measuring this trial-by-trial adaptation rate is not straightforward, however, since externally observed data are masked by noise from several sources and influenced by inaccessible internal processes. Adaptation to perturbation has been used to measure error adaptation as the introduced external disturbance is sufficiently large to overshadow other noise sources. However, perturbation analysis is difficult to implement in real-world scenarios, requires a large number of movement trials to accommodate infrequent perturbations, and the paradigm itself might affect the movement dynamics being observed. Here we focus on error adaptation during unperturbed and naturalistic movements. With increasing motor noise, the conventional estimation of trial-by-trial adaptation increases, a counterintuitive finding that is the consequence of systematic bias in the estimate due to noise masking the learner’s intention. We present an analytic solution relying on stochastic signal processing to reduce this effect of noise, producing an estimate of motor adaptation with reduced bias. The result is an improved estimate of trial-by-trial adaptation in a human learner compared to conventional methods. We demonstrate the effectiveness of the new method in analyzing simulated and empirical movement data under different noise conditions. The analytic approach is applicable across different types of movements in varied contexts and should replace the regression analysis method in future motor analysis studies.

Author Summary When a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction provides insight into how the nervous system is operating, particularly in regard to how much confidence a person places in different sources of information such as sensory feedback or motor command reproducibility. Traditional analysis of movement has required carefully controlled laboratory conditions, limiting the usefulness of motor analysis in clinical and everyday environments. Here we present a new computational method that can be accurately applied to typical movements. Counterintuitive findings of the established approach are corrected by the proposed method. This method will provide a common framework for researchers to analyze movements while extending dynamic motor adaptation analysis capabilities to clinical and non-laboratory settings.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://osf.io/4vsmd/?view_only=9fe78f28eefe4a08aafa56e84cbd9397

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted November 26, 2020.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Cutting through the noise: reducing bias in motor adaptation analysis
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Cutting through the noise: reducing bias in motor adaptation analysis
Daniel H. Blustein, Ahmed W. Shehata, Erin S. Kuylenstierna, Kevin B. Englehart, Jonathon W. Sensinger
bioRxiv 2020.11.25.397992; doi: https://doi.org/10.1101/2020.11.25.397992
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Cutting through the noise: reducing bias in motor adaptation analysis
Daniel H. Blustein, Ahmed W. Shehata, Erin S. Kuylenstierna, Kevin B. Englehart, Jonathon W. Sensinger
bioRxiv 2020.11.25.397992; doi: https://doi.org/10.1101/2020.11.25.397992

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (2533)
  • Biochemistry (4976)
  • Bioengineering (3486)
  • Bioinformatics (15230)
  • Biophysics (6909)
  • Cancer Biology (5395)
  • Cell Biology (7751)
  • Clinical Trials (138)
  • Developmental Biology (4539)
  • Ecology (7158)
  • Epidemiology (2059)
  • Evolutionary Biology (10234)
  • Genetics (7517)
  • Genomics (9791)
  • Immunology (4860)
  • Microbiology (13233)
  • Molecular Biology (5143)
  • Neuroscience (29465)
  • Paleontology (203)
  • Pathology (838)
  • Pharmacology and Toxicology (1465)
  • Physiology (2142)
  • Plant Biology (4756)
  • Scientific Communication and Education (1013)
  • Synthetic Biology (1338)
  • Systems Biology (4014)
  • Zoology (768)