An analytical method reduces noise bias in motor adaptation analysis

Abstract When a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction, quantified as a trial-by-trial adaptation rate, provides insight into how the nervous system is operating, particularly regarding h...

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Autores principales:	Daniel H. Blustein, Ahmed W. Shehata, Erin S. Kuylenstierna, Kevin B. Englehart, Jonathon W. Sensinger
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b7caee3e1b9248a99245db0864fe6e0d
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spelling	oai:doaj.org-article:b7caee3e1b9248a99245db0864fe6e0d2021-12-02T17:39:20ZAn analytical method reduces noise bias in motor adaptation analysis10.1038/s41598-021-88688-52045-2322https://doaj.org/article/b7caee3e1b9248a99245db0864fe6e0d2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88688-5https://doaj.org/toc/2045-2322Abstract When a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction, quantified as a trial-by-trial adaptation rate, provides insight into how the nervous system is operating, particularly regarding how much confidence a person places in different sources of information such as sensory feedback or motor command reproducibility. Traditional analysis has required carefully controlled laboratory conditions such as the application of perturbations or error clamping, limiting the usefulness of motor analysis in clinical and everyday environments. Here we focus on error adaptation during unperturbed and naturalistic movements. With increasing motor noise, we show that 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.Daniel H. BlusteinAhmed W. ShehataErin S. KuylenstiernaKevin B. EnglehartJonathon W. SensingerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daniel H. Blustein Ahmed W. Shehata Erin S. Kuylenstierna Kevin B. Englehart Jonathon W. Sensinger An analytical method reduces noise bias in motor adaptation analysis
description	Abstract When a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction, quantified as a trial-by-trial adaptation rate, provides insight into how the nervous system is operating, particularly regarding how much confidence a person places in different sources of information such as sensory feedback or motor command reproducibility. Traditional analysis has required carefully controlled laboratory conditions such as the application of perturbations or error clamping, limiting the usefulness of motor analysis in clinical and everyday environments. Here we focus on error adaptation during unperturbed and naturalistic movements. With increasing motor noise, we show that 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.
format	article
author	Daniel H. Blustein Ahmed W. Shehata Erin S. Kuylenstierna Kevin B. Englehart Jonathon W. Sensinger
author_facet	Daniel H. Blustein Ahmed W. Shehata Erin S. Kuylenstierna Kevin B. Englehart Jonathon W. Sensinger
author_sort	Daniel H. Blustein
title	An analytical method reduces noise bias in motor adaptation analysis
title_short	An analytical method reduces noise bias in motor adaptation analysis
title_full	An analytical method reduces noise bias in motor adaptation analysis
title_fullStr	An analytical method reduces noise bias in motor adaptation analysis
title_full_unstemmed	An analytical method reduces noise bias in motor adaptation analysis
title_sort	analytical method reduces noise bias in motor adaptation analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b7caee3e1b9248a99245db0864fe6e0d
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An analytical method reduces noise bias in motor adaptation analysis

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