An analytical method reduces noise bias in motor adaptation analysis
| dc.contributor.author | Blustein, Daniel H. | |
| dc.contributor.author | Shehata, Ahmed W. | |
| dc.contributor.author | Kuylenstierna, Erin S. | |
| dc.contributor.author | Englehart, Kevin B. | |
| dc.contributor.author | Sensinger, Jonathon W. | |
| dc.date.accessioned | 2023-06-19T16:56:21Z | |
| dc.date.available | 2023-06-19T16:56:21Z | |
| dc.date.issued | 2021 | |
| dc.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. | |
| dc.description.copyright | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2021 | |
| dc.identifier.doi | 10.1038/s41598-021-88688-5 | |
| dc.identifier.issn | 2045-2322 | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/37229 | |
| dc.language.iso | en | |
| dc.publisher | Nature Research | |
| dc.relation.hasversion | https://doi.org/10.1038/s41598-021-88688-5 | |
| dc.relation.ispartof | Scientific Reports | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Electrical and Computer Engineering | |
| dc.title | An analytical method reduces noise bias in motor adaptation analysis | |
| dc.type | journal-article | |
| oaire.citation.issue | 1 | |
| oaire.citation.title | Scientific Reports | |
| oaire.citation.volume | 11 | |
| oaire.license.condition | http://creativecommons.org/licenses/by/4.0/ | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |