Learning a locomotor task: with or without errors?
Identifieur interne : 000732 ( PubMed/Corpus ); précédent : 000731; suivant : 000733Learning a locomotor task: with or without errors?
Auteurs : Laura Marchal-Crespo ; Jasmin Schneider ; Lukas Jaeger ; Robert RienerSource :
- Journal of neuroengineering and rehabilitation [ 1743-0003 ] ; 2014.
English descriptors
- KwdEn :
- MESH :
- methods : Robotics.
- physiology : Brain, Learning, Motor Activity, Muscle, Skeletal.
- Algorithms, Electromyography, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult.
Abstract
Robotic haptic guidance is the most commonly used robotic training strategy to reduce performance errors while training. However, research on motor learning has emphasized that errors are a fundamental neural signal that drive motor adaptation. Thus, researchers have proposed robotic therapy algorithms that amplify movement errors rather than decrease them. However, to date, no study has analyzed with precision which training strategy is the most appropriate to learn an especially simple task.
DOI: 10.1186/1743-0003-11-25
PubMed: 24594267
Links to Exploration step
pubmed:24594267Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Learning a locomotor task: with or without errors?</title><author><name sortKey="Marchal Crespo, Laura" sort="Marchal Crespo, Laura" uniqKey="Marchal Crespo L" first="Laura" last="Marchal-Crespo">Laura Marchal-Crespo</name><affiliation><nlm:affiliation>Sensory-Motor Systems Lab, Institute of Robotics and Intelligent Systems IRIS, ETH Zurich, Zurich, Switzerland. laura.marchal@hest.ethz.ch.</nlm:affiliation></affiliation></author><author><name sortKey="Schneider, Jasmin" sort="Schneider, Jasmin" uniqKey="Schneider J" first="Jasmin" last="Schneider">Jasmin Schneider</name></author><author><name sortKey="Jaeger, Lukas" sort="Jaeger, Lukas" uniqKey="Jaeger L" first="Lukas" last="Jaeger">Lukas Jaeger</name></author><author><name sortKey="Riener, Robert" sort="Riener, Robert" uniqKey="Riener R" first="Robert" last="Riener">Robert Riener</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2014">2014</date><idno type="doi">10.1186/1743-0003-11-25</idno><idno type="RBID">pubmed:24594267</idno><idno type="pmid">24594267</idno><idno type="wicri:Area/PubMed/Corpus">000732</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Learning a locomotor task: with or without errors?</title><author><name sortKey="Marchal Crespo, Laura" sort="Marchal Crespo, Laura" uniqKey="Marchal Crespo L" first="Laura" last="Marchal-Crespo">Laura Marchal-Crespo</name><affiliation><nlm:affiliation>Sensory-Motor Systems Lab, Institute of Robotics and Intelligent Systems IRIS, ETH Zurich, Zurich, Switzerland. laura.marchal@hest.ethz.ch.</nlm:affiliation></affiliation></author><author><name sortKey="Schneider, Jasmin" sort="Schneider, Jasmin" uniqKey="Schneider J" first="Jasmin" last="Schneider">Jasmin Schneider</name></author><author><name sortKey="Jaeger, Lukas" sort="Jaeger, Lukas" uniqKey="Jaeger L" first="Lukas" last="Jaeger">Lukas Jaeger</name></author><author><name sortKey="Riener, Robert" sort="Riener, Robert" uniqKey="Riener R" first="Robert" last="Riener">Robert Riener</name></author></analytic><series><title level="j">Journal of neuroengineering and rehabilitation</title><idno type="eISSN">1743-0003</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Brain (physiology)</term><term>Electromyography</term><term>Female</term><term>Humans</term><term>Learning (physiology)</term><term>Magnetic Resonance Imaging</term><term>Male</term><term>Motor Activity (physiology)</term><term>Muscle, Skeletal (physiology)</term><term>Robotics (methods)</term><term>Young Adult</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Robotics</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Brain</term><term>Learning</term><term>Motor Activity</term><term>Muscle, Skeletal</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Electromyography</term><term>Female</term><term>Humans</term><term>Magnetic Resonance Imaging</term><term>Male</term><term>Young Adult</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Robotic haptic guidance is the most commonly used robotic training strategy to reduce performance errors while training. However, research on motor learning has emphasized that errors are a fundamental neural signal that drive motor adaptation. Thus, researchers have proposed robotic therapy algorithms that amplify movement errors rather than decrease them. However, to date, no study has analyzed with precision which training strategy is the most appropriate to learn an especially simple task.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">24594267</PMID><DateCreated><Year>2014</Year><Month>03</Month><Day>13</Day></DateCreated><DateCompleted><Year>2014</Year><Month>08</Month><Day>22</Day></DateCompleted><DateRevised><Year>2015</Year><Month>05</Month><Day>15</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1743-0003</ISSN><JournalIssue CitedMedium="Internet"><Volume>11</Volume><PubDate><Year>2014</Year></PubDate></JournalIssue><Title>Journal of neuroengineering and rehabilitation</Title><ISOAbbreviation>J Neuroeng Rehabil</ISOAbbreviation></Journal><ArticleTitle>Learning a locomotor task: with or without errors?</ArticleTitle><Pagination><MedlinePgn>25</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1743-0003-11-25</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Robotic haptic guidance is the most commonly used robotic training strategy to reduce performance errors while training. However, research on motor learning has emphasized that errors are a fundamental neural signal that drive motor adaptation. Thus, researchers have proposed robotic therapy algorithms that amplify movement errors rather than decrease them. However, to date, no study has analyzed with precision which training strategy is the most appropriate to learn an especially simple task.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">In this study, the impact of robotic training strategies that amplify or reduce errors on muscle activation and motor learning of a simple locomotor task was investigated in twenty two healthy subjects. The experiment was conducted with the MAgnetic Resonance COmpatible Stepper (MARCOS) a special robotic device developed for investigations in the MR scanner. The robot moved the dominant leg passively and the subject was requested to actively synchronize the non-dominant leg to achieve an alternating stepping-like movement. Learning with four different training strategies that reduce or amplify errors was evaluated: (i) Haptic guidance: errors were eliminated by passively moving the limbs, (ii) No guidance: no robot disturbances were presented, (iii) Error amplification: existing errors were amplified with repulsive forces, (iv) Noise disturbance: errors were evoked intentionally with a randomly-varying force disturbance on top of the no guidance strategy. Additionally, the activation of four lower limb muscles was measured by the means of surface electromyography (EMG).</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Strategies that reduce or do not amplify errors limit muscle activation during training and result in poor learning gains. Adding random disturbing forces during training seems to increase attention, and therefore improve motor learning. Error amplification seems to be the most suitable strategy for initially less skilled subjects, perhaps because subjects could better detect their errors and correct them.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Error strategies have a great potential to evoke higher muscle activation and provoke better motor learning of simple tasks. Neuroimaging evaluation of brain regions involved in learning can provide valuable information on observed behavioral outcomes related to learning processes. The impacts of these strategies on neurological patients need further investigations.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Marchal-Crespo</LastName><ForeName>Laura</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Sensory-Motor Systems Lab, Institute of Robotics and Intelligent Systems IRIS, ETH Zurich, Zurich, Switzerland. laura.marchal@hest.ethz.ch.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Schneider</LastName><ForeName>Jasmin</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Jaeger</LastName><ForeName>Lukas</ForeName><Initials>L</Initials></Author><Author ValidYN="Y"><LastName>Riener</LastName><ForeName>Robert</ForeName><Initials>R</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>03</Month><Day>04</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J Neuroeng Rehabil</MedlineTA><NlmUniqueID>101232233</NlmUniqueID><ISSNLinking>1743-0003</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Neural Syst Rehabil Eng. 2007 Sep;15(3):379-86</RefSource><PMID Version="1">17894270</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Brain. 2007 Jul;130(Pt 7):1861-72</RefSource><PMID Version="1">17405765</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Biomed Eng. 2008 Jan;55(1):322-34</RefSource><PMID Version="1">18232376</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Neuroeng Rehabil. 2009;6:5</RefSource><PMID 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