Strategy switching in the stabilization of unstable dynamics.
Identifieur interne : 000625 ( PubMed/Corpus ); précédent : 000624; suivant : 000626Strategy switching in the stabilization of unstable dynamics.
Auteurs : Jacopo Zenzeri ; Dalia De Santis ; Pietro MorassoSource :
- PloS one [ 1932-6203 ] ; 2014.
English descriptors
- KwdEn :
- MESH :
- physiology : Psychomotor Performance.
- Humans, Learning, Models, Neurological, User-Computer Interface.
Abstract
In order to understand mechanisms of strategy switching in the stabilization of unstable dynamics, this work investigates how human subjects learn to become skilled users of an underactuated bimanual tool in an unstable environment. The tool, which consists of a mass and two hand-held non-linear springs, is affected by a saddle-like force-field. The non-linearity of the springs allows the users to determine size and orientation of the tool stiffness ellipse, by using different patterns of bimanual coordination: minimal stiffness occurs when the two spring terminals are aligned and stiffness size grows by stretching them apart. Tool parameters were set such that minimal stiffness is insufficient to provide stable equilibrium whereas asymptotic stability can be achieved with sufficient stretching, although at the expense of greater effort. As a consequence, tool users have two possible strategies for stabilizing the mass in different regions of the workspace: 1) high stiffness feedforward strategy, aiming at asymptotic stability and 2) low stiffness positional feedback strategy aiming at bounded stability. The tool was simulated by a bimanual haptic robot with direct torque control of the motors. In a previous study we analyzed the behavior of naïve users and we found that they spontaneously clustered into two groups of approximately equal size. In this study we trained subjects to become expert users of both strategies in a discrete reaching task. Then we tested generalization capabilities and mechanism of strategy-switching by means of stabilization tasks which consist of tracking moving targets in the workspace. The uniqueness of the experimental setup is that it addresses the general problem of strategy-switching in an unstable environment, suggesting that complex behaviors cannot be explained in terms of a global optimization criterion but rather require the ability to switch between different sub-optimal mechanisms.
DOI: 10.1371/journal.pone.0099087
PubMed: 24921254
Links to Exploration step
pubmed:24921254Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Strategy switching in the stabilization of unstable dynamics.</title><author><name sortKey="Zenzeri, Jacopo" sort="Zenzeri, Jacopo" uniqKey="Zenzeri J" first="Jacopo" last="Zenzeri">Jacopo Zenzeri</name><affiliation><nlm:affiliation>Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia, Genoa, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="De Santis, Dalia" sort="De Santis, Dalia" uniqKey="De Santis D" first="Dalia" last="De Santis">Dalia De Santis</name><affiliation><nlm:affiliation>Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia, Genoa, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Morasso, Pietro" sort="Morasso, Pietro" uniqKey="Morasso P" first="Pietro" last="Morasso">Pietro Morasso</name><affiliation><nlm:affiliation>Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia, Genoa, Italy.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2014">2014</date><idno type="doi">10.1371/journal.pone.0099087</idno><idno type="RBID">pubmed:24921254</idno><idno type="pmid">24921254</idno><idno type="wicri:Area/PubMed/Corpus">000625</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Strategy switching in the stabilization of unstable dynamics.</title><author><name sortKey="Zenzeri, Jacopo" sort="Zenzeri, Jacopo" uniqKey="Zenzeri J" first="Jacopo" last="Zenzeri">Jacopo Zenzeri</name><affiliation><nlm:affiliation>Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia, Genoa, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="De Santis, Dalia" sort="De Santis, Dalia" uniqKey="De Santis D" first="Dalia" last="De Santis">Dalia De Santis</name><affiliation><nlm:affiliation>Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia, Genoa, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Morasso, Pietro" sort="Morasso, Pietro" uniqKey="Morasso P" first="Pietro" last="Morasso">Pietro Morasso</name><affiliation><nlm:affiliation>Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia, Genoa, Italy.</nlm:affiliation></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Humans</term><term>Learning</term><term>Models, Neurological</term><term>Psychomotor Performance (physiology)</term><term>User-Computer Interface</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Psychomotor Performance</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Learning</term><term>Models, Neurological</term><term>User-Computer Interface</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In order to understand mechanisms of strategy switching in the stabilization of unstable dynamics, this work investigates how human subjects learn to become skilled users of an underactuated bimanual tool in an unstable environment. The tool, which consists of a mass and two hand-held non-linear springs, is affected by a saddle-like force-field. The non-linearity of the springs allows the users to determine size and orientation of the tool stiffness ellipse, by using different patterns of bimanual coordination: minimal stiffness occurs when the two spring terminals are aligned and stiffness size grows by stretching them apart. Tool parameters were set such that minimal stiffness is insufficient to provide stable equilibrium whereas asymptotic stability can be achieved with sufficient stretching, although at the expense of greater effort. As a consequence, tool users have two possible strategies for stabilizing the mass in different regions of the workspace: 1) high stiffness feedforward strategy, aiming at asymptotic stability and 2) low stiffness positional feedback strategy aiming at bounded stability. The tool was simulated by a bimanual haptic robot with direct torque control of the motors. In a previous study we analyzed the behavior of naïve users and we found that they spontaneously clustered into two groups of approximately equal size. In this study we trained subjects to become expert users of both strategies in a discrete reaching task. Then we tested generalization capabilities and mechanism of strategy-switching by means of stabilization tasks which consist of tracking moving targets in the workspace. The uniqueness of the experimental setup is that it addresses the general problem of strategy-switching in an unstable environment, suggesting that complex behaviors cannot be explained in terms of a global optimization criterion but rather require the ability to switch between different sub-optimal mechanisms.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">24921254</PMID><DateCreated><Year>2014</Year><Month>06</Month><Day>13</Day></DateCreated><DateCompleted><Year>2015</Year><Month>10</Month><Day>07</Day></DateCompleted><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>9</Volume><Issue>6</Issue><PubDate><Year>2014</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Strategy switching in the stabilization of unstable dynamics.</ArticleTitle><Pagination><MedlinePgn>e99087</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0099087</ELocationID><Abstract><AbstractText>In order to understand mechanisms of strategy switching in the stabilization of unstable dynamics, this work investigates how human subjects learn to become skilled users of an underactuated bimanual tool in an unstable environment. The tool, which consists of a mass and two hand-held non-linear springs, is affected by a saddle-like force-field. The non-linearity of the springs allows the users to determine size and orientation of the tool stiffness ellipse, by using different patterns of bimanual coordination: minimal stiffness occurs when the two spring terminals are aligned and stiffness size grows by stretching them apart. Tool parameters were set such that minimal stiffness is insufficient to provide stable equilibrium whereas asymptotic stability can be achieved with sufficient stretching, although at the expense of greater effort. As a consequence, tool users have two possible strategies for stabilizing the mass in different regions of the workspace: 1) high stiffness feedforward strategy, aiming at asymptotic stability and 2) low stiffness positional feedback strategy aiming at bounded stability. The tool was simulated by a bimanual haptic robot with direct torque control of the motors. In a previous study we analyzed the behavior of naïve users and we found that they spontaneously clustered into two groups of approximately equal size. In this study we trained subjects to become expert users of both strategies in a discrete reaching task. Then we tested generalization capabilities and mechanism of strategy-switching by means of stabilization tasks which consist of tracking moving targets in the workspace. The uniqueness of the experimental setup is that it addresses the general problem of strategy-switching in an unstable environment, suggesting that complex behaviors cannot be explained in terms of a global optimization criterion but rather require the ability to switch between different sub-optimal mechanisms.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zenzeri</LastName><ForeName>Jacopo</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia, Genoa, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>De Santis</LastName><ForeName>Dalia</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia, Genoa, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Morasso</LastName><ForeName>Pietro</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia, Genoa, Italy.</Affiliation></AffiliationInfo></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>06</Month><Day>12</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Hum Mov Sci. 2008 Jun;27(3):473-95</RefSource><PMID Version="1">18342382</PMID></CommentsCorrections><CommentsCorrections 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