Coordinate Dependence of Variability Analysis
Identifieur interne : 003838 ( Main/Merge ); précédent : 003837; suivant : 003839Coordinate Dependence of Variability Analysis
Auteurs : Dagmar Sternad [États-Unis] ; Se-Woong Park [États-Unis] ; Hermann Müller [Allemagne] ; Neville Hogan [États-Unis]Source :
- PLoS Computational Biology [ 1553-734X ] ; 2010.
Abstract
Analysis of motor performance variability in tasks with redundancy affords insight about synergies underlying central nervous system (CNS) control. Preferential distribution of variability in ways that minimally affect task performance suggests sophisticated neural control. Unfortunately, in the analysis of variability the choice of coordinates used to represent multi-dimensional data may profoundly affect analysis, introducing an arbitrariness which compromises its conclusions. This paper assesses the influence of coordinates. Methods based on analyzing a covariance matrix are fundamentally dependent on an investigator's choices. Two reasons are identified: using
Url:
DOI: 10.1371/journal.pcbi.1000751
PubMed: 20421930
PubMed Central: 2858681
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PMC:2858681Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>Analysis of motor performance variability in tasks with redundancy affords insight about synergies underlying central nervous system (CNS) control. Preferential distribution of variability in ways that minimally affect task performance suggests sophisticated neural control. Unfortunately, in the analysis of variability the choice of coordinates used to represent multi-dimensional data may profoundly affect analysis, introducing an arbitrariness which compromises its conclusions. This paper assesses the influence of coordinates. Methods based on analyzing a covariance matrix are fundamentally dependent on an investigator's choices. Two reasons are identified: using <italic>anisotropy</italic>
of a covariance matrix as evidence of preferential distribution of variability; and using <italic>orthogonality</italic>
to quantify relevance of variability to task performance. Both are exquisitely sensitive to coordinates. Unless coordinates are known <italic>a priori</italic>
, these methods do not support unambiguous inferences about CNS control. An alternative method uses a two-level approach where variability in task execution (expressed in one coordinate frame) is mapped by a function to its result (expressed in another coordinate frame). An analysis of variability in execution using this function to quantify performance at the level of results offers substantially less sensitivity to coordinates than analysis of a covariance matrix of execution variables. This is an initial step towards developing coordinate-invariant analysis methods for movement neuroscience.</p>
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<author><name sortKey="Li, W" uniqKey="Li W">W Li</name>
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