title: Coordinate dependence of variability analysis.
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 anisotropy of a covariance matrix as evidence of preferential distribution of variability; and using orthogonality to quantify relevance of variability to task performance. Both are exquisitely sensitive to coordinates. Unless coordinates are known a priori, 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.
authors.affiliation: Department of Biology, Northeastern University, Boston, Massachusetts, United States of America. dagmar@neu.edu
authors.forename: Dagmar
authors.initials: D
authors.lastname: Sternad
authors.forename: Se-Woong
authors.initials: SW
authors.lastname: Park
authors.forename: Hermann
authors.initials: H
authors.lastname: Müller
authors.forename: Neville
authors.initials: N
authors.lastname: Hogan
mesh_terms: D000704:Analysis of Variance; D016880:Anisotropy; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D009488:Neurosciences / Q000379:methods; D025341:Principal Component Analysis; D011597:Psychomotor Performance / Q000502:physiology