A formal and data-based comparison of measures of motor-equivalent covariation

Different analysis methods have been developed for assessing motor-equivalent organization of movement variability. In the uncontrolled manifold (UCM) method, the structure of variability is analyzed by comparing goal-equivalent and non-goal-equivalent variability components at the level of elemental variables (e.g., joint angles). In contrast, in the covariation by randomization (CR) approach, motor-equivalent organization is assessed by comparing variability at the task level between empirical and decorrelated surrogate data. UCM effects can be due to both covariation among elemental variables and selective channeling of variability to elemental variables with low task sensitivity ("individual variation"), suggesting a link between the UCM and CR method. However, the precise relationship between the notion of covariation in the two approaches has not been analyzed in detail yet. Analysis of empirical and simulated data from a study on manual pointing shows that in general the two approaches are not equivalent, but the respective covariation measures are highly correlated (ρ > 0.7) for two proposed definitions of covariation in the UCM context. For one-dimensional task spaces, a formal comparison is possible and in fact the two notions of covariation are equivalent. In situations in which individual variation does not contribute to UCM effects, for which necessary and sufficient conditions are derived, this entails the equivalence of the UCM and CR analysis. Implications for the interpretation of UCM effects are discussed.

[1]  G. Schöner,et al.  Analyzing variance in multi-degree-of-freedom movements: uncovering structure versus extracting correlations. , 2007, Motor control.

[2]  Gregor Schöner,et al.  Toward a new theory of motor synergies. , 2007, Motor control.

[3]  Tim Kiemel,et al.  Control and estimation of posture during quiet stance depends on multijoint coordination. , 2007, Journal of neurophysiology.

[4]  Paola Cesari,et al.  Body-goal Variability Mapping in an Aiming Task , 2006, Biological Cybernetics.

[5]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[6]  M. Latash,et al.  Age-related changes in finger coordination in static prehension tasks. , 2004, Journal of applied physiology.

[7]  G. Schöner Recent Developments and Problems in Human Movement Science and Their Conceptual Implications , 1995 .

[8]  Vladimir M. Zatsiorsky,et al.  Finger interaction during accurate multi-finger force production tasks in young and elderly persons , 2004, Experimental Brain Research.

[9]  Ulman Lindenberger,et al.  Normal aging reduces motor synergies in manual pointing , 2012, Neurobiology of Aging.

[10]  Young-Hui Chang,et al.  Rate-dependent control strategies stabilize limb forces during human locomotion , 2010, Journal of The Royal Society Interface.

[11]  M. Latash,et al.  Finger coordination in persons with Down syndrome: atypical patterns of coordination and the effects of practice , 2002, Experimental Brain Research.

[12]  Mark L Latash,et al.  Anticipatory synergy adjustments in preparation to self-triggered perturbations in elderly individuals. , 2008, Journal of applied biomechanics.

[13]  Gregor Schöner,et al.  The uncontrolled manifold concept: identifying control variables for a functional task , 1999, Experimental Brain Research.

[14]  M. Latash,et al.  Time evolution of the organization of multi-muscle postural responses to sudden changes in the external force applied at the trunk level , 2008, Neuroscience Letters.

[15]  M. Latash,et al.  Structure of motor variability in marginally redundant multifinger force production tasks , 2001, Experimental Brain Research.

[16]  Dagmar Sternad,et al.  A randomization method for the calculation of covariation in multiple nonlinear relations: illustrated with the example of goal-directed movements , 2003, Biological Cybernetics.

[17]  U. Lindenberger,et al.  Motor-equivalent covariation stabilizes step parameters and center of mass position during treadmill walking , 2010, Experimental Brain Research.

[18]  M. Latash,et al.  Uncontrolled manifold analysis of single trials during multi-finger force production by persons with and without Down syndrome , 2003, Experimental Brain Research.

[19]  Julius Verrel Distributional properties and variance-stabilizing transformations for measures of uncontrolled manifold effects , 2010, Journal of Neuroscience Methods.

[20]  Christopher A. Zirker,et al.  Angular momentum synergies during walking , 2009, Experimental Brain Research.

[21]  Mark L. Latash,et al.  Analyses of joint variance related to voluntary whole-body movements performed in standing , 2010, Journal of Neuroscience Methods.