HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) All Versions of this Article: 99/6/3027    most recent 01367.2007v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Wei, K. Articles by Sternad, D. PubMed PubMed Citation Articles by Wei, K. Articles by Sternad, D.

Stability and Variability: Indicators for Passive Stability and Active Control in a Rhythmic Task

¹Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois; ²Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands; and ³Departments of Kinesiology and Integrative Biosciences, The Pennsylvania State University, University Park, Pennsylvania

Submitted 18 December 2007; accepted in final form 12 March 2008

Using a rhythmic task where human subjects bounced a ball with a handheld racket, fine-grained analyses of stability and variability extricated contributions from open-loop control, noise strength, and active error compensation. Based on stability analyses of a stochastic-deterministic model of the task—a surface contacting the ball by periodic movements—open-loop or dynamic stability was assessed by the acceleration of the racket at contact. Autocovariance analyses of model and data were further used to gauge the contributions of open-loop stability and noise strength. Variability and regression analyses estimated active error compensation. Empirical results demonstrated that experienced actors exploited open-loop stability more than novices, had lower noise strength, and applied more active error compensations. By manipulating the model parameter coefficient of restitution, task stability was varied and showed that actors graded these three components as a function of task stability. It is concluded that actors tune into task stability when stability is high but use more active compensation when stability is reduced. Implications for the neural underpinnings for passive stability and active control are discussed. Further, results showed that stability and variability are not simply the inverse of each other but contain more quantitative information when combined with model analyses.