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) 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 ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (26) Citing Articles via Google Scholar Google Scholar Articles by Osu, R. Articles by Kawato, M. Search for Related Content PubMed PubMed Citation Articles by Osu, R. Articles by Kawato, M.

Different Mechanisms Involved in Adaptation to Stable and Unstable Dynamics

¹ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan; ²Department of Mechanical Engineering and Division of Bioengineering, National University of Singapore 119260, Singapore; and ³School of Kinesiology, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada

Submitted 27 January 2003; accepted in final form 31 July 2003

Recently, we demonstrated that humans can learn to make accurate movements in an unstable environment by controlling magnitude, shape, and orientation of the endpoint impedance. Although previous studies of human motor learning suggest that the brain acquires an inverse dynamics model of the novel environment, it is not known whether this control mechanism is operative in unstable environments. We compared learning of multijoint arm movements in a "velocity-dependent force field" (VF), which interacted with the arm in a stable manner, and learning in a "divergent force field" (DF), where the interaction was unstable. The characteristics of error evolution were markedly different in the 2 fields. The direction of trajectory error in the DF alternated to the left and right during the early stage of learning; that is, signed error was inconsistent from movement to movement and could not have guided learning of an inverse dynamics model. This contrasted sharply with trajectory error in the VF, which was initially biased and decayed in a manner that was consistent with rapid feedback error learning. EMG recorded before and after learning in the DF and VF are also consistent with different learning and control mechanisms for adapting to stable and unstable dynamics, that is, inverse dynamics model formation and impedance control. We also investigated adaptation to a rotated DF to examine the interplay between inverse dynamics model formation and impedance control. Our results suggest that an inverse dynamics model can function in parallel with an impedance controller to compensate for consistent perturbing force in unstable environments.

This article has been cited by other articles:

				A. G. Richardson, G. Lassi-Tucci, C. Padoa-Schioppa, and E. Bizzi Neuronal Activity in the Cingulate Motor Areas During Adaptation to a New Dynamic Environment J Neurophysiol, March 1, 2008; 99(3): 1253 - 1266. [Abstract] [Full Text] [PDF]

				D. R. Lametti, G. Houle, and D. J. Ostry Control of Movement Variability and the Regulation of Limb Impedance J Neurophysiol, December 1, 2007; 98(6): 3516 - 3524. [Abstract] [Full Text] [PDF]

				M. R. Hinder and T. E. Milner Rapid Adaptation to Scaled Changes of the Mechanical Environment J Neurophysiol, November 1, 2007; 98(5): 3072 - 3080. [Abstract] [Full Text] [PDF]

				D. W. Franklin, G. Liaw, T. E. Milner, R. Osu, E. Burdet, and M. Kawato Endpoint Stiffness of the Arm Is Directionally Tuned to Instability in the Environment J. Neurosci., July 18, 2007; 27(29): 7705 - 7716. [Abstract] [Full Text] [PDF]

				E. Guigon, P. Baraduc, and M. Desmurget Computational Motor Control: Redundancy and Invariance J Neurophysiol, January 1, 2007; 97(1): 331 - 347. [Abstract] [Full Text] [PDF]

				T. E. Milner and M. R. Hinder Position Information But Not Force Information Is Used in Adapting to Changes in Environmental Dynamics J Neurophysiol, August 1, 2006; 96(2): 526 - 534. [Abstract] [Full Text] [PDF]

				T. E. Milner and D. W. Franklin Impedance control and internal model use during the initial stage of adaptation to novel dynamics in humans J. Physiol., September 1, 2005; 567(2): 651 - 664. [Abstract] [Full Text] [PDF]

				A. G. Richardson, J.-J. E. Slotine, E. Bizzi, and M. C. Tresch Intrinsic Musculoskeletal Properties Stabilize Wiping Movements in the Spinalized Frog J. Neurosci., March 23, 2005; 25(12): 3181 - 3191. [Abstract] [Full Text] [PDF]

				D. W. Franklin, U. So, M. Kawato, and T. E. Milner Impedance Control Balances Stability With Metabolically Costly Muscle Activation J Neurophysiol, November 1, 2004; 92(5): 3097 - 3105. [Abstract] [Full Text] [PDF]

				R. Osu, N. Kamimura, H. Iwasaki, E. Nakano, C. M. Harris, Y. Wada, and M. Kawato Optimal Impedance Control for Task Achievement in the Presence of Signal-Dependent Noise J Neurophysiol, August 1, 2004; 92(2): 1199 - 1215. [Abstract] [Full Text] [PDF]