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Movement Coordination

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Encyclopedia of Complexity and Systems Science

Definition of the Subject

Movement Coordination is present all the time in daily life but tends to be taken for granted when it works. One might say it is quite an arcanesubject also for science. This changes drastically when some pieces of the locomotor system are not functioning properly because of injury, disease orage. In most cases it is only then that people become aware of the complex mechanisms that must be in place to control and coordinate the hundreds ofmuscles and joints in the body of humans or animals to allow for maintaining balance while maneuvering through rough terrains, for example. No robotperformance comes even close in such a task.

Although these issues have been around for a long time it was only during the last quarter century that scientists developed quantitative models formovement coordination based on the theory of nonlinear dynamical systems . Coordination dynamics , as the field is now called, has become arguably...

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Abbreviations

Control parameter :

A parameter of internal or external origin that when manipulated controls the system in a nonspecific fashion and is capable of inducing changes in the system’s behavior. These changes may be a smooth function of the control parameter, or abrupt at certain critical values. The latter, also referred to as phase transitions, are of main interest here as they only occur in nonlinear systems and are accompanied by phenomena like critical slowing down and fluctuation enhancement that can be probed for experimentally.

Haken–Kelso–Bunz (HKB) model :

First published in 1985, the HKB model is the best known and probably most extensively tested quantitative model in human movement behavior. In its original form it describes the dynamics of the relative phase between two oscillating fingers or limbs under frequency scaling. The HKB model can be derived from coupled nonlinear oscillators and has been successfully extended in various ways, for instance, to situations where different limbs like an arm and a leg, a single limb and a metronome, or even two different people are involved.

Order parameter :

Order parameters are quantities that allow for a usually low-dimensional description of the dynamical behavior of a high-dimensional system on a macroscopic level. These quantities change their values abruptly when a system undergoes a phase transition. For example, density is an order parameter in the ice to water, or water to vapor transitions. In movement coordination the most-studied order parameter is relative phase, i.?e. the difference in the phases between two or more oscillating entities.

Phase transition :

The best-known phase transitions are the changes from a solid to a fluid phase like ice to water, or from fluid to gas like water to vapor. These transitions are called first-order phase transitions as they involve latent heat, which means that a certain amount of energy has to be put into the system at the transition point that does not cause an increase in temperature. For the second-order phase transitions there is no latent heat involved. An example from physics is heating a magnet above its Curie temperature at which point it switches from a magnetic to a nonmagnetic state. The qualitative changes that are observed in many nonlinear dynamical systems when a parameter exceeds a certain threshold are also such second-order phase transitions.

Bibliography

Primary Literature

  1. Assisi CG, Jirsa VK, Kelso JAS (2005) Dynamics of multifrequency coordinationusing parametric driving: Theory and Experiment. Biol Cybern 93:6–21

    MathSciNet  MATH  Google Scholar 

  2. Buchanan JJ, Kelso JAS (1993) Posturally induced transitions in rhythmicmultijoint limb movements. Exp Brain Res 94:131–142

    Google Scholar 

  3. Buchanan JJ, Kelso JAS, Fuchs A (1996) Coordination dynamics of trajectoryformation. Biol Cybern 74:41–54

    MATH  Google Scholar 

  4. Buchanan JJ, Kelso JAS, DeGuzman GC (1997) The self-organization oftrajectory formation: I. Experimental evidence. Biol Cybern 76:257–273

    MATH  Google Scholar 

  5. Carson RG, Goodman D, Kelso JAS, Elliott D (1995) Phase transitions and criticalfluctuations in rhythmic coordination of ipsilateral hand and foot. J Mot Behav 27:211–224

    Google Scholar 

  6. Carson RG, Rick S, Smethrust CJ, Lison JF, Biblow WD (2000)Neuromuscular-skeletal constraints upon the dynamics of unimanual and bimanual coordination. Exp Brain Res131:196–214

    Google Scholar 

  7. Carver FW, Fuchs A, Jantzen KJ, Kelso JAS (2002) Spatiotemporal analysis ofneuromagnetic activity associated with rhythmic auditory stimulation. Clin Neurophysiol 113:1909–1920

    Google Scholar 

  8. Collins DR, Sternad D, Turvey MT (1996) An experimental note on definingfrequency competition in intersegmental coordination dynamics. J Mot Behav 28:299–303

    Google Scholar 

  9. DeGuzman GC, Kelso JAS, Buchanan JJ (1997) The self-organization oftrajectory formation: II. Theoretical model. Biol Cybern 76:275–284

    Google Scholar 

  10. Fink P, Kelso JAS, Jirsa VK, Foo P (2000) Local and global stabilization ofcoordination by sensory information. Exp Brain Res 134:9–20

    Google Scholar 

  11. Fuchs A, Jirsa VK (2000) The HKB model revisited: How varying the degree ofsymmetry controls dynamics. Hum Mov Sci 19:425–449

    Google Scholar 

  12. Fuchs A, Kelso JAS (1994) A theoretical note on models of Interlimbcoordination. J Exp Spychol Hum Percept Perform 20:1088–1097

    Google Scholar 

  13. Fuchs A, Kelso JAS, Haken H (1992) Phase transitions in the human brain:Spatial mode dynamics. Int J Bifurc Chaos 2:917–939

    MATH  Google Scholar 

  14. Fuchs A, Jirsa VK, Haken H, Kelso JAS (1996) Extending the HKB-Model ofcoordinated movement to oscillators with different eigenfrequencies. Biol Cybern 74:21–30

    MATH  Google Scholar 

  15. Fuchs A, Mayville JM, Cheyne D, Weinberg H, Deecke L, Kelso JAS (2000)Spatiotemporal Analysis of Neuromagnetic Events Underlying the Emergence of Coordinative Instabilities. NeuroImage12:71–84

    Google Scholar 

  16. Gardiner CW (1985) Handbook of stochastic Systems. Springer,Heidelberg

    Google Scholar 

  17. Haken H (1977) Synergetics, an introduction. Springer,Heidelberg

    MATH  Google Scholar 

  18. Haken H (1983) Advanced Synergetics. Springer,Heidelberg

    MATH  Google Scholar 

  19. Haken H, Kelso JAS, Bunz H (1985) A theoretical model of phase transitionin human hand movements. Biol Cybern 51:347–356

    MathSciNet  MATH  Google Scholar 

  20. Jantzen KJ, Kelso JAS (2007) Neural coordination dynamics of humansensorimotor behavior: A review. In: Jirsa VK, McIntosh AR (eds) Handbook of Brain Connectivity. Springer, Heidelberg

    Google Scholar 

  21. Jeka JJ, Kelso JAS (1995) Manipulating symmetry in human two-limb coordinationdynamics. J Exp Psychol Hum Percept Perform 21:360–374

    Google Scholar 

  22. Jeka JJ, Kelso JAS, Kiemel T (1993) Pattern switching in human multilimbcoordination dynamics. Bull Math Biol 55:829–845

    MATH  Google Scholar 

  23. Jeka JJ, Kelso JAS, Kiemel T (1993) Spontaneous transitions and symmetry:Pattern dynamics in human four limb coordination. Hum Mov Sci 12:627–651

    Google Scholar 

  24. Jirsa VK, Fink P, Foo P, Kelso JAS (2000) Parameteric stabilization ofbiological coordination: a theoretical model. J Biol Phys 26:85–112

    Google Scholar 

  25. Kay BA, Kelso JAS, Saltzman EL, Schöner G (1987) Space-time behavior of singleand bimanual rhythmic movements: Data and limit cycle model. J Exp Psychol Hum Percept Perform 13:178–192

    Google Scholar 

  26. Kay BA, Saltzman EL, Kelso JAS (1991) Steady state and perturbed rhythmicalmovements: Dynamical modeling using a variety of analytic tools. J Exp Psychol Hum Percept Perform 17:183–197

    Google Scholar 

  27. Kelso JAS (1981) On the oscillatory basis of movement. Bull Psychon Soc18:63

    Google Scholar 

  28. Kelso JAS (1984) Phase transitions and critical behavior in human bimanualcoordination. Am J Physiol Regul Integr Comp 15:R1000–R1004

    Google Scholar 

  29. Kelso JAS, Jeka JJ (1992) Symmetry breaking dynamics of human multilimbcoordination. J Exp Psychol Hum Percept Perform 18:645–668

    Google Scholar 

  30. Kelso JAS, Scholz JP, Schöner G (1986) Nonequilibrium phase transitions incoordinated biological motion: Critical fluctuations. Phys Lett A 118:279–284

    Google Scholar 

  31. Kelso JAS, Schöner G, Scholz JP, Haken H (1987) Phase locked modes, phasetransitions and component oscillators in coordinated biological motion. Phys Scr 35:79–87

    Google Scholar 

  32. Kelso JAS, DelColle J, Schöner G (1990) Action-perception asa pattern forming process. In: Jannerod M (ed) Attention and performance XIII. Erlbaum, Hillsdale, pp 139–169

    Google Scholar 

  33. Kelso JAS, Bressler SL, Buchanan S, DeGuzman GC, Ding M, Fuchs A, Holroyd T(1992) A phase transition in human brain and behavior. Phys Lett A 169:134–144

    ADS  Google Scholar 

  34. Kelso JAS, Fuchs A, Holroyd T, Lancaster R, Cheyne D, Weinberg H (1998)Dynamic cortical activity in the human brain reveals motor equivalence. Nature 392:814–818

    ADS  Google Scholar 

  35. Mayville JM, Fuchs A, Ding M, Cheyne D, Deecke L, Kelso JAS (2001)Event-related changes in neuromagnetic activity associated with syncopation and synchronization timing tasks. Hum Brain Mapp14:65–80

    Google Scholar 

  36. MayvilleJM, Fuchs A, Kelso JAS (2005) Neuromagnetic motor fields accompanyingself-paced rhythmic finger movements of different rates. Exp Brain Res166:190–199

    Google Scholar 

  37. Park H, Turvey MT (2008) Imperfect symmetry and the elementary coordinationlaw. In: Fuchs A, Jirsa VK (eds) Coordination: Neural, Behavioral and Social Dynamics. Springer, Heidelberg,pp 3–25

    Google Scholar 

  38. Peper CE, Beek PJ (1998) Distinguishing between the effects of frequency andamplitude on interlimb coupling in tapping a 2:3 polyrhythm. Exp Brain Res 118:78–92

    Google Scholar 

  39. Peper CE, Beek PJ, van Wieringen PC (1995) Frequency-induced phasetransitions in bimanual tapping. Biol Cybern 73:303–309

    Google Scholar 

  40. Post AA, Peeper CE, Daffertshofer A, Beek PJ (2000) Relative phase dynamics inperturbed interlimb coordination: stability and stochasticity. Biol Cybern 83:443–459

    MATH  Google Scholar 

  41. Schmidt RC, Beek PJ, Treffner PJ, Turvey MT (1991) Dynamical substructure ofcoordinated rhythmic movements. J Exp Psychol Hum Percept Perform 17:635–651

    Google Scholar 

  42. Schöner G, Kelso JAS (1988) Dynamic pattern generation in behavioral andneural systems. Science 239:1513–1520

    Google Scholar 

  43. Schöner G, Haken H, Kelso JAS (1986) A stochastic theory of phasetransitions in human hand movements. Biol Cybern 53:442–453

    Google Scholar 

  44. Scholz JP, Kelso JAS (1989) A quantitative approach to understanding theformation and change of coordinated movement patterns. J Mot Behav 21:122–144

    Google Scholar 

  45. Scholz JP, Kelso JAS, Schöner G (1987) Nonequilibrium phase transitions incoordinated biological motion: Critical slowing down and switching time. Phys Lett A 8:90–394

    Google Scholar 

  46. Sternad D, Collins D, Turvey MT (1995) The detuning factor in the dynamics ofinterlimb rhythmic coordination. Biol Cybern 73:27–35

    Google Scholar 

  47. Sternad D, Turvey MT, Saltzman EL (1999) Dynamics of 1:2 Coordination:Generalizing Relative Phase to n:m Rhythms. J Mot Behav 31:207–233

    Google Scholar 

  48. KelsoJAS, DeGuzman GC, (1988) Order in time: How the cooperation betweenthe hands informs the design of the brain. In: Haken H (ed) Neural andSynergetic Computers. Springer, Berlin

    Google Scholar 

  49. DeGuzmanGC, Kelso JAS (1991) Multifrequency behavioral patterns and the phaseattractive circle map. Biol Cybern 64:485–495

    MATH  Google Scholar 

Books and Reviews

  1. Fuchs A, Jirsa VK (eds) (2007) Coordination: Neural, Behavioral and Social Dynamics. Springer, Heidelberg

    Google Scholar 

  2. Jirsa VK, Kelso JAS (eds) (2004) Coordination Dynamics: Issues and Trends. Springer, Heidelberg

    Google Scholar 

  3. Kelso JAS (1995) Dynamics Pattern: The Self-Organization of Brain and Behavior. MIT Press, Cambridge

    Google Scholar 

  4. Haken H (1996) Principles of Brain Functioning. Springer, Heidelberg

    MATH  Google Scholar 

  5. Tschacher W, Dauwalder JP (eds) (2003) The Dynamical Systems Approach to Cognition: Concepts and Empirical Paradigms Based on Self-Organization, Embodiment and Coordination Dynamics. World Scientific, Singapore

    Google Scholar 

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Acknowledgment

Work reported herein was supported by NINDS grant48299, NIMH grants 42900 and 80838, and the Pierre de Fermat Chair to J.A.S.K.

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Fuchs, A., Kelso, J.A.S. (2009). Movement Coordination . In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_341

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