Modeling of Human Behavior Within the Paradigm of Modern Physics

  • Ihor Lubashevsky
Part of the Understanding Complex Systems book series (UCS)


A considerable progress in modeling human actions and social phenomena achieved at the beginning of twenty-first century demonstrates that the notions and formalism developed in modern physics are really efficient in describing systems and phenomena where human role is crucial. It turns our that a wide variety of fundamental notions such as dynamical systems, attractors, deterministic chaos, Markov stochastic processes, cooperative behavior, self-organization, phase transitions play a crucial role in understanding and modeling many mental processes and social phenomena. As a result, a number of novel interdisciplinary branches of science like sociophysics, econophysics, brain dynamics have been developed.


Traffic Flow Collective Variable Brain Dynamic Pedestrian Motion Social Force Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Abrams, D.M., Strogatz, S.H.: Chimera states for coupled oscillators. Phys. Rev. Lett. 93, 174102 (2004)ADSCrossRefGoogle Scholar
  2. Anderson, F.C., Pandy, M.G.: Dynamic optimization of human walking. J. Biomech. Eng. 123 (5), 381–390 (2001)CrossRefGoogle Scholar
  3. Antonopoulos, C.G., Srivastava, S., Pinto, S.E.d.S., Baptista, M.S.: Do brain networks evolve by maximizing their information flow capacity? PLoS Comput. Biol. 11 (8), 1–29 (2015)Google Scholar
  4. Arenas, A., Díaz-Guilera, A., Kurths, J., Moreno, Y., Zhou, C.: Synchronization in complex networks. Phys. Rep. 469 (3), 93–153 (2008)ADSMathSciNetCrossRefGoogle Scholar
  5. Aruin, A.S.: The effect of changes in the body configuration on anticipatory postural adjustments. Mot. Control 7 (3), 264–277 (2003)CrossRefGoogle Scholar
  6. Asaro, P.: Heinz von Foerster and the bio-computing movements of the 1960s. In: Müller, A., Müller, K.H. (eds.) An Unfinished Revolution?: Heinz Von Foerster and the Biological Computer Laboratory, BCL, 1958–1976, pp. 253–275. Edition Echoraum, Vienna (2007)Google Scholar
  7. Ashby, W.: Principles of the self-organizing dynamic system. J. Gen. Psychol. 37 (2), 125–128 (1947)CrossRefGoogle Scholar
  8. Axelrod, R.: The dissemination of culture: a model with local convergence and global polarization. J. Confl. Resolut. 41 (2), 203–226 (1997)CrossRefGoogle Scholar
  9. Azevedo, F.A.C., Carvalho, L.R.B., Grinberg, L.T., Farfel, J.M., Ferretti, R.E.L., Leite, R.E.P., Filho, W.J., Lent, R., Herculano-Houzel, S.: Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol. 513 (5), 532–541 (2009)CrossRefGoogle Scholar
  10. Babiloni, F., Astolfi, L.: Social neuroscience and hyperscanning techniques: past, present and future. Neurosci. Biobehav. Rev. 44, 76–93 (2014)CrossRefGoogle Scholar
  11. Bak, P.: How Nature Works: The Science of Self-Organised Criticality. Copernicus/Springer, New York (1996)zbMATHCrossRefGoogle Scholar
  12. Balanov, A., Janson, N., Postnov, D., Sosnovtseva, O.: Synchronization: From Simple to Complex. Springer, Berlin (2009)zbMATHGoogle Scholar
  13. Balasubramaniam, R., Feldman, A.G.: Guiding movements without redundancy problems. In: Jirsa, V.K., Kelso, J.A.S. (eds.) Coordination Dynamics: Issues and Trends, pp. 155–176. Springer, Berlin (2004)CrossRefGoogle Scholar
  14. Bando, M., Hasebe, K., Nakanishi, K., Nakayama, A.: Analysis of optimal velocity model with explicit delay. Phys. Rev. E 58, 5429–5435 (1998)ADSCrossRefGoogle Scholar
  15. Bando, M., Hasebe, K., Nakayama, A., Shibata, A., Sugiyama, Y.: Dynamical model of traffic congestion and numerical simulation. Phys. Rev. E 51, 1035–1042 (1995)ADSCrossRefGoogle Scholar
  16. Barab, P.: The Complementary Nature of Reality. Open Way Press, Portland (2010)Google Scholar
  17. Barrat, A., Barthelemy, M., Vespignani, A.: Dynamical Processes on Complex Networks. Cambridge University Press, Cambridge (2008)zbMATHCrossRefGoogle Scholar
  18. Beek, P.J., Peper, C.E., Daffertshofer, A.: Modeling rhythmic interlimb coordination: beyond the Haken–Kelso–Bunz model. Brain Cogn. 48 (1), 149–165 (2002)CrossRefGoogle Scholar
  19. Bellomo, N., Dogbe, C.: On the modeling of traffic and crowds: a survey of models, speculations, and perspectives. SIAM Rev. 53 (3), 409–463 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  20. Bellomo, N., Gibelli, L.: Toward a mathematical theory of behavioral-social dynamics for pedestrian crowds. Math. Mod. Methods Appl. Sci. 25 (13), 2417–2437 (2015)MathSciNetzbMATHCrossRefGoogle Scholar
  21. Bellomo, N., Piccoli, B., Tosin, A.: Modeling crowd dynamics from a complex system viewpoint. Math. Mod. Methods Appl. Sci. 22 (supp02), 1230004 [29 pages] (2012)Google Scholar
  22. Beni, G.: Swarm intelligence. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 8869–8888. Springer Science+Buisiness Media, LLC, New York (2009)CrossRefGoogle Scholar
  23. Bernstein, N.A.: The problem of interrelation between coordination and localization. Arch. Biol. Sci. 38, 1–35. (1935, in Russian)Google Scholar
  24. Bernstein, N.A.: Urgent problems of the physiology of activity. Probl. Cybern. 6, 101–160 (1961, in Russian)Google Scholar
  25. Bernstein, N.A.: Essays on the Physiology of Movements and Physiology of Activity. Meditsina, Moscow (1966, in Russian).Google Scholar
  26. Bernstein, N.A.: The Co-ordination and Regulation of Movements. Pergamon Press, Oxford (1967)Google Scholar
  27. Bertin, E., Droz, M., Grégoire, G.: Hydrodynamic equations for self-propelled particles: microscopic derivation and stability analysis. J. Phys. A: Math. Theor. 42 (44), 445001 (2009)ADSzbMATHCrossRefGoogle Scholar
  28. Blue, V., Adler, J.: Emergent fundamental pedestrian flows from cellular automata microsimulation. Transp. Res. Rec.: J. Transp. Res. Board 1644, 29–36 (1998)Google Scholar
  29. Blue, V., Adler, J.: Cellular automata microsimulation of bidirectional pedestrian flows. Transp. Res. Rec.: J. Transp. Res. Board 1678, 135–141 (1999)Google Scholar
  30. Bordogna, C.M., Albano, E.V.: Dynamic behavior of a social model for opinion formation. Phys. Rev. E 76 (6), 061125 (2007a)ADSCrossRefGoogle Scholar
  31. Bordogna, C.M., Albano, E.V.: Statistical methods applied to the study of opinion formation models: a brief overview and results of a numerical study of a model based on the social impact theory. J. Phys. Condens. Matter 19 (6), 065144 (2007b)ADSCrossRefGoogle Scholar
  32. Braun, J., Mattia, M.: Attractors and noise: twin drivers of decisions and multistability. NeuroImage 52 (3), 740–751 (2010). Special issue: Computational Models of the BrainGoogle Scholar
  33. Breakspear, M.: “Dynamic” connectivity in neural systems. Neuroinformatics 2 (2), 205–224 (2004)CrossRefGoogle Scholar
  34. Breakspear, M., Jirsa, V.K.: Neuronal dynamics and brain connectivity. In: Handbook of Brain Connectivity, pp. 3–64. Springer, Berlin (2007)Google Scholar
  35. Breakspear, M., Stam, C.J.: Dynamics of a neural system with a multiscale architecture. Philos. Trans. R. Soc. B: Biol. Sci. 360 (1457), 1051–1074 (2005)CrossRefGoogle Scholar
  36. Bressler, S.L., Seth, A.K.: Wiener–Granger causality: a well established methodology. NeuroImage 58 (2), 323–329 (2011)CrossRefGoogle Scholar
  37. Bressloff, P.C.: Spatiotemporal dynamics of continuum neural fields. J. Phys. A: Math. Theor. 45 (3), 033001 (2012)ADSMathSciNetzbMATHCrossRefGoogle Scholar
  38. Burdet, E., Osu, R., Franklin, D.W., Milner, T.E., Kawato, M.: The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature 414 (6862), 446–449 (2001)ADSCrossRefGoogle Scholar
  39. Burstedde, C., Klauck, K., Schadschneider, A., Zittarz, J.: Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Phys. A: Stat. Mech. Its Appl. 295 (3–4), 507–525 (2001)ADSzbMATHCrossRefGoogle Scholar
  40. Calvin, S., Milliex, L., Coyle, T., Temprado, J.-J.: Stabilization and destabilization of perception-action patterns influence the self-organized recruitment of degrees of freedom. J. Exp. Psychol.: Hum. Percept. Perform. 30 (6), 1032–1042 (2004)Google Scholar
  41. Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. Princeton University Press, Princeton (2001)zbMATHGoogle Scholar
  42. Campbell, S.A.: Time delays in neural systems. In: Jirsa, V.K., McIntosh, A.R. (eds.) Handbook of Brain Connectivity, pp. 65–90. Springer, Berlin (2007)CrossRefGoogle Scholar
  43. Cangelosi, A., Parisi, D. (eds): Simulating the Evolution of Language. Springer, London (2002)zbMATHGoogle Scholar
  44. Carrillo, J.A., Fornasier, M., Rosado, J., Toscani, G.: Asymptotic flocking dynamics for the kinetic Cucker-Smale model. SIAM J. Math. Anal. 42 (1), 218–236 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  45. Carter, P., Christiansen, P.L., Gaididei, Y.B., Gorria, C., Sandstede, B., Sørensen, M.P., Starke, J.: Multijam solutions in traffic models with velocity-dependent driver strategies. SIAM J. Appl. Math. 74 (6), 1895–1918 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  46. Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Rev. Mod. Phys. 81 (2), 591–646 (2009)ADSCrossRefGoogle Scholar
  47. Castellano, C., Marsili, M., Vespignani, A.: Nonequilibrium phase transition in a model for social influence. Phys. Rev. Lett. 85 (16), 3536 (2000)ADSCrossRefGoogle Scholar
  48. Chakrabarti, B., Chakraborti, A., Chatterjee, A.: Econophysics and Sociophysics: Trends and Perspectives. Wiley-VCH Verlag GmbH & Co. KGaA, Weinhaim (2006)CrossRefGoogle Scholar
  49. Chater, N., Tenenbaum, J.B., Yuille, A.: Probabilistic models of cognition: conceptual foundations. Trends Cogn. Sci. 10 (7), 287–291 (2006)CrossRefGoogle Scholar
  50. Chialvo, D.R.: Emergent complex neural dynamics. Nat. Phys. 6 (10), 744–750 (2010)CrossRefGoogle Scholar
  51. Chicharro, D., Ledberg, A.: When two become one: the limits of causality analysis of brain dynamics. PLoS ONE 7 (3), 1–16 (2012)CrossRefGoogle Scholar
  52. Chowdhury, D., Santen, L., Schadschneider, A.: Statistical physics of vehicular traffic and some related systems. Phys. Rep. 329 (4–6), 199–329 (2000)ADSMathSciNetCrossRefGoogle Scholar
  53. Cialdini, R.B., Goldstein, N.J.: Social influence: compliance and conformity. Annu. Rev. Psychol. 55, 591–621 (2004)CrossRefGoogle Scholar
  54. Clark, A.: Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36 (03), 181–204 (2013)CrossRefGoogle Scholar
  55. Conradt, L., List, C.: Group decisions in humans and animals: a survey. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 364 (1518), 719–742 (2009)CrossRefGoogle Scholar
  56. Culicover, P.W., Nowak, A.: Dynamical Grammar: Minimalism, Acquisition, and Change. Oxford University Press, Oxford (2003)Google Scholar
  57. Czirók, A., Vicsek, M., Vicsek, T.: Collective motion of organisms in three dimensions. Phys. A: Stat. Mech. Appl. 264 (1), 299–304 (1999)zbMATHCrossRefGoogle Scholar
  58. Czirók, A., Vicsek, T.: Collective behavior of interacting self-propelled particles. Phys. A: Stat. Mech. Appl. 281 (1), 17–29 (2000)CrossRefGoogle Scholar
  59. Dana, S.K., Roy, P.K., Kurths, J. (eds.): Complex Dynamics in Physiological Systems: From Heart to Brain. Springer Science+Business Media B.V., Dordrecht (2009)zbMATHGoogle Scholar
  60. Daunizeau, J., David, O., Stephan, K.E.: Dynamic causal modelling: a critical review of the biophysical and statistical foundations. NeuroImage 58 (2), 312–322 (2011)CrossRefGoogle Scholar
  61. Davidson, P.A.: Turbulence: An Introduction for Scientists and Engineers, 2nd edn. Oxford University Press, Oxford (2015)zbMATHCrossRefGoogle Scholar
  62. De Luca, C., Jantzen, K.J., Comani, S., Bertollo, M., Kelso, J.A.S.: striatal activity during intentional switching depends on pattern stability. J. Neurosci. 30 (9), 3167–3174 (2010)Google Scholar
  63. Deco, G., Jirsa, V.K., Robinson, P.A., Breakspear, M., Friston, K.: The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS Comput. Biol. 4 (8), e1000092 (35 pages) (2008)Google Scholar
  64. Deffuant, G., Amblard, F., Weisbuch, G., Faure, T.: How can extremism prevail? A study based on the relative agreement interaction model. J. Artif. Soc. Soc. Simul. 5 (4) (2002)Google Scholar
  65. Degond, P., Dimarco, G., Mac, T.B.N.: Hydrodynamics of the Kuramoto–Vicsek model of rotating self-propelled particles. Math. Mod. Methods Appl. Sci. 24 (02), 277–325 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  66. Degond, P., Liu, J.-G.: Hydrodynamics of self-alignment interactions with precession and derivation of the Landau–Lifschitz–Gilbert equation. Math. Mod. Methods Appl. Sci. 22 (supp01), 1140001 (18 pages) (2012)Google Scholar
  67. Demšar, J., Hemelrijk, C.K., Hildenbrandt, H., Bajec, I.L.: Simulating predator attacks on schools: evolving composite tactics. Ecol. Model. 304, 22–33 (2015)CrossRefGoogle Scholar
  68. Devaney, R.L.: An Introduction to Chaotic Dynamical Systems, 2nd edn. Westview Press, Boulder (2003)zbMATHGoogle Scholar
  69. Diedrichsen, J., Shadmehr, R., Ivry, R.B.: The coordination of movement: optimal feedback control and beyond. Trends Cogn. Sci. 14 (1), 31–39 (2010)CrossRefGoogle Scholar
  70. Dietmar, P., Thiagarajan, T.C.: The organizing principles of neuronal avalanches: cell assemblies in the cortex? Trends Neurosci. 30 (3), 101–110 (2007)CrossRefGoogle Scholar
  71. Dörfler, F., Bullo, F.: Synchronization in complex networks of phase oscillators: a survey. Automatica 50 (6), 1539–1564 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  72. Dubois, D.M.: Incursive and hyperincursive systems, fractal machine and anticipatory logic. AIP Conf. Proc. 573, 437–451 (2001)ADSCrossRefGoogle Scholar
  73. Dubois, D.M.: Mathematical foundations of discrete and functional systems with strong and weak anticipations. In: Butz, M.V., Sigaud, O., Gérard, P. (eds.) Anticipatory Behavior in Adaptive Learning Systems: Foundations, Theories, and Systems, pp. 110–132. Springer, Berlin (2003)CrossRefGoogle Scholar
  74. Edelman, G.M.: Bright Air, Brilliant Fire: On the Matter of the Mind. BasicBooks, New York (1992)Google Scholar
  75. Edelman, G.M.: Wider Than the Sky: The Phenomenal Gift of Consciousness. Yale University Press, London (2004)Google Scholar
  76. Edelman, G.M.: Second Nature: Brain Science and Human Knowledge. Yale University Press, London (2006)Google Scholar
  77. Edelman, G.M., Gally, J.A.: Degeneracy and complexity in biological systems. Proc. Natl. Acad. Sci. 98 (24), 13763–13768 (2001)ADSCrossRefGoogle Scholar
  78. Edelman, G.M., Tononi, G.: A Universe Of Consciousness: How Matter Becomes Imagination. Basic Books, New York (2000)Google Scholar
  79. Elliott, D., Smith, D.: Football stadia disasters in the United Kingdom: learning from tragedy? Organ. Environ. 7 (3), 205–229 (1993)CrossRefGoogle Scholar
  80. Feigenberg, I.M.: Probabilistic prognosis and its significance in normal and pathological subjects. In: Cole, M., Malzman, I. (eds.) Handbook of Contemporary Soviet Psychology. Foreworded by A.N. Leont’ev, A.R. Luria, and A.A. Smirnov, pp. 355–360. Basic Books, New York (1969)Google Scholar
  81. Feigenberg, I.M.: The model of the future in motor control. In: Latash, M.L. (ed.) Progress in Motor Control, Vol. I: Bernstein’s Traditions in Movement Studies, vol. 1, pp. 89–104. Human Kinetics, Champaign (1998)Google Scholar
  82. Feigenberg, I.M.: Memory, probabilistic prognosis, and presetting for action. In: Nadin, M. (ed.) Anticipation: Learning from the Past The Russian/Soviet Contributions to the Science of Anticipation, pp. 301–312. Springer, Cham (2015)CrossRefGoogle Scholar
  83. Feistel, R., Ebeling, W.: Physics of Self-Organization and Evolution. Wiley-VCH Verlag & Co. KGaA, Weinheim (2011)zbMATHCrossRefGoogle Scholar
  84. Feldman, A.G.: Functional tuning of the nervous system with control of movement of maintenance of a steady posture of movement or maintenance of a steady posture: II. Controllable parameters of the muscles. Biophysics 11, 498–508 (1966)Google Scholar
  85. Feldman, A.G.: Once more on the equilibrium-point hypothesis (λ model) for motor control. J. Mot. Behav. 18 (1), 17–54 (1986)CrossRefGoogle Scholar
  86. Feldman, A.G.: Origin and advances of the equilibrium-point hypothesis. In: Sternad, D. (ed.) Progress in Motor Control: A Multidisciplinary Perspective, pp. 637–643. Springer Science+Business Media, LLC, New York (2009)CrossRefGoogle Scholar
  87. Feldman, A.G.: Space and time in the context of equilibrium-point theory. Wiley Interdiscip. Rev.: Cogn. Sci. 2 (3), 287–304 (2011)MathSciNetCrossRefGoogle Scholar
  88. Feldman, A.G., Levin, M.F.: The equilibrium-point hypothesis – past, present and future. In: Sternad, D. (ed.) Progress in Motor Control: A Multidisciplinary Perspective, pp. 699–726. Springer Science+Business Media, LLC, New York (2009)CrossRefGoogle Scholar
  89. Flash, T., Hogan, N.: The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci. 5 (7), 1688–1703 (1985)Google Scholar
  90. Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11 (2), 127–138 (2010a)CrossRefGoogle Scholar
  91. Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11 (2), 127–138 (2010b)CrossRefGoogle Scholar
  92. Friston, K.: What is optimal about motor control? Neuron 72 (3), 488–498 (2011)CrossRefGoogle Scholar
  93. Friston, K., Ao, P.: Free energy, value, and attractors. Comput. Math. Methods Med. 2012, Article 937860 (27 pages) (2012)Google Scholar
  94. Friston, K.J., Daunizeau, J., Kiebel, S.J.: Reinforcement learning or active inference? PloS ONE 4 (7), e6421 (2009)ADSCrossRefGoogle Scholar
  95. Friston, K.J., Daunizeau, J., Kilner, J., Kiebel, S.J.: Action and behavior: a free-energy formulation. Biol. Cybern. 102 (3), 227–260 (2010)CrossRefGoogle Scholar
  96. Friston, K.J., Harrison, L., Penny, W.: Dynamic causal modelling. NeuroImage 19 (4), 1273–1302 (2003)CrossRefGoogle Scholar
  97. Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., Pezzulo, G.: Active inference and epistemic value. Cogn. Neurosci. 6 (4), 187–214 (2015)CrossRefGoogle Scholar
  98. Friston, K., Schwartenbeck, P., Fitzgerald, T., Moutoussis, M., Behrens, T., Dolan, R.J.: The anatomy of choice: active inference and agency. Front. Hum. Neurosci. 7 (598), Article 598 (pp. 1–18) (2013)Google Scholar
  99. Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., Dolan, R.J.: The anatomy of choice: dopamine and decision-making. Philos. Trans. R. Soc. B: Biol. Sci. 369 (1655), 20130481 (2014)CrossRefGoogle Scholar
  100. Fuchs, A., Kelso, J.A.S.: Movement coordination. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 5718–5736. Springer Science+Buisiness Media, LLC, New York (2009)CrossRefGoogle Scholar
  101. Fukui, M., Ishibashi, Y.: Self-organized phase transitions in cellular automaton models for pedestrians. J. Phys. Soc. Jpn. 68 (8), 2861–2863 (1999)ADSCrossRefGoogle Scholar
  102. Gaididei, Y.B., Gorria, C., Berkemer, R., Kawamoto, A., Shiga, T., Christiansen, P.L., Sørensen, M.P., Starke, J.: Controlling traffic jams by time modulating the safety distance. Phys. Rev. E 88 (4), 042803 (2013)ADSCrossRefGoogle Scholar
  103. Galam, S.: Sociophysics: a review of Galam models. Int. J. Mod. Phys. C 19 (03), 409–440 (2008)ADSzbMATHCrossRefGoogle Scholar
  104. Galam, S.: Sociophysics: A Physicist’s Modeling of Psycho-Political Phenomena. Springer, New York (2012)CrossRefGoogle Scholar
  105. Gardiner, C.: Stochastic Methods: A Handbook for the Natural and Social Sciences, 4th edn. Springer, Berlin (2009)zbMATHGoogle Scholar
  106. Gazis, D.C., Herman, R., Rothery, R.W.: Nonlinear follow-the-leader models of traffic flow. Oper. Res. 9 (4), 545–567 (1961)MathSciNetzbMATHCrossRefGoogle Scholar
  107. Gelfand, I.M., Latash, M.L.: On the problem of adequate language in motor control. Mot. Control 2 (4), 306–313 (1998)CrossRefGoogle Scholar
  108. Gerstner, W., Kistler, W.M., Naud, R., Paninski, L.: Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Cambridge University Press, Cambridge (2014)CrossRefGoogle Scholar
  109. Gipps, P.G., Marksjö, B.: A micro-simulation model for pedestrian flows. Math. Comput. Simul. 27 (2), 95–105 (1985)CrossRefGoogle Scholar
  110. Glansdorff, P., Prigogine, I.: Thermodynamic Theory of Structure, Stability and Fluctuations. Wiley, London (1971)zbMATHGoogle Scholar
  111. Goldman, A.I.: Simulating Minds: The Philosophy, Psychology, and Neuroscience of Mindreading. Oxford University Press, Oxford (2006)CrossRefGoogle Scholar
  112. Grush, R.: The emulation theory of representation: motor control, imagery, and perception. Behav. Brain Sci. 27 (3), 377–396 (2004)Google Scholar
  113. Guigon, E., Baraduc, P., Desmurget, M.: Coding of movement-and force-related information in primate primary motor cortex: a computational approach. Eur. J. Neurosci. 26 (1), 250–260 (2007a)CrossRefGoogle Scholar
  114. Guigon, E., Baraduc, P., Desmurget, M.: Computational motor control: redundancy and invariance. J. Neurophys. 97 (1), 331–347 (2007b)CrossRefGoogle Scholar
  115. Guigon, E., Baraduc, P., Desmurget, M.: Computational motor control: feedback and accuracy. Eur. J. Neurosci. 27 (4), 1003–1016 (2008)CrossRefGoogle Scholar
  116. Ha, S.-Y., Tadmor, E.: From particle to kinetic and hydrodynamic descriptions of flocking. Kinet. Relat. Mod. 1 (3), 415–435 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  117. Haken, H.: Information and Self-Organization: A Macroscopic Approach to Complex Systems, 3rd edn. Springer, Berlin (2006)zbMATHGoogle Scholar
  118. Haken, H.: Brain Dynamics: An Introduction to Models and Simulations, 2nd edn. Springer, Berlin (2008a)zbMATHGoogle Scholar
  119. Haken, H.: Self-organization. Scholarpedia 3 (8), 1401 (2008b). Revision #137295Google Scholar
  120. Haken, H.: Synergetics: basic concepts. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 8926–8946. Springer Science+Buisiness Media, LLC, New York (2009a)CrossRefGoogle Scholar
  121. Haken, H.: Introduction to Synergetics. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 8946–8948. Springer Science+Buisiness Media, LLC, New York (2009b)CrossRefGoogle Scholar
  122. Haken, H., Kelso, J.A.S., Bunz, H.: A theoretical model of phase transitions in human hand movements. Biol. Cybern. 51 (5), 347–356 (1985)MathSciNetzbMATHCrossRefGoogle Scholar
  123. Harris, C.M., Wolpert, D.M.: Signal-dependent noise determines motor planning. Nature 394 (6695), 780–784 (1998)ADSCrossRefGoogle Scholar
  124. Hatze, H., Buys, J.D.: Energy-optimal controls in the Mammalian neuromuscular system. Biol. Cybern. 27 (1), 9–20 (1977)zbMATHCrossRefGoogle Scholar
  125. Hegselmann, R., Krause, U., et al.: Opinion dynamics and bounded confidence models, analysis, and simulation. J. Artif. Soc. Soc. Simul. 5 (3), 1–33 (2002)Google Scholar
  126. Helbing, D.: A mathematical model for the behavior of pedestrians. Behav. Sci. 36 (4), 298–310 (1991)CrossRefGoogle Scholar
  127. Helbing, D.: A mathematical model for the behavior of individuals in a social field. J. Math. Soc. 19 (3), 189–219 (1994)CrossRefGoogle Scholar
  128. Helbing, D.: Traffic and related self-driven many-particle systems. Rev. Mod. Phys. 73, 1067–1141 (2001)ADSCrossRefGoogle Scholar
  129. Helbing, D.: Quantitative Sociodynamics Stochastic Methods and Models of Social Interaction Processes. Springer, Berlin (2010)zbMATHGoogle Scholar
  130. Helbing, D., Buzna, L., Johansson, A., Werner, T.: Self-organized pedestrian crowd dynamics: experiments, simulations, and design solutions. Transp. Sci. 39 (1), 1–24 (2005)CrossRefGoogle Scholar
  131. Helbing, D. (ed.): Social Self-Organization: Agent-Based Simulations and Experiments to Study Emergent Social Behavior. Springer, Berlin (2012)Google Scholar
  132. Helbing, D., Farkas, I.J., Vicsek, T.: Freezing by heating in a driven mesoscopic system. Phys. Rev. Lett. 84 (6), 1240 (2000a)ADSzbMATHCrossRefGoogle Scholar
  133. Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407 (6803), 487–490 (2000b)ADSCrossRefGoogle Scholar
  134. Helbing, D., Johansson, A.: Pedestrian, crowd and evacuation dynamics. In: Meyers, R. (ed.) Encyclopedia of Complexity and Systems Science, pp. 6476–6495. Springer Science+Buisiness Media, LLC, New York (2009)CrossRefGoogle Scholar
  135. Helbing, D., Johansson, A., Al-Abideen, H.Z.: Dynamics of crowd disasters: an empirical study. Phys. Rev. E 75 (4), 046109 (2007)ADSCrossRefGoogle Scholar
  136. Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51 (5), 4282 (1995)ADSCrossRefGoogle Scholar
  137. Helbing, D., Molnár, P., Farkas, I. J., Bolay, K.: Self-organizing pedestrian movement. Environ. Plann. B: Plann. Des. 28 (3), 361–383 (2001)CrossRefGoogle Scholar
  138. Hesse, J., Gross, T.: Self-organized criticality as a fundamental property of neural systems. Front. Syst. Neurosci. 8, Article 166, (14 pages) (2014)Google Scholar
  139. Hesslow, G.: Conscious thought as simulation of behaviour and perception. Trends Cogn. Sci. 6 (6), 242–247 (2002)CrossRefGoogle Scholar
  140. Hildenbrandt, H., Carere, C., Hemelrijk, C.K.: Self-organized aerial displays of thousands of starlings: a model. Behav. Ecol. 21 (6), 1349–1359 (2010)CrossRefGoogle Scholar
  141. Hilgetag, C.C., Kaiser, M.: Clustered organization of cortical connectivity. Neuroinformatics 2 (3), 353–360 (2004)CrossRefGoogle Scholar
  142. Hizanidis, J., Kanas, V.G., Bezerianos, A., Bountis, T.: Chimera states in networks of nonlocally coupled Hindmarsh–Rose Neuron models. Int. J. Bifurcation Chaos 24 (03), 1450030 (2014)ADSMathSciNetzbMATHCrossRefGoogle Scholar
  143. Hizanidis, J., Kouvaris, N.E., Gorka, Z.-L., Díaz-Guilera, A., Antonopoulos, C.G.: Chimera-like states in modular neural networks. Sci. Rep. 6, 19845 (2016)ADSCrossRefGoogle Scholar
  144. Hogan, N.: An organizing principle for a class of voluntary movements. J. Neurosci. 4 (11), 2745–2754 (1984)Google Scholar
  145. Hölldobler, B., Wilson, E.O.: The Superorganisms: The Beauty, Elegance, and Strangeness of Insect Societies. W. W. Norton & Company, Inc., New York (2009)Google Scholar
  146. Hołyst, J.A., Kacperski, K., Schweitzer, F.: Phase transitions in social impact models of opinion formation. Phys. A: Stat. Mech. Appl. 285 (1), 199–210 (2000)zbMATHCrossRefGoogle Scholar
  147. Hołyst, J.A., Kacperski, K., Schweitzer, F.: Social impact models of opinion dynamics. In: Stauffer, D. (ed.) Annual Reviews of Computational Physics, vol. 9, pp. 253–273. World Scientific, Singapore (2001)CrossRefGoogle Scholar
  148. Hoogendoorn, S., Knoop, V.: Traffic flow theory and modelling. In: van Wee, B., Annema, J.A., Banister, D. (eds.) The Transport System and Transport Policy: An Introduction, pp. 125–159. Edward Elgar Publishing, Inc, Cheltenham (2013)Google Scholar
  149. Hoogendoorn, S.P., Bovy, P.H.L.: State-of-the-art of vehicular traffic flow modelling. Proc. Inst. Mech. Eng. I: J. Syst. Control Eng. 215 (4), 283–303 (2001)CrossRefGoogle Scholar
  150. Huepe, C., Aldana, M.: New tools for characterizing swarming systems: a comparison of minimal models. Phys. A: Stat. Mech. Appl. 387 (12), 2809–2822 (2008)CrossRefGoogle Scholar
  151. Huys, R., Jirsa, V.K. (eds.): Nonlinear Dynamics in Human Behavior. Springer, Berlin (2010)Google Scholar
  152. Huys, R., Perdikis, D., Jirsa, V.K.: Functional architectures and structured flows on manifolds: a dynamical framework for motor behavior. Psychol. Rev. 121 (3), 302–336 (2014)CrossRefGoogle Scholar
  153. Hwang, E.J., Shadmehr, R.: Internal models of limb dynamics and the encoding of limb state. J. Neural Eng. 2 (3), S266–S278 (2005)CrossRefGoogle Scholar
  154. Ito, J.P.: Repetition without repetition: how Bernstein illumines motor skill in music performance. In: Nadin, M. (ed.) Anticipation: Learning from the Past the Russian/Soviet Contributions to the Science of Anticipation, pp. 257–268. Springer, Cham (2015)CrossRefGoogle Scholar
  155. Izhikevich, E.M.: Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. The MIT Press, Cambridge (2007)Google Scholar
  156. Jackson, J.M.: Social impact theory: a social forces model of influence. In: Mullen, B., Goethals, G.R. (eds.) Theories of Group Behavior, pp. 111–124. Springer, New York (1987)CrossRefGoogle Scholar
  157. Jantzen, K.J., Steinberg, F.L., Kelso, J.A.S.: Coordination dynamics of large-scale neural circuitry underlying rhythmic sensorimotor behavior. J. Cogn. Neurosci. 21 (12), 2420–2433 (2008)CrossRefGoogle Scholar
  158. Jeannerod, M.: Motor Cognition: What Actions Tell the Self. Oxford University Press, Oxford (2006)CrossRefGoogle Scholar
  159. Jensen, K., Silk, J.B., Andrews, K., Bshary, R., Cheney, D.L., Emery, N., Hemelrijk, C.K., Holekamp, K., Penn, D.C., Perner, J., Teufel, C.: Social knowledge. In: Menzel, R., Fischer, J. (eds.) Animal Thinking: Contemporary Issues in Comparative Cognition, pp. 267–291. The MIT Press, Cambridge (2011)Google Scholar
  160. Jirsa, V.K., McIntosh, A. (eds.): Handbook of Brain Connectivity. Springer, Berlin (2007)zbMATHGoogle Scholar
  161. Jordan, M.I., Wolpert, D.M.: Computational motor control. In: Gazzaniga, M.S., et al. (eds.) The New Cognitive Neurosciences, 2nd edn., pp. 601–618. The MIT Press, Cambridge (2000)Google Scholar
  162. Kalitzin, S.N., Velis, D.N., da Silva, F.L.: Autonomous in the epileptic brain anticipation and control. In: Osorio, I., Zaveri, H.P., Frei, M.G., Arthurs, S. (eds.) Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics, pp. 175–199. CRC Press/Taylor & Francis Group, LLC, London (2011)CrossRefGoogle Scholar
  163. Kawato, M.: Internal models for motor control and trajectory planning. Curr. Opin. Neurobiol. 9 (6), 718–727 (1999)CrossRefGoogle Scholar
  164. Kelso, J.A.S.: Dynamic Patterns: The Self-Organization of Brain and Behavior. The MIT Press, Cambridge (1995)Google Scholar
  165. Kelso, J.A.S.: Coordination dynamics. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 1537–1565. Springer Science+Buisiness Media, LLC, New York (2009a)CrossRefGoogle Scholar
  166. Kelso, J.A.S.: Synergies: atoms of brain and behavior. In: Sternad, D. (ed.) Progress in Motor Control: A Multidisciplinary Perspective, pp. 83–91. Springer Science+Buisiness Media, LLC, Boston (2009b)CrossRefGoogle Scholar
  167. Kelso, J.A.S.: Instabilities and phase transitions in human brain and behavior. Front. Hum. Neurosci. 4, Article 23 (2 pages) (2010)Google Scholar
  168. Kelso, J.A.S.: Multistability and metastability: understanding dynamic coordination in the brain. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 367 (1591), 906–918 (2012)CrossRefGoogle Scholar
  169. Kelso, J.A.S.: The dynamic brain in action: coordinative structures, criticality, and coordination dynamics. In: Plenz, D., Niebu, E. (eds.) Criticality in Neural Systems, pp. 67–104. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim (2014)CrossRefGoogle Scholar
  170. Kelso, J.A.S., de Guzman, G.C., Colin, R., Tognoli, E.: Virtual partner interaction (VPI): exploring novel behaviors via coordination dynamics. PLoS One 4 (6), e5749 (11 pages) (2009)Google Scholar
  171. Kelso, J.A.S., Engstrøm, D.A.: The Complementary Nature. The MIT Press, Cambridge (2006)Google Scholar
  172. Kelso, J.A.S., Scholz, J.P., Schöner, G.: Dynamics governs switching among patterns of coordination in biological movement. Phys. Lett. A 134 (1), 8–12 (1988)ADSCrossRefGoogle Scholar
  173. Kerner, B.: The Physics of Traffic: Empirical Freeway Pattern Features, Engineering Applications, and Theory. Springer, Berlin (2004)CrossRefGoogle Scholar
  174. Kerner, B.S.: Introduction to Modern Traffic Flow Theory and Control: The Long Road to Three-Phase Traffic Theory. Springer, Berlin (2009a)zbMATHCrossRefGoogle Scholar
  175. Kerner, B.S.: Traffic congestion, modeling approaches to. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 9302–9355. Springer Science+Buisiness Media, LLC, New York (2009b)CrossRefGoogle Scholar
  176. Kerner, B.S., Klenov, S.L.: A microscopic model for phase transitions in traffic flow. J. Phys. A: Math. Gen. 35 (3), L31 (2002)ADSMathSciNetzbMATHCrossRefGoogle Scholar
  177. Kerner, B.S., Klenov, S.L.: Deterministic microscopic three-phase traffic flow models. J. Phys. A: Math. Gen. 39 (8), 1775 (2006)ADSMathSciNetzbMATHCrossRefGoogle Scholar
  178. Kersten, D., Yuille, A.: Bayesian models of object perception. Curr. Opin. Neurobiol. 13 (2), 150–158 (2003)CrossRefGoogle Scholar
  179. Kirchner, A., Schadschneider, A.: Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics. Phys. A: Stat. Mech. Appl. 312 (1), 260–276 (2002)zbMATHCrossRefGoogle Scholar
  180. Kistemaker, D.A., Van Soest, A.K.J., Bobbert, M.F.: Is equilibrium point control feasible for fast goal-directed single-joint movements? J. Neurophysiol. 95 (5), 2898–2912 (2006)CrossRefGoogle Scholar
  181. Klimontovich, Y.L.: Statistical Theory of Open Systems: A Unified Approach to Kinetic Description of Processes in Active Systems. Springer Science+Business Media, B.V., Dordrecht (1995)zbMATHCrossRefGoogle Scholar
  182. Klous, M., Mikulic, P., Latash, M.L.: Two aspects of feedforward postural control: anticipatory postural adjustments and anticipatory synergy adjustments. J. Neurophysiol. 105 (5), 2275–2288 (2011)CrossRefGoogle Scholar
  183. Klous, M., Mikulic, P., Latash, M.L.: Early postural adjustments in preparation to whole-body voluntary sway. J. Electromyogr. Kinesiol. 22 (1), 110–116 (2012)CrossRefGoogle Scholar
  184. Klüpfel, H., Meyer-König, T., Wahle, J., Schreckenberg, M.: Microscopic simulation of evacuation processes on passenger ships. In: Bandini, S., Worsch, T. (eds.) Theory and Practical Issues on Cellular Automata: Proceedings of the Fourth International Conference on Cellular Automata for Research and Industry, Karlsruhe, Oct 4–6 2000, pp. 63–71. Springer, London (2001)CrossRefGoogle Scholar
  185. Kohring, G.A.: Ising models of social impact: the role of cumulative advantage. Journal de Physique I France 6 (2), 301–308 (1996)ADSCrossRefGoogle Scholar
  186. Körding, K.P., Wolpert, D.M.: Bayesian integration in sensorimotor learning. Nature 427 (6971), 244–247 (2004)ADSCrossRefGoogle Scholar
  187. Körding, K.P., Wolpert, D.M.: Bayesian decision theory in sensorimotor control. Trends Cogn. Sci. 10 (7), 319–326 (2006)CrossRefGoogle Scholar
  188. Kostrubiec, V., Tallet, J., Zanone, P.-G.: How a new behavioral pattern is stabilized with learning determines its persistence and flexibility in memory. Exp. Brain Res. 170 (2), 238–244 (2006)CrossRefGoogle Scholar
  189. Kostrubiec, V., Zanone, P.-G., Fuchs, A., Kelso, J.A.S.: Beyond the blank slate: routes to learning new coordination patterns depend on the intrinsic dynamics of the learner—experimental evidence and theoretical model. Front. Hum. Neurosci. 6, Article 222 (14 pages) (2012)Google Scholar
  190. Krause, J., Ruxton, G.D., Krause, S.: Swarm intelligence in animals and humans. Trends Ecol. Evol. 25 (1), 28–34 (2010)CrossRefGoogle Scholar
  191. Krause, S., James, R., Faria, J.J., Ruxton, G.D., Krause, J.: Swarm intelligence in humans: diversity can trump ability. Anim. Behav. 81 (5), 941–948 (2011)CrossRefGoogle Scholar
  192. Krishnan, V., Aruin, A.S., Latash, M.L.: Two stages and three components of the postural preparation to action. Exp. Brain Res. 212 (1), 47–63 (2011)CrossRefGoogle Scholar
  193. Kröger, B.: Hermann Haken: From the Laser to Synergetics: A Scientific Biography of the Early Years. Springer, Heidelberg (2015)zbMATHGoogle Scholar
  194. Latané, B.: The psychology of social impact. Am. Psychol. 36 (4), 343–356 (1981)CrossRefGoogle Scholar
  195. Latané, B.: Dynamic social impact: the creation of culture by communication. J. Commun. 46 (4), 13–25 (1996)CrossRefGoogle Scholar
  196. Latané, B., Bourgeois, M.J.: Dynamic social impact and the consolidation, clustering, correlation, and continuing diversity of culture. In: Hogg, M.A., Tindale, R.S. (eds.) Blackwell Handbook of Social Psychology: Group Processes, pp. 235–258. Blackwell Publishers Ltd., Malden (2001)Google Scholar
  197. Latané, B., Drigotas, S.: Social influence. In: Manstead, A.S.R., Hewstone, M., Fiske, S.T., Hogg, M.A., Reis, H.T., Semin, G.R. (eds.) The Blackwell encyclopedia of social psychology, pp. 562–567. Blackwell Reference/Blackwell Publishers, Cambridge (1995)Google Scholar
  198. Latash, M.L.: Neurophysiological Basis of Movement, 2nd edn. Human Kinetics, Urbana (2008a)Google Scholar
  199. Latash, M.L.: Synergy. Oxford University Press, Oxford (2008b)CrossRefGoogle Scholar
  200. Latash, M.L.: Motor synergies and the equilibrium-point hypothesis. Mot. Control 14 (3), 294–322 (2010)MathSciNetCrossRefGoogle Scholar
  201. Latash, M.L.: The bliss (not the problem) of motor abundance (not redundancy). Exp. Brain Res. 217 (1), 1–5 (2012)CrossRefGoogle Scholar
  202. Latash, M.L.: Bernstein’s “desired future” and physics of human movement. In: Nadin, M. (ed.) Anticipation: Learning from the Past the Russian/Soviet Contributions to the Science of Anticipation, pp. 287–300. Springer, Cham (2015)CrossRefGoogle Scholar
  203. Latash, M.L., Scholz, J.P., Schöner, G.: Toward a new theory of motor synergies. Mot. Control 11 (3), 276–308 (2007)CrossRefGoogle Scholar
  204. Latash, M.L., Shim, J.K., Smilga, A.V., Zatsiorsky, V.M.: A central back-coupling hypothesis on the organization of motor synergies: a physical metaphor and a neural model. Biol. Cybern. 92 (3), 186–191 (2005)zbMATHCrossRefGoogle Scholar
  205. Lee, T.D., Blandin, Y., Proteau, L.: Effects of task instructions and oscillation frequency on bimanual coordination. Psychol. Res. 59 (2), 100–106 (1996)CrossRefGoogle Scholar
  206. Lewin, K., Cartwright, D. (eds.): Field Theory in Social Science: Selected Theoretical Papers. Harpers, Oxford (1951)Google Scholar
  207. Liggett, T.M.: Interacting Particle Systems. Springer, New York (1985)zbMATHCrossRefGoogle Scholar
  208. Loreto, V., Baronchelli, A., Mukherjee, A., Puglisi, A., Tria, F.: Statistical physics of language dynamics. J. Stat. Mech: Theory Exp. 2011 (04), P04006 (2011)CrossRefGoogle Scholar
  209. Macintyre, A.: The Tasks of Philosophy: Selected Essays, vol. 1. Cambridge University Press, Cambridge (2006)CrossRefGoogle Scholar
  210. Marreiros, A.C., Stephan, K.E., Friston, K.J.: Dynamic causal modeling. Scholarpedia 5 (7), 9568 (2010). Revision #91214Google Scholar
  211. Marschler, C., Sieber, J., Berkemer, R., Kawamoto, A., Starke, J.: Implicit methods for equation-free analysis: convergence results and analysis of emergent waves in microscopic traffic models. SIAM J. Appl. Dyn. Syst. 13 (3), 1202–1238 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  212. Marschler, C., Sieber, J., Hjorth, P.G., Starke, J.: Equation-free analysis of macroscopic behavior in traffic and pedestrian flow. In: Chraibi, M., Boltes, M., Schadschneider, A. Seyfried, A. (eds.) Traffic and Granular Flow’13, pp. 423–439. Springer International Publishing, Switzerland (2015)Google Scholar
  213. Martin, V., Scholz, J.P., Schöner, G.: Redundancy, self-motion, and motor control. Neural Comput. 21 (5), 1371–1414 (2009)zbMATHCrossRefGoogle Scholar
  214. Martyushev, L.M., Seleznev, V.D.: Maximum entropy production principle in physics, chemistry and biology. Phys. Rep. 426 (1), 1–45 (2006)ADSMathSciNetCrossRefGoogle Scholar
  215. Mattos, D.J.S., Latash, M.L., Park, E., Kuhl, J., Scholz, J.P.: Unpredictable elbow joint perturbation during reaching results in multijoint motor equivalence. J. Neurophysiol. 106 (3), 1424–1436 (2011)CrossRefGoogle Scholar
  216. McIntosh, A.R.: Large-scale network dynamics in neurocognitive function. In: Fuchs, A., Jirsa, V.K. (eds.) Coordination: Neural, Behavioral and Social Dynamics, pp. 183–204. Springer, Berlin (2008)CrossRefGoogle Scholar
  217. McIntyre, J., Bizzi, E.: Servo hypotheses for the biological control of movement. J. Mot. Behav. 25 (3), 193–202 (1993)CrossRefGoogle Scholar
  218. Meyer-Lindenberg, A., Bassett, D.S.: Nonlinear and cooperative dynamics in the human brain: evidence from multimodal neuroimaging. In: Fuchs, A., Jirsa, V.K. (eds.) Coordination: Neural, Behavioral and Social Dynamics, pp. 161–181. Springer, Berlin (2008)CrossRefGoogle Scholar
  219. Meyer-Lindenberg, A., Ziemann, U., Hajak, G., Cohen, L., Berman, K.F.: Transitions between dynamical states of differing stability in the human brain. Proc. Natl. Acad. Sci. 99 (17), 10948–10953 (2002)ADSCrossRefGoogle Scholar
  220. Miller, N.E.: Experimental studies of conflict. In: Hunt, J.M. (ed.) Personality and The Behavior Disorders, vol. I, pp. 431–465. The Ronald Press Company, New York (1944)Google Scholar
  221. Miller, N.E.: Liberalization of basic S-R concepts: extensions to conflict behavior, motivation and social learning. In: Koch, S. (ed.) Psychology: A Study of a Science. General Systematic Formulations, Learning, and Special Processes, vol. 2, pp. 196–292. McGraw-Hill Book Company, Inc., New York (1959)Google Scholar
  222. Milliex, L., Calvin, S.J., Temprado, J.-J.: Limiting the recruitment of degrees of freedom reduces the stability of perception–action patterns. Hum. Mov. Sci. 24 (2), 218–233 (2005)CrossRefGoogle Scholar
  223. Mishra, S., Tunstrøm, K., Couzin, I.D., Huepe, C.: Collective dynamics of self-propelled particles with variable speed. Phys. Rev. E 86 (1), 011901 (2012)ADSCrossRefGoogle Scholar
  224. Mitra, S., Riley, M.A., Turvey, M.T.: Chaos in human rhythmic movement. J. Mot. Behav. 29 (3), 195–198 (1997)CrossRefGoogle Scholar
  225. Montagne, G., Rugy, A.D., Bueker, M., Durey, A., Taga, G., Laurent, M.: How time-to-contact is involved in the regulation of goal-directed locomotion. In: Hecht, H., Savelsburgh, G.J.P. (eds.) Time-to-Contact, pp. 475–491. Elsevier, Amsterdam (2004)CrossRefGoogle Scholar
  226. Moulton, S.T., Kosslyn, S.M.: Imagining predictions: mental imagery as mental emulation. Philos. Trans. R. Soc. B: Biol. Sci. 364 (1521), 1273–1280 (2009)CrossRefGoogle Scholar
  227. Moutoussis, M., Fearon, P., El-Deredy, W., Dolan, R.J., Friston, K.J.: Bayesian inferences about the self (and others): a review. Conscious. Cogn. 25, 67–76 (2014)CrossRefGoogle Scholar
  228. Muramatsu, M., Irie, T., Nagatani, T.: Jamming transition in pedestrian counter flow. Phys. A: Stat. Mech. Appl. 267 (3), 487–498 (1999)CrossRefGoogle Scholar
  229. Muramatsu, M., Nagatani, T.: Jamming transition in two-dimensional pedestrian traffic. Phys. A: Stat. Mech. Appl. 275 (1), 281–291 (2000)zbMATHCrossRefGoogle Scholar
  230. Nadin, M.: Variability by another name: “Repetition Without Repetition”. In: Nadin, M. (ed.) Anticipation: Learning from the Past the Russian/Soviet Contributions to the Science of Anticipation, pp. 329–337. Springer, Cham (2015)CrossRefGoogle Scholar
  231. Nagatani, T.: Time-dependent Ginzburg–Landau equation for the jamming transition in traffic flow. Phys. A: Stat. Mech. Appl. 258 (1), 237–242 (1998)CrossRefGoogle Scholar
  232. Nagatani, T.: Jamming transitions and the modified Korteweg–de Vries equation in a two-lane traffic flow. Phys. A: Stat. Mech. Appl. 265 (1), 297–310 (1999)CrossRefGoogle Scholar
  233. Nagatani, T.: The physics of traffic jams. Rep. Prog. Phys. 65 (9), 1331–1386 (2002)ADSCrossRefGoogle Scholar
  234. Nagel, K., Wagner, P., Woesler, R.: Still flowing: approaches to traffic flow and traffic jam modeling. Oper. Res. 51 (5), 681–710 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  235. Nakano, E., Imamizu, H., Osu, R., Uno, Y., Gomi, H., Yoshioka, T., Kawato, M.: Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model. J. Neurophysiol. 81 (5), 2140–2155 (1999)Google Scholar
  236. Newell, G.F.: Nonlinear effects in the dynamics of car following. Oper. Res. 9 (2), 209–229 (1961)zbMATHCrossRefGoogle Scholar
  237. Nicolis, G., Nicolis, C.: Foundation of Complex Systems: Emergence, Information, and Prediction, 2nd edn. World Scientific Publishing Co., Singapore (2013)zbMATHGoogle Scholar
  238. Nishinari, K., Kirchner, A., Namazi, A., Schadschneider, A.: Extended floor field CA model for evacuation dynamics. IEICE Trans. Inf. Syst. E87-D (3), 726–732 (2004)Google Scholar
  239. Noback, C.R., Strominger, N.L., Demarest, R.J., Ruggiero, D.A.: The Human Nervous System: Structure and Function. Humana Press Inc., Totowa (2005)Google Scholar
  240. Nowak, A., Szamrej, J., Latané, B.: From private attitude to public opinion: a dynamic theory of social impact. Psychol. Rev. 97 (3), 362–376 (1990)CrossRefGoogle Scholar
  241. Omelchenko, I., Omel’chenko, O.E., Hövel, P., Schöll, E.: When nonlocal coupling between oscillators becomes stronger: patched synchrony or multichimera states. Phys. Rev. Lett. 110, 224101 (2013)ADSCrossRefGoogle Scholar
  242. Omelchenko, I., Provata, A., Hizanidis, J., Schöll, E., Hövel, P.: Robustness of chimera states for coupled FitzHugh-Nagumo oscillators. Phys. Rev. E 91 (2), 022917 (13 pages) (2015)Google Scholar
  243. Oullier, O., Jantzen, K.J.: Neural indices of behavioral instability in coordination dynamics. In: Fuchs, A., Jirsa, V.K. (eds.) Coordination: Neural, Behavioral and Social Dynamics, pp. 205–227. Springer, Berlin (2008)CrossRefGoogle Scholar
  244. Oullier, O., Kelso, J.A.S.: Social coordination, from the perspective of coordination dynamics. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 8198–8213. Springer Science+Buisiness Media, LLC, New York (2009)CrossRefGoogle Scholar
  245. Pandy, M.G., Garner, B.A., Anderson, F.C.: Optimal control of non-ballistic muscular movements: a constraint-based performance criterion for rising from a chair. J. Biomech. Eng. 117 (1), 15–26 (1995)CrossRefGoogle Scholar
  246. Pasquale, V., Massobrio, P., Bologna, L.L., Chiappalone, M., Martinoia, S.: Self-organization and neuronal avalanches in networks of dissociated cortical neurons. Neuroscience 153 (4), 1354–1369 (2008)CrossRefGoogle Scholar
  247. Pedotti, A., Krishnan, V.V., Stark, L.: Optimization of muscle-force sequencing in human locomotion. Math. Biosci. 38 (1), 57–76 (1978)CrossRefGoogle Scholar
  248. Penny, W.D., Stephan, K.E., Mechelli, A., Friston, K.J.: Modelling functional integration: a comparison of structural equation and dynamic causal models. NeuroImage 23 (Supplement 1), S264–S274 (2004). Mathematics in Brain ImagingGoogle Scholar
  249. Perdikis, D., Huys, R., Jirsa, V.K.: Time scale hierarchies in the functional organization of complex behaviors. PLoS Comput. Biol. 7 (9), e1002198 (18 pages) (2011)Google Scholar
  250. Perdikis, D., Raoul, H., Viktor, J.: Complex processes from dynamical architectures with time-scale hierarchy. PLoS ONE 6 (2), 1–12 (2011)CrossRefGoogle Scholar
  251. Pesenson, M.M.Z. (ed.): Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim (2013)Google Scholar
  252. Peshkov, A., Bertin, E., Ginelli, F., Chaté, H.: Boltzmann-Ginzburg-Landau approach for continuous descriptions of generic Vicsek-like models. Eur. Phys. J. Spec. Top. 223 (7), 1315–1344 (2014)CrossRefGoogle Scholar
  253. Pezzulo, G.: Grounding procedural and declarative knowledge in sensorimotor anticipation. Mind Lang. 26 (1), 78–114 (2011)CrossRefGoogle Scholar
  254. Pezzulo, G.: An active inference view of cognitive control. Front. Psychol. 3 (478), Article 478 (2 pages) (2012)Google Scholar
  255. Pezzulo, G., Castelfranchi, C.: The symbol detachment problem. Cogn. Process. 8 (2), 115–131 (2007)CrossRefGoogle Scholar
  256. Pezzulo, G., Castelfranchi, C.: Thinking as the control of imagination: a conceptual framework for goal-directed systems. Psychol. Res. PRPF 73 (4), 559–577 (2009)CrossRefGoogle Scholar
  257. Pezzulo, G., Rigoli, F., Friston, K.: Active inference, homeostatic regulation and adaptive behavioural control. Prog. Neurobiol. 134, 17–35 (2015)CrossRefGoogle Scholar
  258. Pipes, L.A.: An operational analysis of traffic dynamics. J. Appl. Phys. 24 (3), 274–281 (1953)ADSMathSciNetCrossRefGoogle Scholar
  259. Plenz, D., Niebur, E. (eds.): Criticality in Neural Systems. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim (2014)zbMATHGoogle Scholar
  260. Prigogine, I.: Modération et transformations irréversibles des systèmes ouverts. Académie Royale de Belgique 31 (11), 600–606 (1945)Google Scholar
  261. Prigogine, I., Nicolis, G.: Self Organization in Non-equilibrium Systems. Wiley, New York (1977)zbMATHGoogle Scholar
  262. Rabinovich, M.I., Friston, K.J., Varona, P.: Principles of Brain Dynamics: Global State Interactions. The MIT Press, Cambridge (2012)Google Scholar
  263. Reuschel, A.: Vehicle movements in a platoon. Österreichisches Ingenieur-Archir 4, 193–215 (1950a)zbMATHGoogle Scholar
  264. Reuschel, A.: Vehicle movements in a platoon with uniform acceleration or deceleration of the lead vehicle. Zeitschrift des Österreichischen Ingenieur-und Architekten-Vereines 95, 50–62; 73–77 (1950b)Google Scholar
  265. Riley, M.A., Turvey, M.T.: Variability and determinism in motor behavior. J. Mot. Behav. 34 (2), 99–125 (2002)CrossRefGoogle Scholar
  266. Rolls, E.T., Deco, G.: The Noisy Brain: Stochastic Dynamics as a Principle of Brain Function. Oxford University Press, New York (2010)zbMATHCrossRefGoogle Scholar
  267. Romanczuk, P., Bär, M., Ebeling, W., Lindner, B., Schimansky-Geier, L.: Active Brownian particles: from individual to collective stochastic dynamics. Eur. Phys. J. Spec. Top. 202 (1), 1–162 (2012)CrossRefGoogle Scholar
  268. Root-Bernstein, R.S., Dillon, P.F.: Molecular complementarity I: the complementarity theory of the origin and evolution of life. J. Theor. Biol. 188 (4), 447–479 (1997)CrossRefGoogle Scholar
  269. Schadschneider, A., Kirchner, A., Nishinari, K.: CA approach to collective phenomena in pedestrian dynamics. In: Bandini, S., Chopard, B., Tomassini, M. (eds.) Cellular Automat. Proceedings of 5th International Conference on Cellular Automata for Research and Industry, ACRI 2002 Geneva, 9–11 Oct 2002. Lecture Notes in Computer Science, vol. 2493, pp. 239–248. Springer (2002)Google Scholar
  270. Schelling, T.C.: Dynamic models of segregation. J. Math. Soc. 1 (2), 143–186 (1971)CrossRefGoogle Scholar
  271. Schmidt, R.A.: A schema theory of discrete motor skill learning. Psychol. Rev. 82 (4), 225–260 (1975)CrossRefGoogle Scholar
  272. Schmidt, R.A.: Motor schema theory after 27 years: reflections and implications for a new theory. Res. Q. Exerc. Sport 74 (4), 366–375 (2003)CrossRefGoogle Scholar
  273. Schmidt, R.A., Lee, T.D.: Motor Control and Learning: A Behavioral Emphasis, 5th edn. Human Kinetics, Champaign (2011)Google Scholar
  274. Scholz, J.P., Kelso, J.A.S.: Intentional switching between patterns of bimanual coordination depends on the intrinsic dynamics of the patterns. J. Mot. Behav. 22 (1), 98–124 (1990)CrossRefGoogle Scholar
  275. Scholz, P.J., Schöner, G.: The uncontrolled manifold concept: identifying control variables for a functional task. Exp. Brain Res. 126 (3), 289–306 (1999)CrossRefGoogle Scholar
  276. Schöner, G.: Recent developments and problems in human movement science and their conceptual implications. Ecol. Psychol. 7 (4), 291–314 (1995)CrossRefGoogle Scholar
  277. Schöner, G., Kelso, J.A.S.: A dynamic pattern theory of behavioral change. J. Theor. Biol. 135 (4), 501–524 (1988)MathSciNetCrossRefGoogle Scholar
  278. Schulze, C., Stauffer, D., Wichmann, S.: Birth, survival and death of languages by Monte Carlo simulation. Commun. Comput. Phys. 3 (2), 271–294 (2008)MathSciNetzbMATHGoogle Scholar
  279. Schweitzer, F.: Brownian Agents and Active Particles: Collective Dynamics in the Natural and Social Sciences. Springer, Berlin (2003). With a Foreword by J. Doyne FarmerGoogle Scholar
  280. Schweitzer, F., Hołyst, J.A.: Modelling collective opinion formation by means of active Brownian particles. Eur. Phys. J. B-Condens. Matter Complex Syst. 15 (4), 723–732 (2000)CrossRefGoogle Scholar
  281. Sen, P., Chakrabarti, B.K.: Sociophysics: An Introduction. Oxford University Press, New York (2014)Google Scholar
  282. Seth, A.K., Barrett, A.B., Barnett, L.: Granger causality analysis in neuroscience and neuroimaging. J. Neurosci. 35 (8), 3293–3297 (2015)CrossRefGoogle Scholar
  283. Shajahan, T.K., Sinha, S., Pandit, R.: The mathematical modelling of inhomogeneities in ventricular tissue. In: Dana, S.K., Roy, P.K., Kurths, J. (eds.) Complex Dynamics in Physiological Systems: From Heart to Brain, pp. 51–67. Springer Science+Business Media B.V., Dordrecht (2009)CrossRefGoogle Scholar
  284. Sheets-Johnstone, M.: Preserving integrity against colonization. Phenomenol. Cogn. Sci. 3 (3), 249–261 (2004)CrossRefGoogle Scholar
  285. Sheets-Johnstone, M.: The Primacy of Movement, Expanded 2nd edn.. John Benjamins Publishing Company, Amsterdam (2011)CrossRefGoogle Scholar
  286. Shew, W.L., Yang, H., Petermann, T., Roy, R., Plenz, D.: Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. J. Neurosci. 29 (49), 15595–15600 (2009)CrossRefGoogle Scholar
  287. Shim, J.K., Olafsdottir, H., Zatsiorsky, V.M., Latash, M.L.: The emergence and disappearance of multi-digit synergies during force-production tasks. Exp. Brain Res. 164 (2), 260–270 (2005)CrossRefGoogle Scholar
  288. Sinha, S., Sridhar, S.: Controlling spiral turbulence in simulated cardiac tissue by low-amplitude traveling wave stimulation. In: Dana, S.K. Roy, P.K., Kurths, J. (eds.) Complex Dynamics in Physiological Systems: From Heart to Brain, pp. 69–87. Springer Science+Business Media B.V., Dordrecht (2009)CrossRefGoogle Scholar
  289. Sirotkina, I.E., Biryukova, E.V.: Futurism in physiology: Nikolai Bernstein, anticipation, and kinaesthetic imagination. In: Nadin, M. (ed.) Anticipation: Learning from the Past the Russian/Soviet Contributions to the Science of Anticipation, pp. 269–286. Springer, Cham (2015)CrossRefGoogle Scholar
  290. Slanina, F.: Social processes, physical models of. In: Meyers, R. (ed.) Encyclopedia of Complexity and Systems Science, pp. 8379–8405. Springer Science+Buisiness Media, LLC, New York (2009)CrossRefGoogle Scholar
  291. Solway, A., Botvinick, M.: Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates. Psychol. Rev. 119 (1), 120–154 (2012)CrossRefGoogle Scholar
  292. Stauffer, D.: Opinion dynamics and sociophysics. In: Meyers, R. (ed.) Encyclopedia of Complexity and Systems Science, pp. 6380–6388. Springer Science+Buisiness Media, LLC, New York (2009)CrossRefGoogle Scholar
  293. Stauffer, D.: A biased review of sociophysics. J. Stat. Phys. 151 (1–2), 9–20 (2013)ADSMathSciNetzbMATHCrossRefGoogle Scholar
  294. Stauffer, D., de Oliveira, S.M.M., de Oliveira, P.M.C., Martins, J.S.S.: Biology, Sociology, Geology by Computational Physicists. Elsevier, Amsterdam (2006)zbMATHGoogle Scholar
  295. Stephan, K.E., Penny, W.D., Moran, R.J., den Ouden, H.E.M., Daunizeau, J., Friston, K.J.: Ten simple rules for dynamic causal modeling. NeuroImage 49 (4), 3099–3109 (2010)CrossRefGoogle Scholar
  296. Stephen, D.G., Dixon, J.A.: Strong anticipation: multifractal cascade dynamics modulate scaling in synchronization behaviors. Chaos Solitons Fractals 44 (1), 160–168 (2011)ADSMathSciNetCrossRefGoogle Scholar
  297. Stephen, D.G., Stepp, N., Dixon, J.A., Turvey, M.: Strong anticipation: sensitivity to long-range correlations in synchronization behavior. Phys. A: Stat. Mech. Appl. 387 (21), 5271–5278 (2008)CrossRefGoogle Scholar
  298. Stephens, G.J., de Mesquita, M.B., Ryu, W.S., Bialek, W.: Emergence of long timescales and stereotyped behaviors in Caenorhabditis elegans. Proc. Natl. Acad. Sci. 108 (18), 7286–7289 (2011)ADSCrossRefGoogle Scholar
  299. Stepp, N., Turvey, M.T.: On strong anticipation. Cogn. Syst. Res. 11 (2), 148–164 (2010)CrossRefGoogle Scholar
  300. Sznajd-Weron, K., Sznajd, J.: Opinion evolution in closed community. Int. J. Mod. Phys. C 11 (06), 1157–1165 (2000)ADSzbMATHCrossRefGoogle Scholar
  301. Talis, V.L.: New pages in the biography of Nikolai Alexandrovich Bernstein. In: Nadin, M. (ed.) Anticipation: Learning from the Past the Russian/Soviet Contributions to the Science of Anticipation, pp. 313–328. Springer, Cham (2015)CrossRefGoogle Scholar
  302. Teleology: Encyclopædia Britannica. Encyclopædia Britannica Ultimate Reference Suite (2015)Google Scholar
  303. Todorov, E.: Optimality principles in sensorimotor control. Nat. Neurosci. 7 (9), 907–915 (2004)CrossRefGoogle Scholar
  304. Todorov, E.: Stochastic optimal control and estimation methods adapted to the noise characteristics of the sensorimotor system. Neural Comput. 17 (5), 1084–1108 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  305. Todorov, E., Jordan, M.I.: Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5 (11), 1226–1235 (2002)CrossRefGoogle Scholar
  306. Toner, J., Tu, Y.: Long-range order in a two-dimensional dynamical XY model: how birds fly together. Phys. Rev. Lett. 75 (23), 4326 (1995)ADSCrossRefGoogle Scholar
  307. Toner, J., Tu, Y.: Flocks, herds, and schools: a quantitative theory of flocking. Phys. Rev. E 58 (4), 4828 (1998)ADSMathSciNetCrossRefGoogle Scholar
  308. Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Phys. Rev. E 62, 1805–1824 (2000)ADSzbMATHCrossRefGoogle Scholar
  309. Treiber, M., Kesting, A.: Traffic Flow Dynamics: Data, Models and Simulation. Springer, Berlin (2013)zbMATHCrossRefGoogle Scholar
  310. Uno, Y., Kawato, M., Suzuki, R.: Formation and control of optimal trajectory in human multijoint arm movement. Biol. Cybern. 61 (2), 89–101 (1989)CrossRefGoogle Scholar
  311. Vallacher, R.: Social psychology, applications of complexity to. In: Meyers, R. (ed.) Encyclopedia of Complexity and Systems Science, pp. 8405–8420. Springer Science+Buisiness Media, LLC, New York (2009)Google Scholar
  312. Van der Vaart, E., Hemelrijk, C.K.: ‘Theory of mind’ in animals: ways to make progress. Synthese 191 (3), 335–354 (2014)CrossRefGoogle Scholar
  313. Van der Vaart, E., Verbrugge, R., Hemelrijk, C.K.: Corvid re-caching without ‘Theory of Mind’: a model. PLoS One 7 (3), e32904 (2012)ADSCrossRefGoogle Scholar
  314. Varas, A., Cornejo, M.D., Mainemer, D., Toledo, B., Rogan, J., Muñoz, V., Valdivia, J.A.: Cellular automaton model for evacuation process with obstacles. Phys. A: Stat. Mech. Appl. 382 (2), 631–642 (2007)CrossRefGoogle Scholar
  315. Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., Shochet, O.: Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75 (6), 1226 (1995)ADSMathSciNetCrossRefGoogle Scholar
  316. Vicsek, T., Zafeiris, A.: Collective motion. Phys. Rep. 517 (3–4), 71–140 (2012)ADSCrossRefGoogle Scholar
  317. Walker, H.K.: Deep tendon reflexes. In: Walker, H.K., Hall W.D., Hurst, J.W. (eds.) Clinical Methods: The History, Physical, and Laboratory Examinations, 3rd edn, pp. 365–368. Butterworths, Boston (1990)Google Scholar
  318. Weidlich, W.: Physics and social science: the approach of synergetics. Phys. Rep. 204 (1), 1–163 (1991)ADSMathSciNetzbMATHCrossRefGoogle Scholar
  319. Weidlich, W.: Sociodynamics: A Systematic Approach to Mathematical Modelling in the Social Sciences. Harwood Academic Pubisher, London (2000)zbMATHGoogle Scholar
  320. Weidlich, W.: Sociodynamics—an integrated approach to modelling in the social sciences. In: Dopfer, K. (ed.) Economics, Evolution and the State: The Governance of Complexity, pp. 120–139. Edward Elgar Publishing, Cheltenham (2005)Google Scholar
  321. Wiggins, S.: Introduction to Applied Nonlinear Dynamical Systems and Chaos, 2nd edn. Springer, New York (2003)zbMATHGoogle Scholar
  322. Wolpert, D.M.: Probabilistic models in human sensorimotor control. Hum. Mov. Sci. 26 (4), 511–524 (2007)CrossRefGoogle Scholar
  323. Wolpert, D.M., Doya, K., Kawato, M.: A unifying computational framework for motor control and social interaction. Philos. Trans. R. Soc. Lond. B Biol. Sci. 358 (1431), 593–602 (2003)CrossRefGoogle Scholar
  324. Wolpert, D.M., Ghahramani, Z.: Computational principles of movement neuroscience. Nat. Neurosci. 3, 1212–1217 (2000)CrossRefGoogle Scholar
  325. Wolpert, D.M., Kawato, M.: Multiple paired forward and inverse models for motor control. Neural Netw. 11 (7), 1317–1329 (1998)CrossRefGoogle Scholar
  326. Yang, H., Shew, W.L., Roy, R., Plenz, D.: Peak variability and optimal performance in cortical networks at criticality. In: Plenz, D., Niebur, E. (eds.) Criticality in Neural Systems, pp. 335–346. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim (2014)CrossRefGoogle Scholar
  327. Yates, C.A., Baker, R.E., Erban, R., Maini, P.K.: Refining self-propelled particle models for collective behaviour. Can. Appl. Math. Q. 18 (3), 299–350 (2010)MathSciNetzbMATHGoogle Scholar
  328. Yuille, A., Kersten, D.: Vision as Bayesian inference: analysis by synthesis? Trends Cogn. Sci. 10 (7), 301–308 (2006)CrossRefGoogle Scholar
  329. Zaal, F.T.J.M., Bootsma, R.J.: The use of time-to-contact information for the initiation of hand closure in natural prehension. In: Hecht, H., Savelsburgh, G.J.P. (eds.) Time-to-Contact, pp. 389–420. Elsevier, Amsterdam (2004)CrossRefGoogle Scholar
  330. Zanone, P.-G., Kostrubiec, V.: Searching for (dynamic) principles of learning. In: Jirsa, V.K., Kelso, J.A.S. (eds.) Coordination Dynamics: Issues and Trends, pp. 57–89. Springer, Berlin (2004)CrossRefGoogle Scholar
  331. Zanone, P.G., Kostrubiec, V., Albaret, J.M., Temprado, J.-J.: Covariation of attentional cost and stability provides further evidence for two routes to learning new coordination patterns. Acta Psychol. 133 (2), 107–118 (2010a)CrossRefGoogle Scholar
  332. Zanone, P.G., Kostrubiec, V., Albaret, J.M., Temprado, J.J.: Covariation of attentional cost and stability provides further evidence for two routes to learning new coordination patterns. Acta Psychol. 133 (2), 107–118 (2010b)CrossRefGoogle Scholar
  333. Zanone, P.G., Monno, A., Temprado, J.-J., Laurent, M.: Shared dynamics of attentional cost and pattern stability. Hum. Mov. Sci. 20 (6), 765–789 (2001)CrossRefGoogle Scholar
  334. Zhou, T., Wu, Y.-H., Bartsch, A., Cuadra, C., Zatsiorsky, V.M., Latash, M.L.: Anticipatory synergy adjustments: preparing a quick action in an unknown direction. Exp. Brain Res. 226 (4), 565–573 (2013)CrossRefGoogle Scholar
  335. Ziman, J.M.: The general variational principle of transport theory. Can. J. Phys. 34 (12A), 1256–1273 (1956)ADSMathSciNetzbMATHCrossRefGoogle Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  • Ihor Lubashevsky
    • 1
  1. 1.Dept. of Computer Science & EngineeringUniversity of AizuAizu-WakamatsuJapan

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