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Modeling of Human Behavior Within the Paradigm of Modern Physics

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Part of the book series: Understanding Complex Systems ((UCS))

Abstract

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.

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Notes

  1. 1.

    The original voter model used a power-law ansatz in stead of the exponential one (6.21).

  2. 2.

    Teleology (from Greek telos “end” and logos, “reason”) is the approach to explaining observed phenomena by reference to some purpose or end; also described as final causality, in contrast with explanation by efficient causes only (Teleology 2015), for details a reader may be referred to Macintyre (2006).

  3. 3.

    By definition, a functional is a relation mapping functions—in our case possible trajectories of body movement—onto numbers, real or complex.

  4. 4.

    In physics, jerk or jolt is the rate of time variations in acceleration or, what is the same, the time derivative of acceleration, or the second derivative of velocity with respect to time, or the third derivative of position.

  5. 5.

    Alpha motorneurons (also lower motorneurons) convey impulses to skeletal muscles, innervate them causing the muscle contractions, and thereby generate body movement (e.g., Noback et al. 2005)

  6. 6.

    The stretch reflex is a muscle contraction in response to stretching the muscle, which plays a crucial role in automatic regulation of skeletal muscle length. In simplified form its mechanics may be conceived of as follows. When a muscle lengthens, the muscle spindle—sensory receptor located in it—is stretched and its nerve activity increases, causing the muscle fibers to contract and thus resist the stretching (e.g., Walker 1990).

References

  • Abrams, D.M., Strogatz, S.H.: Chimera states for coupled oscillators. Phys. Rev. Lett. 93, 174102 (2004)

    Article  ADS  Google Scholar 

  • Anderson, F.C., Pandy, M.G.: Dynamic optimization of human walking. J. Biomech. Eng. 123 (5), 381–390 (2001)

    Article  Google Scholar 

  • 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 

  • Arenas, A., Díaz-Guilera, A., Kurths, J., Moreno, Y., Zhou, C.: Synchronization in complex networks. Phys. Rep. 469 (3), 93–153 (2008)

    Article  ADS  MathSciNet  Google Scholar 

  • Aruin, A.S.: The effect of changes in the body configuration on anticipatory postural adjustments. Mot. Control 7 (3), 264–277 (2003)

    Article  Google Scholar 

  • 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 

  • Ashby, W.: Principles of the self-organizing dynamic system. J. Gen. Psychol. 37 (2), 125–128 (1947)

    Article  Google Scholar 

  • Axelrod, R.: The dissemination of culture: a model with local convergence and global polarization. J. Confl. Resolut. 41 (2), 203–226 (1997)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Babiloni, F., Astolfi, L.: Social neuroscience and hyperscanning techniques: past, present and future. Neurosci. Biobehav. Rev. 44, 76–93 (2014)

    Article  Google Scholar 

  • Bak, P.: How Nature Works: The Science of Self-Organised Criticality. Copernicus/Springer, New York (1996)

    Book  MATH  Google Scholar 

  • Balanov, A., Janson, N., Postnov, D., Sosnovtseva, O.: Synchronization: From Simple to Complex. Springer, Berlin (2009)

    MATH  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Bando, M., Hasebe, K., Nakanishi, K., Nakayama, A.: Analysis of optimal velocity model with explicit delay. Phys. Rev. E 58, 5429–5435 (1998)

    Article  ADS  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • Barab, P.: The Complementary Nature of Reality. Open Way Press, Portland (2010)

    Google Scholar 

  • Barrat, A., Barthelemy, M., Vespignani, A.: Dynamical Processes on Complex Networks. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  MathSciNet  MATH  Google Scholar 

  • 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)

    Article  MathSciNet  MATH  Google Scholar 

  • 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 

  • 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)

    Chapter  Google Scholar 

  • Bernstein, N.A.: The problem of interrelation between coordination and localization. Arch. Biol. Sci. 38, 1–35. (1935, in Russian)

    Google Scholar 

  • Bernstein, N.A.: Urgent problems of the physiology of activity. Probl. Cybern. 6, 101–160 (1961, in Russian)

    Google Scholar 

  • Bernstein, N.A.: Essays on the Physiology of Movements and Physiology of Activity. Meditsina, Moscow (1966, in Russian).

    Google Scholar 

  • Bernstein, N.A.: The Co-ordination and Regulation of Movements. Pergamon Press, Oxford (1967)

    Google Scholar 

  • 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)

    Article  ADS  MATH  Google Scholar 

  • 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 

  • Blue, V., Adler, J.: Cellular automata microsimulation of bidirectional pedestrian flows. Transp. Res. Rec.: J. Transp. Res. Board 1678, 135–141 (1999)

    Google Scholar 

  • Bordogna, C.M., Albano, E.V.: Dynamic behavior of a social model for opinion formation. Phys. Rev. E 76 (6), 061125 (2007a)

    Article  ADS  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • 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 Brain

    Google Scholar 

  • Breakspear, M.: “Dynamic” connectivity in neural systems. Neuroinformatics 2 (2), 205–224 (2004)

    Article  Google Scholar 

  • Breakspear, M., Jirsa, V.K.: Neuronal dynamics and brain connectivity. In: Handbook of Brain Connectivity, pp. 3–64. Springer, Berlin (2007)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • Bressler, S.L., Seth, A.K.: Wiener–Granger causality: a well established methodology. NeuroImage 58 (2), 323–329 (2011)

    Article  Google Scholar 

  • Bressloff, P.C.: Spatiotemporal dynamics of continuum neural fields. J. Phys. A: Math. Theor. 45 (3), 033001 (2012)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • 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)

    Article  ADS  MATH  Google Scholar 

  • 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 

  • Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. Princeton University Press, Princeton (2001)

    MATH  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Cangelosi, A., Parisi, D. (eds): Simulating the Evolution of Language. Springer, London (2002)

    MATH  Google Scholar 

  • 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)

    Article  MathSciNet  MATH  Google Scholar 

  • 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)

    Article  MathSciNet  MATH  Google Scholar 

  • Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Rev. Mod. Phys. 81 (2), 591–646 (2009)

    Article  ADS  Google Scholar 

  • Castellano, C., Marsili, M., Vespignani, A.: Nonequilibrium phase transition in a model for social influence. Phys. Rev. Lett. 85 (16), 3536 (2000)

    Article  ADS  Google Scholar 

  • Chakrabarti, B., Chakraborti, A., Chatterjee, A.: Econophysics and Sociophysics: Trends and Perspectives. Wiley-VCH Verlag GmbH & Co. KGaA, Weinhaim (2006)

    Book  Google Scholar 

  • Chater, N., Tenenbaum, J.B., Yuille, A.: Probabilistic models of cognition: conceptual foundations. Trends Cogn. Sci. 10 (7), 287–291 (2006)

    Article  Google Scholar 

  • Chialvo, D.R.: Emergent complex neural dynamics. Nat. Phys. 6 (10), 744–750 (2010)

    Article  Google Scholar 

  • Chicharro, D., Ledberg, A.: When two become one: the limits of causality analysis of brain dynamics. PLoS ONE 7 (3), 1–16 (2012)

    Article  Google Scholar 

  • Chowdhury, D., Santen, L., Schadschneider, A.: Statistical physics of vehicular traffic and some related systems. Phys. Rep. 329 (4–6), 199–329 (2000)

    Article  ADS  MathSciNet  Google Scholar 

  • Cialdini, R.B., Goldstein, N.J.: Social influence: compliance and conformity. Annu. Rev. Psychol. 55, 591–621 (2004)

    Article  Google Scholar 

  • Clark, A.: Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36 (03), 181–204 (2013)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Culicover, P.W., Nowak, A.: Dynamical Grammar: Minimalism, Acquisition, and Change. Oxford University Press, Oxford (2003)

    Google Scholar 

  • Czirók, A., Vicsek, M., Vicsek, T.: Collective motion of organisms in three dimensions. Phys. A: Stat. Mech. Appl. 264 (1), 299–304 (1999)

    Article  MATH  Google Scholar 

  • Czirók, A., Vicsek, T.: Collective behavior of interacting self-propelled particles. Phys. A: Stat. Mech. Appl. 281 (1), 17–29 (2000)

    Article  Google Scholar 

  • 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)

    MATH  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Davidson, P.A.: Turbulence: An Introduction for Scientists and Engineers, 2nd edn. Oxford University Press, Oxford (2015)

    Book  MATH  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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)

    Article  MathSciNet  MATH  Google Scholar 

  • 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 

  • Demšar, J., Hemelrijk, C.K., Hildenbrandt, H., Bajec, I.L.: Simulating predator attacks on schools: evolving composite tactics. Ecol. Model. 304, 22–33 (2015)

    Article  Google Scholar 

  • Devaney, R.L.: An Introduction to Chaotic Dynamical Systems, 2nd edn. Westview Press, Boulder (2003)

    MATH  Google Scholar 

  • Diedrichsen, J., Shadmehr, R., Ivry, R.B.: The coordination of movement: optimal feedback control and beyond. Trends Cogn. Sci. 14 (1), 31–39 (2010)

    Article  Google Scholar 

  • Dietmar, P., Thiagarajan, T.C.: The organizing principles of neuronal avalanches: cell assemblies in the cortex? Trends Neurosci. 30 (3), 101–110 (2007)

    Article  Google Scholar 

  • Dörfler, F., Bullo, F.: Synchronization in complex networks of phase oscillators: a survey. Automatica 50 (6), 1539–1564 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  • Dubois, D.M.: Incursive and hyperincursive systems, fractal machine and anticipatory logic. AIP Conf. Proc. 573, 437–451 (2001)

    Article  ADS  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Edelman, G.M.: Bright Air, Brilliant Fire: On the Matter of the Mind. BasicBooks, New York (1992)

    Google Scholar 

  • Edelman, G.M.: Wider Than the Sky: The Phenomenal Gift of Consciousness. Yale University Press, London (2004)

    Google Scholar 

  • Edelman, G.M.: Second Nature: Brain Science and Human Knowledge. Yale University Press, London (2006)

    Google Scholar 

  • Edelman, G.M., Gally, J.A.: Degeneracy and complexity in biological systems. Proc. Natl. Acad. Sci. 98 (24), 13763–13768 (2001)

    Article  ADS  Google Scholar 

  • Edelman, G.M., Tononi, G.: A Universe Of Consciousness: How Matter Becomes Imagination. Basic Books, New York (2000)

    Google Scholar 

  • Elliott, D., Smith, D.: Football stadia disasters in the United Kingdom: learning from tragedy? Organ. Environ. 7 (3), 205–229 (1993)

    Article  Google Scholar 

  • 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 

  • 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 

  • 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)

    Chapter  Google Scholar 

  • Feistel, R., Ebeling, W.: Physics of Self-Organization and Evolution. Wiley-VCH Verlag & Co. KGaA, Weinheim (2011)

    Book  MATH  Google Scholar 

  • 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 

  • Feldman, A.G.: Once more on the equilibrium-point hypothesis (λ model) for motor control. J. Mot. Behav. 18 (1), 17–54 (1986)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Feldman, A.G.: Space and time in the context of equilibrium-point theory. Wiley Interdiscip. Rev.: Cogn. Sci. 2 (3), 287–304 (2011)

    Article  MathSciNet  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Flash, T., Hogan, N.: The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci. 5 (7), 1688–1703 (1985)

    Google Scholar 

  • Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11 (2), 127–138 (2010a)

    Article  Google Scholar 

  • Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11 (2), 127–138 (2010b)

    Article  Google Scholar 

  • Friston, K.: What is optimal about motor control? Neuron 72 (3), 488–498 (2011)

    Article  Google Scholar 

  • Friston, K., Ao, P.: Free energy, value, and attractors. Comput. Math. Methods Med. 2012, Article 937860 (27 pages) (2012)

    Google Scholar 

  • Friston, K.J., Daunizeau, J., Kiebel, S.J.: Reinforcement learning or active inference? PloS ONE 4 (7), e6421 (2009)

    Article  ADS  Google Scholar 

  • Friston, K.J., Daunizeau, J., Kilner, J., Kiebel, S.J.: Action and behavior: a free-energy formulation. Biol. Cybern. 102 (3), 227–260 (2010)

    Article  Google Scholar 

  • Friston, K.J., Harrison, L., Penny, W.: Dynamic causal modelling. NeuroImage 19 (4), 1273–1302 (2003)

    Article  Google Scholar 

  • Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., Pezzulo, G.: Active inference and epistemic value. Cogn. Neurosci. 6 (4), 187–214 (2015)

    Article  Google Scholar 

  • 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 

  • 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)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Fukui, M., Ishibashi, Y.: Self-organized phase transitions in cellular automaton models for pedestrians. J. Phys. Soc. Jpn. 68 (8), 2861–2863 (1999)

    Article  ADS  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • Galam, S.: Sociophysics: a review of Galam models. Int. J. Mod. Phys. C 19 (03), 409–440 (2008)

    Article  ADS  MATH  Google Scholar 

  • Galam, S.: Sociophysics: A Physicist’s Modeling of Psycho-Political Phenomena. Springer, New York (2012)

    Book  Google Scholar 

  • Gardiner, C.: Stochastic Methods: A Handbook for the Natural and Social Sciences, 4th edn. Springer, Berlin (2009)

    MATH  Google Scholar 

  • Gazis, D.C., Herman, R., Rothery, R.W.: Nonlinear follow-the-leader models of traffic flow. Oper. Res. 9 (4), 545–567 (1961)

    Article  MathSciNet  MATH  Google Scholar 

  • Gelfand, I.M., Latash, M.L.: On the problem of adequate language in motor control. Mot. Control 2 (4), 306–313 (1998)

    Article  Google Scholar 

  • 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)

    Book  Google Scholar 

  • Gipps, P.G., Marksjö, B.: A micro-simulation model for pedestrian flows. Math. Comput. Simul. 27 (2), 95–105 (1985)

    Article  Google Scholar 

  • Glansdorff, P., Prigogine, I.: Thermodynamic Theory of Structure, Stability and Fluctuations. Wiley, London (1971)

    MATH  Google Scholar 

  • Goldman, A.I.: Simulating Minds: The Philosophy, Psychology, and Neuroscience of Mindreading. Oxford University Press, Oxford (2006)

    Book  Google Scholar 

  • Grush, R.: The emulation theory of representation: motor control, imagery, and perception. Behav. Brain Sci. 27 (3), 377–396 (2004)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • Guigon, E., Baraduc, P., Desmurget, M.: Computational motor control: redundancy and invariance. J. Neurophys. 97 (1), 331–347 (2007b)

    Article  Google Scholar 

  • Guigon, E., Baraduc, P., Desmurget, M.: Computational motor control: feedback and accuracy. Eur. J. Neurosci. 27 (4), 1003–1016 (2008)

    Article  Google Scholar 

  • Ha, S.-Y., Tadmor, E.: From particle to kinetic and hydrodynamic descriptions of flocking. Kinet. Relat. Mod. 1 (3), 415–435 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  • Haken, H.: Information and Self-Organization: A Macroscopic Approach to Complex Systems, 3rd edn. Springer, Berlin (2006)

    MATH  Google Scholar 

  • Haken, H.: Brain Dynamics: An Introduction to Models and Simulations, 2nd edn. Springer, Berlin (2008a)

    MATH  Google Scholar 

  • Haken, H.: Self-organization. Scholarpedia 3 (8), 1401 (2008b). Revision #137295

    Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Article  MathSciNet  MATH  Google Scholar 

  • Harris, C.M., Wolpert, D.M.: Signal-dependent noise determines motor planning. Nature 394 (6695), 780–784 (1998)

    Article  ADS  Google Scholar 

  • Hatze, H., Buys, J.D.: Energy-optimal controls in the Mammalian neuromuscular system. Biol. Cybern. 27 (1), 9–20 (1977)

    Article  MATH  Google Scholar 

  • 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 

  • Helbing, D.: A mathematical model for the behavior of pedestrians. Behav. Sci. 36 (4), 298–310 (1991)

    Article  Google Scholar 

  • Helbing, D.: A mathematical model for the behavior of individuals in a social field. J. Math. Soc. 19 (3), 189–219 (1994)

    Article  Google Scholar 

  • Helbing, D.: Traffic and related self-driven many-particle systems. Rev. Mod. Phys. 73, 1067–1141 (2001)

    Article  ADS  Google Scholar 

  • Helbing, D.: Quantitative Sociodynamics Stochastic Methods and Models of Social Interaction Processes. Springer, Berlin (2010)

    MATH  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Helbing, D. (ed.): Social Self-Organization: Agent-Based Simulations and Experiments to Study Emergent Social Behavior. Springer, Berlin (2012)

    Google Scholar 

  • Helbing, D., Farkas, I.J., Vicsek, T.: Freezing by heating in a driven mesoscopic system. Phys. Rev. Lett. 84 (6), 1240 (2000a)

    Article  ADS  MATH  Google Scholar 

  • Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407 (6803), 487–490 (2000b)

    Article  ADS  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Helbing, D., Johansson, A., Al-Abideen, H.Z.: Dynamics of crowd disasters: an empirical study. Phys. Rev. E 75 (4), 046109 (2007)

    Article  ADS  Google Scholar 

  • Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51 (5), 4282 (1995)

    Article  ADS  Google Scholar 

  • Helbing, D., Molnár, P., Farkas, I. J., Bolay, K.: Self-organizing pedestrian movement. Environ. Plann. B: Plann. Des. 28 (3), 361–383 (2001)

    Article  Google Scholar 

  • 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 

  • Hesslow, G.: Conscious thought as simulation of behaviour and perception. Trends Cogn. Sci. 6 (6), 242–247 (2002)

    Article  Google Scholar 

  • Hildenbrandt, H., Carere, C., Hemelrijk, C.K.: Self-organized aerial displays of thousands of starlings: a model. Behav. Ecol. 21 (6), 1349–1359 (2010)

    Article  Google Scholar 

  • Hilgetag, C.C., Kaiser, M.: Clustered organization of cortical connectivity. Neuroinformatics 2 (3), 353–360 (2004)

    Article  Google Scholar 

  • 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)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • Hogan, N.: An organizing principle for a class of voluntary movements. J. Neurosci. 4 (11), 2745–2754 (1984)

    Google Scholar 

  • 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 

  • 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)

    Article  MATH  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Huys, R., Jirsa, V.K. (eds.): Nonlinear Dynamics in Human Behavior. Springer, Berlin (2010)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • Hwang, E.J., Shadmehr, R.: Internal models of limb dynamics and the encoding of limb state. J. Neural Eng. 2 (3), S266–S278 (2005)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Izhikevich, E.M.: Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. The MIT Press, Cambridge (2007)

    Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Jeannerod, M.: Motor Cognition: What Actions Tell the Self. Oxford University Press, Oxford (2006)

    Book  Google Scholar 

  • 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 

  • Jirsa, V.K., McIntosh, A. (eds.): Handbook of Brain Connectivity. Springer, Berlin (2007)

    MATH  Google Scholar 

  • 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 

  • 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)

    Chapter  Google Scholar 

  • Kawato, M.: Internal models for motor control and trajectory planning. Curr. Opin. Neurobiol. 9 (6), 718–727 (1999)

    Article  Google Scholar 

  • Kelso, J.A.S.: Dynamic Patterns: The Self-Organization of Brain and Behavior. The MIT Press, Cambridge (1995)

    Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Kelso, J.A.S.: Instabilities and phase transitions in human brain and behavior. Front. Hum. Neurosci. 4, Article 23 (2 pages) (2010)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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 

  • Kelso, J.A.S., Engstrøm, D.A.: The Complementary Nature. The MIT Press, Cambridge (2006)

    Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • Kerner, B.: The Physics of Traffic: Empirical Freeway Pattern Features, Engineering Applications, and Theory. Springer, Berlin (2004)

    Book  Google Scholar 

  • Kerner, B.S.: Introduction to Modern Traffic Flow Theory and Control: The Long Road to Three-Phase Traffic Theory. Springer, Berlin (2009a)

    Book  MATH  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Kerner, B.S., Klenov, S.L.: A microscopic model for phase transitions in traffic flow. J. Phys. A: Math. Gen. 35 (3), L31 (2002)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • Kerner, B.S., Klenov, S.L.: Deterministic microscopic three-phase traffic flow models. J. Phys. A: Math. Gen. 39 (8), 1775 (2006)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • Kersten, D., Yuille, A.: Bayesian models of object perception. Curr. Opin. Neurobiol. 13 (2), 150–158 (2003)

    Article  Google Scholar 

  • 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)

    Article  MATH  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Book  MATH  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Kohring, G.A.: Ising models of social impact: the role of cumulative advantage. Journal de Physique I France 6 (2), 301–308 (1996)

    Article  ADS  Google Scholar 

  • Körding, K.P., Wolpert, D.M.: Bayesian integration in sensorimotor learning. Nature 427 (6971), 244–247 (2004)

    Article  ADS  Google Scholar 

  • Körding, K.P., Wolpert, D.M.: Bayesian decision theory in sensorimotor control. Trends Cogn. Sci. 10 (7), 319–326 (2006)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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 

  • Krause, J., Ruxton, G.D., Krause, S.: Swarm intelligence in animals and humans. Trends Ecol. Evol. 25 (1), 28–34 (2010)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Kröger, B.: Hermann Haken: From the Laser to Synergetics: A Scientific Biography of the Early Years. Springer, Heidelberg (2015)

    MATH  Google Scholar 

  • Latané, B.: The psychology of social impact. Am. Psychol. 36 (4), 343–356 (1981)

    Article  Google Scholar 

  • Latané, B.: Dynamic social impact: the creation of culture by communication. J. Commun. 46 (4), 13–25 (1996)

    Article  Google Scholar 

  • 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 

  • 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 

  • Latash, M.L.: Neurophysiological Basis of Movement, 2nd edn. Human Kinetics, Urbana (2008a)

    Google Scholar 

  • Latash, M.L.: Synergy. Oxford University Press, Oxford (2008b)

    Book  Google Scholar 

  • Latash, M.L.: Motor synergies and the equilibrium-point hypothesis. Mot. Control 14 (3), 294–322 (2010)

    Article  MathSciNet  Google Scholar 

  • Latash, M.L.: The bliss (not the problem) of motor abundance (not redundancy). Exp. Brain Res. 217 (1), 1–5 (2012)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Latash, M.L., Scholz, J.P., Schöner, G.: Toward a new theory of motor synergies. Mot. Control 11 (3), 276–308 (2007)

    Article  Google Scholar 

  • 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)

    Article  MATH  Google Scholar 

  • Lee, T.D., Blandin, Y., Proteau, L.: Effects of task instructions and oscillation frequency on bimanual coordination. Psychol. Res. 59 (2), 100–106 (1996)

    Article  Google Scholar 

  • Lewin, K., Cartwright, D. (eds.): Field Theory in Social Science: Selected Theoretical Papers. Harpers, Oxford (1951)

    Google Scholar 

  • Liggett, T.M.: Interacting Particle Systems. Springer, New York (1985)

    Book  MATH  Google Scholar 

  • Loreto, V., Baronchelli, A., Mukherjee, A., Puglisi, A., Tria, F.: Statistical physics of language dynamics. J. Stat. Mech: Theory Exp. 2011 (04), P04006 (2011)

    Article  Google Scholar 

  • Macintyre, A.: The Tasks of Philosophy: Selected Essays, vol. 1. Cambridge University Press, Cambridge (2006)

    Book  Google Scholar 

  • Marreiros, A.C., Stephan, K.E., Friston, K.J.: Dynamic causal modeling. Scholarpedia 5 (7), 9568 (2010). Revision #91214

    Google Scholar 

  • 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)

    Article  MathSciNet  MATH  Google Scholar 

  • 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 

  • Martin, V., Scholz, J.P., Schöner, G.: Redundancy, self-motion, and motor control. Neural Comput. 21 (5), 1371–1414 (2009)

    Article  MATH  Google Scholar 

  • Martyushev, L.M., Seleznev, V.D.: Maximum entropy production principle in physics, chemistry and biology. Phys. Rep. 426 (1), 1–45 (2006)

    Article  ADS  MathSciNet  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • McIntyre, J., Bizzi, E.: Servo hypotheses for the biological control of movement. J. Mot. Behav. 25 (3), 193–202 (1993)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • 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 

  • 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 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • Mitra, S., Riley, M.A., Turvey, M.T.: Chaos in human rhythmic movement. J. Mot. Behav. 29 (3), 195–198 (1997)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Muramatsu, M., Irie, T., Nagatani, T.: Jamming transition in pedestrian counter flow. Phys. A: Stat. Mech. Appl. 267 (3), 487–498 (1999)

    Article  Google Scholar 

  • Muramatsu, M., Nagatani, T.: Jamming transition in two-dimensional pedestrian traffic. Phys. A: Stat. Mech. Appl. 275 (1), 281–291 (2000)

    Article  MATH  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Nagatani, T.: Time-dependent Ginzburg–Landau equation for the jamming transition in traffic flow. Phys. A: Stat. Mech. Appl. 258 (1), 237–242 (1998)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Nagatani, T.: The physics of traffic jams. Rep. Prog. Phys. 65 (9), 1331–1386 (2002)

    Article  ADS  Google Scholar 

  • Nagel, K., Wagner, P., Woesler, R.: Still flowing: approaches to traffic flow and traffic jam modeling. Oper. Res. 51 (5), 681–710 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  • 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 

  • Newell, G.F.: Nonlinear effects in the dynamics of car following. Oper. Res. 9 (2), 209–229 (1961)

    Article  MATH  Google Scholar 

  • Nicolis, G., Nicolis, C.: Foundation of Complex Systems: Emergence, Information, and Prediction, 2nd edn. World Scientific Publishing Co., Singapore (2013)

    MATH  Google Scholar 

  • 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 

  • 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 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • 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 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Pedotti, A., Krishnan, V.V., Stark, L.: Optimization of muscle-force sequencing in human locomotion. Math. Biosci. 38 (1), 57–76 (1978)

    Article  Google Scholar 

  • 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 Imaging

    Google Scholar 

  • 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 

  • Perdikis, D., Raoul, H., Viktor, J.: Complex processes from dynamical architectures with time-scale hierarchy. PLoS ONE 6 (2), 1–12 (2011)

    Article  Google Scholar 

  • 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 

  • 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)

    Article  Google Scholar 

  • Pezzulo, G.: Grounding procedural and declarative knowledge in sensorimotor anticipation. Mind Lang. 26 (1), 78–114 (2011)

    Article  Google Scholar 

  • Pezzulo, G.: An active inference view of cognitive control. Front. Psychol. 3 (478), Article 478 (2 pages) (2012)

    Google Scholar 

  • Pezzulo, G., Castelfranchi, C.: The symbol detachment problem. Cogn. Process. 8 (2), 115–131 (2007)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Pezzulo, G., Rigoli, F., Friston, K.: Active inference, homeostatic regulation and adaptive behavioural control. Prog. Neurobiol. 134, 17–35 (2015)

    Article  Google Scholar 

  • Pipes, L.A.: An operational analysis of traffic dynamics. J. Appl. Phys. 24 (3), 274–281 (1953)

    Article  ADS  MathSciNet  Google Scholar 

  • Plenz, D., Niebur, E. (eds.): Criticality in Neural Systems. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim (2014)

    MATH  Google Scholar 

  • Prigogine, I.: Modération et transformations irréversibles des systèmes ouverts. Académie Royale de Belgique 31 (11), 600–606 (1945)

    Google Scholar 

  • Prigogine, I., Nicolis, G.: Self Organization in Non-equilibrium Systems. Wiley, New York (1977)

    MATH  Google Scholar 

  • Rabinovich, M.I., Friston, K.J., Varona, P.: Principles of Brain Dynamics: Global State Interactions. The MIT Press, Cambridge (2012)

    Google Scholar 

  • Reuschel, A.: Vehicle movements in a platoon. Österreichisches Ingenieur-Archir 4, 193–215 (1950a)

    MATH  Google Scholar 

  • 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 

  • Riley, M.A., Turvey, M.T.: Variability and determinism in motor behavior. J. Mot. Behav. 34 (2), 99–125 (2002)

    Article  Google Scholar 

  • Rolls, E.T., Deco, G.: The Noisy Brain: Stochastic Dynamics as a Principle of Brain Function. Oxford University Press, New York (2010)

    Book  MATH  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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 

  • Schelling, T.C.: Dynamic models of segregation. J. Math. Soc. 1 (2), 143–186 (1971)

    Article  Google Scholar 

  • Schmidt, R.A.: A schema theory of discrete motor skill learning. Psychol. Rev. 82 (4), 225–260 (1975)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Schmidt, R.A., Lee, T.D.: Motor Control and Learning: A Behavioral Emphasis, 5th edn. Human Kinetics, Champaign (2011)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • Scholz, P.J., Schöner, G.: The uncontrolled manifold concept: identifying control variables for a functional task. Exp. Brain Res. 126 (3), 289–306 (1999)

    Article  Google Scholar 

  • Schöner, G.: Recent developments and problems in human movement science and their conceptual implications. Ecol. Psychol. 7 (4), 291–314 (1995)

    Article  Google Scholar 

  • Schöner, G., Kelso, J.A.S.: A dynamic pattern theory of behavioral change. J. Theor. Biol. 135 (4), 501–524 (1988)

    Article  MathSciNet  Google Scholar 

  • Schulze, C., Stauffer, D., Wichmann, S.: Birth, survival and death of languages by Monte Carlo simulation. Commun. Comput. Phys. 3 (2), 271–294 (2008)

    MathSciNet  MATH  Google Scholar 

  • Schweitzer, F.: Brownian Agents and Active Particles: Collective Dynamics in the Natural and Social Sciences. Springer, Berlin (2003). With a Foreword by J. Doyne Farmer

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • Sen, P., Chakrabarti, B.K.: Sociophysics: An Introduction. Oxford University Press, New York (2014)

    Google Scholar 

  • Seth, A.K., Barrett, A.B., Barnett, L.: Granger causality analysis in neuroscience and neuroimaging. J. Neurosci. 35 (8), 3293–3297 (2015)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Sheets-Johnstone, M.: Preserving integrity against colonization. Phenomenol. Cogn. Sci. 3 (3), 249–261 (2004)

    Article  Google Scholar 

  • Sheets-Johnstone, M.: The Primacy of Movement, Expanded 2nd edn.. John Benjamins Publishing Company, Amsterdam (2011)

    Book  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Stauffer, D.: A biased review of sociophysics. J. Stat. Phys. 151 (1–2), 9–20 (2013)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • 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)

    MATH  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Stephen, D.G., Dixon, J.A.: Strong anticipation: multifractal cascade dynamics modulate scaling in synchronization behaviors. Chaos Solitons Fractals 44 (1), 160–168 (2011)

    Article  ADS  MathSciNet  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • Stepp, N., Turvey, M.T.: On strong anticipation. Cogn. Syst. Res. 11 (2), 148–164 (2010)

    Article  Google Scholar 

  • Sznajd-Weron, K., Sznajd, J.: Opinion evolution in closed community. Int. J. Mod. Phys. C 11 (06), 1157–1165 (2000)

    Article  ADS  MATH  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Teleology: Encyclopædia Britannica. Encyclopædia Britannica Ultimate Reference Suite (2015)

    Google Scholar 

  • Todorov, E.: Optimality principles in sensorimotor control. Nat. Neurosci. 7 (9), 907–915 (2004)

    Article  Google Scholar 

  • Todorov, E.: Stochastic optimal control and estimation methods adapted to the noise characteristics of the sensorimotor system. Neural Comput. 17 (5), 1084–1108 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  • Todorov, E., Jordan, M.I.: Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5 (11), 1226–1235 (2002)

    Article  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • Toner, J., Tu, Y.: Flocks, herds, and schools: a quantitative theory of flocking. Phys. Rev. E 58 (4), 4828 (1998)

    Article  ADS  MathSciNet  Google Scholar 

  • Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Phys. Rev. E 62, 1805–1824 (2000)

    Article  ADS  MATH  Google Scholar 

  • Treiber, M., Kesting, A.: Traffic Flow Dynamics: Data, Models and Simulation. Springer, Berlin (2013)

    Book  MATH  Google Scholar 

  • Uno, Y., Kawato, M., Suzuki, R.: Formation and control of optimal trajectory in human multijoint arm movement. Biol. Cybern. 61 (2), 89–101 (1989)

    Article  Google Scholar 

  • 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 

  • Van der Vaart, E., Hemelrijk, C.K.: ‘Theory of mind’ in animals: ways to make progress. Synthese 191 (3), 335–354 (2014)

    Article  Google Scholar 

  • Van der Vaart, E., Verbrugge, R., Hemelrijk, C.K.: Corvid re-caching without ‘Theory of Mind’: a model. PLoS One 7 (3), e32904 (2012)

    Article  ADS  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  ADS  MathSciNet  Google Scholar 

  • Vicsek, T., Zafeiris, A.: Collective motion. Phys. Rep. 517 (3–4), 71–140 (2012)

    Article  ADS  Google Scholar 

  • 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 

  • Weidlich, W.: Physics and social science: the approach of synergetics. Phys. Rep. 204 (1), 1–163 (1991)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • Weidlich, W.: Sociodynamics: A Systematic Approach to Mathematical Modelling in the Social Sciences. Harwood Academic Pubisher, London (2000)

    MATH  Google Scholar 

  • 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 

  • Wiggins, S.: Introduction to Applied Nonlinear Dynamical Systems and Chaos, 2nd edn. Springer, New York (2003)

    MATH  Google Scholar 

  • Wolpert, D.M.: Probabilistic models in human sensorimotor control. Hum. Mov. Sci. 26 (4), 511–524 (2007)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Wolpert, D.M., Ghahramani, Z.: Computational principles of movement neuroscience. Nat. Neurosci. 3, 1212–1217 (2000)

    Article  Google Scholar 

  • Wolpert, D.M., Kawato, M.: Multiple paired forward and inverse models for motor control. Neural Netw. 11 (7), 1317–1329 (1998)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    MathSciNet  MATH  Google Scholar 

  • Yuille, A., Kersten, D.: Vision as Bayesian inference: analysis by synthesis? Trends Cogn. Sci. 10 (7), 301–308 (2006)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Ziman, J.M.: The general variational principle of transport theory. Can. J. Phys. 34 (12A), 1256–1273 (1956)

    Article  ADS  MathSciNet  MATH  Google Scholar 

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Lubashevsky, I. (2017). Modeling of Human Behavior Within the Paradigm of Modern Physics. In: Physics of the Human Mind. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-51706-3_6

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