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2018 | OriginalPaper | Buchkapitel

The Human Dynamic Clamp: A Probe for Coordination Across Neural, Behavioral, and Social Scales

verfasst von : Guillaume Dumas, Aline Lefebvre, Mengsen Zhang, Emmanuelle Tognoli, J. A. Scott Kelso

Erschienen in: Complexity and Synergetics

Verlag: Springer International Publishing

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Abstract

Humans (with their brains, bodies and behaviors) are complex dynamical systems embedded in an environment that includes a multitude of other conspecifics. Moving beyond previous brain- centered views of the human mind requires to develop a parsimonious yet integrative account that relates neural, behavioral, and social scales. Social neuroscience has recently started to acknowledge the importance of relational dynamics when it extended its purview from social stimuli to human-human interactions. Human-machine interactions also constitute promising tools to probe multiple scales in a controlled manner. Inspired by the electrophysiological method of the dynamic clamp, Virtual Partner Interaction (VPI) allows real time interaction between human subjects and their simulations as dynamical system. This provides a new test bed for operationalizing theoretical models in experimental settings. We discuss how VPI can be generalized into a Human Dynamic Clamp (HDC), a paradigm that allows the exploration of the parameter spaces of interactional dynamics in various contexts: from rhythmic and discrete coordination to adaptive and intentional behaviors, including learning. HDC brings humans and machines together to question our understanding of the natural and our theory behind the artificial.

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Vorheriges Kapitel Coordination Dynamics and Synergetics: From Finger Movements to Brain Patterns and Ballet Dancing

Nächstes Kapitel Design Principle for a Population-Based Model of Epileptic Dynamics

J.A.S. Kelso, G. Dumas, E. Tognoli, Outline of a general theory of behavior and brain coordination. Neural Netw. Off. J. Int. Neural Netw. Soc. 37, 120–131 (2013). https://doi.org/10.1016/j.neunet.2012.09.003 CrossRef

J.A.S. Kelso, Coordination dynamics, in Encyclopedia of Complexity and Systems Science, ed. by R.M. Meyers (Springer, New York, 2009), pp. 1537–1565CrossRef

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

J.A.S. Kelso, J.J. Jeka, Symmetry breaking dynamics of human multilimb coordination. J. Exp. Psychol. Hum. Percept. Perform. 18, 645 (1992)CrossRef

G. Dumas, G.C. de Guzman, E. Tognoli, J.A.S. Kelso, The human dynamic clamp as a paradigm for social interaction. Proc. Natl. Acad. Sci. U.S.A. 111, E3726–E3734 (2014). https://doi.org/10.1073/pnas.1407486111 CrossRef

J.A.S. Kelso, G.C. de Guzman, C. Reveley, E. Tognoli, Virtual partner interaction (VPI): exploring novel behaviors via coordination dynamics. PLoS ONE 4, e5749 (2009). https://doi.org/10.1371/journal.pone.0005749.t002 CrossRef

S.L. Bressler, E. Tognoli, Operational principles of neurocognitive networks. Int. J. Psychophysiol. 60, 139–148 (2006). https://doi.org/10.1016/j.ijpsycho.2005.12.008 CrossRef

O. Oullier, G.C. de Guzman, K.J. Jantzen, J.A.S. Kelso, On context dependence of behavioral variability in inter-personal coordination. Int. J. Comput. Sci. Sport 2, 126–128 (2003)

G. Dumas, J. Nadel, R. Soussignan et al., Inter-brain synchronization during social interaction. PLoS ONE 5, e12166 (2010). https://doi.org/10.1371/journal.pone.0012166 CrossRef

10.

V. Müller, J. Sänger, U. Lindenberger, Intra- and inter-brain synchronization during musical improvisation on the guitar. PLoS ONE 8, e73852 (2013). https://doi.org/10.1371/journal.pone.0073852.s012 CrossRef

11.

E. Tognoli, J. Lagarde, G.C. de Guzman, J.A.S. Kelso, The phi complex as a neuromarker of human social coordination. Proc. Natl. Acad. Sci. U.S.A. 104, 8190–8195 (2007)CrossRef

12.

G. Buzsáki, A. Draguhn, Neuronal oscillations in cortical networks. Sci. NY 304, 1926–1929 (2004). https://doi.org/10.1126/science.1099745 CrossRef

13.

E. Tognoli, J.A.S. Kelso, The metastable brain. Neuron 81, 35–48 (2014). https://doi.org/10.1016/j.neuron.2013.12.022 CrossRef

14.

H.R. Maturana, F.J. Varela, The Tree of Knowledge: The Biological Roots of Human Understanding (New Science Library/Shambhala Publications, 1987)

15.

H. Haken, Advanced Synergetics (Springer, Berlin, Heidelberg, 1983)MATH

16.

A. Fuchs, Nonlinear Dynamics in Complex Systems (Springer, Berlin, Heidelberg, 2013)CrossRefMATH

17.

G. Schöner, A dynamic theory of coordination of discrete movement. Biol. Cybern. 63(4), 257–270 (1990)

18.

F. Babiloni, L. Astolfi, Social neuroscience and hyperscanning techniques: past, present and future. Neurosci. Biobehav. Rev. 44, 1–18 (2012). https://doi.org/10.1016/j.neubiorev.2012.07.006

19.

G. Dumas, F. Lachat, J. Martinerie et al., From social behaviour to brain synchronization: review and perspectives in hyperscanning. Irbm 32, 48–53 (2011)CrossRef

20.

U. Hasson, A.A. Ghazanfar, B. Galantucci et al., Brain-to-brain coupling: a mechanism for creating and sharing a social world. Trends Cogn. Sci. 16, 114–121 (2012). https://doi.org/10.1016/j.tics.2011.12.007 CrossRef

21.

I. Konvalinka, A. Roepstorff, The two-brain approach: how can mutually interacting brains teach us something about social interaction? Front. Hum. Neurosci. 6, 215 (2012)CrossRef

22.

P.R. Montague, G.S. Berns, J.D. Cohen et al., Hyperscanning: simultaneous fMRI during linked social interactions. NeuroImage 16, 1159–1164 (2002). https://doi.org/10.1006/nimg.2002.1150 CrossRef

23.

G. Dumas, Towards a two-body neuroscience. Commun. Integr. Biol. 4, 349–352 (2011). https://doi.org/10.4161/cib.4.3.15110 CrossRef

24.

R. Hari, L. Henriksson, S. Malinen, L. Parkkonen, Centrality of social interaction in human brain function. Neuron 88, 181–193 (2015). https://doi.org/10.1016/j.neuron.2015.09.022 CrossRef

25.

R. Hari, M.V. Kujala, Brain basis of human social interaction: from concepts to brain imaging. Physiol. Rev. 89, 453–479 (2009). https://doi.org/10.1152/physrev.00041.2007 CrossRef

26.

L. Schilbach, B. Timmermans, V. Reddy et al., Toward a second-person neuroscience. Behav. Brain Sci. 36, 393–414 (2013). https://doi.org/10.1017/S0140525X12000660 CrossRef

27.

J. Mattout. Brain-computer interfaces: a neuroscience paradigm of social interaction? A matter of perspective. Front. Hum. Neurosci. (2012). https://doi.org/10.3389/fnhum.2012.00114

28.

U.J. Pfeiffer, B. Timmermans, G. Bente et al., A non-verbal Turing test: differentiating mind from machine in gaze-based social interaction. PLoS ONE 6, e27591 (2011). https://doi.org/10.1371/journal.pone.0027591 CrossRef

29.

J. Scholtz, Theory and evaluation of human robot interactions, in Proceedings of the 36th Annual Hawaii International Conference on System Sciences, 2003 (IEEE, 2003), p 10

30.

M. Ochs, C. Pelachaud, D. Sadek, An empathic virtual dialog agent to improve human-machine interaction, in Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (Estoril, Portugal, 2008)

31.

M. Zhang, G. Dumas, J.A.S. Kelso, E. Tognoli, Enhanced emotional responses during social coordination with a virtual partner. Int. J. Psychophysiol. 104, 33–43 (2016). https://doi.org/10.1016/j.ijpsycho.2016.04.001 CrossRef

32.

L. Schilbach, A.M. Wohlschlaeger, N.C. Kraemer et al., Being with virtual others: neural correlates of social interaction. Neuropsychologia 44, 718–730 (2006). https://doi.org/10.1016/j.neuropsychologia.2005.07.017 CrossRef

33.

J.M. Loomis, J.J. Blascovich, A.C. Beall, Immersive virtual environment technology as a basic research tool in psychology. Behav. Res. Methods Instrum. Comput. 31, 557–564 (1999)CrossRef

34.

C. McCall, J. Blascovich, How, when, and why to use digital experimental virtual environments to study social behavior. Soc. Personal Psychol. Compass 3, 744–758 (2009)CrossRef

35.

M.V. Sanchez-Vives, M. Slater, From presence to consciousness through virtual reality. Nat. Rev. Neurosci. 6, 332–339 (2005). https://doi.org/10.1038/nrn1651 CrossRef

36.

F. Biocca, C. Harms, J.K. Burgoon, Toward a more robust theory and measure of social presence: review and suggested criteria. Presence 12, 456–480 (2003)CrossRef

37.

M. Garau, M. Slater, D.-P. Pertaub, S. Razzaque, The responses of people to virtual humans in an immersive virtual environment. Presence Teleoper. Virtual Environ. 14, 104–116 (2005)CrossRef

38.

B.H. Repp, P.E. Keller, Sensorimotor synchronization with adaptively timed sequences. Hum. Mov. Sci. 27, 423–456 (2008)CrossRef

39.

M.T. Fairhurst, P. Janata, P.E. Keller, Being and feeling in sync with an adaptive virtual partner: Brain mechanisms underlying dynamic cooperativity. Cereb. Cortex 23, 2592–2600 (2013). https://doi.org/10.1093/cercor/bhs243 CrossRef

40.

V. Kostrubiec, G. Dumas, P.-G. Zanone, J.A.S. Kelso, The virtual teacher (VT) paradigm: Learning new patterns of interpersonal coordination using the human dynamic clamp. PLoS ONE 10, e0142029 (2015)CrossRef

41.

J.A.S. Kelso, Phase transitions: foundations of behavior, in Synergetics of Cognition, ed. by H. Haken, M. Stadler (Springer, Berlin, 1990), pp. 249–268

42.

J.A.S. Kelso, G. Schöner, J.P. Scholz, H. Haken, Phase-locked modes, phase transitions and component oscillators in biological motion. Phys. Scr. 35, 1–9 (1987)CrossRef

43.

M. Golubitsky, I. Stewart, The Symmetry Perspective: From Equilibrium to Chaos in Phase Space and Physical Space (Springer Science and Business Media, 2003)

44.

K.G. Wilson, Problems in physics with many scales of length. Sci. Am. 241, 158–179 (1979)CrossRef

45.

A.A. Prinz, L.F. Abbott, E. Marder, The dynamic clamp comes of age. Trends Neurosci. 27, 218–224 (2004). https://doi.org/10.1016/j.tins.2004.02.004 CrossRef

46.

S.H. Strogatz, Nonlinear Dynamics and Chaos (Westview Press, Boulder, 2008)

47.

H. Haken, J.A.S. Kelso, H. Bunz, A theoretical model of phase transitions in human hand movements. Biol. Cybern. 51, 347–356 (1985)CrossRefMATHMathSciNet

48.

O. Oullier, G.C. de Guzman, K.J. Jantzen et al., Social coordination dynamics: measuring human bonding. Soc. Neurosci. 3, 178–192 (2008)CrossRef

49.

J. Lagarde, C. Peham, T. Licka, J.A.S. Kelso, Coordination dynamics of the horse-rider system. J. Mot. Behav. 37, 418–424 (2005)CrossRef

50.

J. Lagarde, J.A.S. Kelso, Binding of movement, sound and touch: multimodal coordination dynamics. Exp. Brain Res. 173, 673–688 (2006)CrossRef

51.

J.A.S. Kelso, S.L. Bressler, S. Buchanan et al., A phase transition in human brain and behavior. Phys. Lett. A 169, 134–144 (1992). https://doi.org/10.1016/0375-9601(92)90583-8 CrossRef

52.

J.A.S. Kelso, A. Fuchs, R. Lancaster et al., Dynamic cortical activity in the human brain reveals motor equivalence. Nature 392, 814–817 (1998)CrossRef

53.

S.L. Bressler, J.A.S. Kelso, Cortical coordination dynamics and cognition. Trends Cogn. Sci. 5, 26–36 (2001)CrossRef

54.

K.J. Jantzen, F.L. Steinberg, J.A.S. Kelso, Coordination dynamics of large-scale neural circuitry underlying rhythmic sensorimotor behavior. J. Cogn. Neurosci. 21, 2420–2433 (2009)CrossRef

55.

E. Tognoli, J.A.S. Kelso, Brain coordination dynamics: true and false faces of phase synchrony and metastability. Prog. Neurobiol. 87, 31–40 (2009). https://doi.org/10.1016/j.pneurobio.2008.09.014 CrossRef

56.

J.A.S. Kelso, J. DelColle, G. Schöner, Action perception as a pattern formation process, in Attention and Performance XIII, ed. by M. Jeannerod (Erlbaum, Hills-dale, NJ, 1990), pp. 139–169

57.

K.J. Jantzen, F.L. Steinberg, J.A.S. Kelso, Brain networks underlying human timing behavior are influenced by prior context. Proc. Natl. Acad. Sci. U.S.A. 101, 6815–6820 (2004). https://doi.org/10.1073/pnas.0401300101 CrossRef

58.

O. Oullier, K.J. Jantzen, F.L. Steinberg, J.A.S. Kelso, Neural substrates of real and imagined sensorimotor coordination. Cereb. Cortex 15, 975–985 (2004). https://doi.org/10.1093/cercor/bhh198 CrossRef

59.

A. Fuchs, J.A.S. Kelso, Movement coordination, in Encyclopedia of Complexity and Systems Science (Springer, 2009), pp. 5718–5736

60.

R. Huys, D. Perdikis, V.K. Jirsa, Functional architectures and structured flows on manifolds: a dynamical framework for perceptual-motor behavior. Psychol. Rev. 121, 1–113 (2013). https://doi.org/10.1037/a0037014

61.

F. Danion, Do we need internal models for movement control? Nonlinear Dyn. Hum. Behav. 328, 115–134 (2011). https://doi.org/10.1007/978-3-642-16262-6_5 CrossRefMathSciNet

62.

S. Degallier, A. Ijspeert, Modeling discrete and rhythmic movements through motor primitives: a review. Biol. Cybern. 103, 319–338 (2010)CrossRefMATH

63.

R. Huys, B.E. Studenka, N.L. Rheaume et al., Distinct timing mechanisms produce discrete and continuous movements. PLoS Comput. Biol. 4, e1000061 (2008). https://doi.org/10.1371/journal.pcbi.1000061.g004 CrossRefMathSciNet

64.

R. Huys, V.K. Jirsa, Nonlinear Dynamics in Human Behavior (Springer, Heidelberg, 2010)

65.

J.A.S. Kelso, K.G. Holt, P.N. Kugler, M.T. Turvey, On the concept of coordinative structures as dissipative structures: II. Empirical lines of convergence. Adv Psychol. 1, 49–70 (1980)CrossRef

66.

P.N. Kugler, J.A.S. Kelso, M.T. Turvey, On the concept of coordinative structures as dissipative structures: I. Theoretical lines of convergence. Tutor. Mot. Behav. 3, 47 (1980)

67.

D. Sternad, W.J. Dean, S. Schaal, Interaction of rhythmic and discrete pattern generators in single-joint movements. Hum. Mov. Sci. 19(4), 627–664 (2000)

68.

V.K. Jirsa, J.A.S. Kelso, The excitator as a minimal model for the coordination dynamics of discrete and rhythmic movement generation. J. Mot. Behav. 37, 35–51 (2005)CrossRef

69.

P.W. Fink, J.A.S. Kelso, V.K. Jirsa, Perturbation-induced false starts as a test of the Jirsa-Kelso Excitator model. J. Mot. Behav. 41, 147–157 (2009)CrossRef

70.

R. FitzHugh, Impulses and physiological states in theoretical models of nerve membrane. Biophys. J. 1, 445 (1961)CrossRef

71.

R. Rosen, Life itself: a comprehensive inquiry into the nature, origin, and fabrication of life (Columbia University Press, New York, 1991)

72.

J. Holland, Adaptation in Natural and Artificial Systems (MIT Press, Cambridge, MA, 1992)

73.

M. Kinsbourne, J.S. Jordan, Embodied anticipation: a neurodevelopmental interpretation. Discourse Process. 46, 103–126 (2009)CrossRef

74.

T.L. Chartrand, J.A. Bargh, The chameleon effect: the perception–behavior link and social interaction. J. Pers. Soc. Psychol. 76, 893 (1999)CrossRef

75.

A. Fogel, Two principles of communication: co-regulation and framing. New Perspect. Early Commun. Dev. 9–22 (1993)

76.

D.A. Engstrøm, J.A.S. Kelso, T. Holroyd, Reaction-anticipation transitions in human perception-action patterns. Hum. Mov. Sci. 15, 809–832 (1996)CrossRef

77.

J. Peters, S. Schaal, Policy gradient methods for robotics, in 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2006), pp. 2219–2225

78.

Friston KJ (2010) The free-energy principle: a unified brain theory? 11:127–138. https://doi.org/10.1038/nrn2787

79.

T. Kohonen, Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)CrossRefMATHMathSciNet

80.

I. Harvey, E. Di Paolo, R. Wood et al., Evolutionary robotics: a new scientific tool for studying cognition. Artif. Life 11, 79–98 (2005)CrossRef

81.

B. Ermentrout, Neural networks as spatio-temporal pattern-forming systems. Rep. Prog. Phys. 61, 353 (1999)CrossRef

82.

E.W. Large, M.R. Jones, The dynamics of attending: how people track time-varying events. Psychol. Rev. 106, 119 (1999)CrossRef

83.

I. Konvalinka, P. Vuust, A. Roepstorff, A coupled oscillator model of interactive tapping, in Proceedings of the 7th Triennial Conference of European Society for the Cognitive Sciences of Music (Jyvaskyla, Finland, 2009)

84.

E.L. Saltzman, K.G. Munhall, Skill acquisition and development: The roles of state-, parameter-, and graph-dynamics. J. Mot. Behav. 24, 49–57 (1992)CrossRef

85.

L. Righetti, J. Buchli, A.J. Ijspeert, Adaptive frequency oscillators and applications. Open Cybern. Syst. J. 3, 64–69 (2009)CrossRefMATHMathSciNet

86.

L. Righetti, J. Buchli, A.J. Ijspeert, Dynamic Hebbian learning in adaptive frequency oscillators. Phys. Nonlinear Phenom. 216, 269–281 (2006). https://doi.org/10.1016/j.physd.2006.02.009 CrossRefMATHMathSciNet

87.

A.A. Sharp, M.B. O’Neil, L.F. Abbott, E. Marder, The dynamic clamp: artificial conductances in biological neurons. Trends Neurosci. 16, 389–394 (1993)CrossRef

88.

W.J. Freeman, Consciousness, intentionality and causality. J. Conscious. Stud. 6, 143–172 (1999)

89.

J.-J. Aucouturier, T. Ikegami, The illusion of agency: two engineering approaches to compromise autonomy and reactivity in an artificial system. Adapt. Behav. 17, 402–420 (2009). https://doi.org/10.1177/1059712309344420 CrossRef

90.

X.E. Barandiaran, E. Di Paolo, M. Rohde, Defining agency: Individuality, normativity, asymmetry, and spatio-temporality in action. Adapt. Behav. 17, 367–386 (2009)CrossRef

91.

J.A.S. Kelso, J.P. Scholz, G. Schöner, Dynamics governs switching among patterns of coordination in biological movement. Phys. Lett. A 134, 8–12 (1988)CrossRef

92.

C. De Luca, K.J. Jantzen, S. Comani et al., Striatal activity during intentional switching depends on pattern stability. J. Neurosci. 30, 3167–3174 (2010). https://doi.org/10.1523/JNEUROSCI.2673-09.2010 CrossRef

93.

E. Tognoli, G. Dumas, J.A.S. Kelso, A roadmap to computational social neuroscience, in Advances in Cognitive Neurodynamics (V) (Springer, Dordrecht, in press)

94.

G. Schöner, J.A.S. Kelso, A dynamic pattern theory of behavioral change. Science 135, 1513–1520 (1988). https://doi.org/10.1126/science.3281253 CrossRef

Titel: The Human Dynamic Clamp: A Probe for Coordination Across Neural, Behavioral, and Social Scales
verfasst von: Guillaume Dumas
Aline Lefebvre
Mengsen Zhang
Emmanuelle Tognoli
J. A. Scott Kelso
Verlag: Springer International Publishing
Buch: Complexity and Synergetics
Print ISBN: 978-3-319-64333-5

Electronic ISBN: 978-3-319-64334-2

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-64334-2_24