Sensitivity to temporal contingencies appears early in life and plays a key role in the ontogeny of socio-cognitive abilities in humans (Nadel et al., 1999; Gratier and Apter-Danon, 2009). The tendency for rhythmic coordination, sometimes referred to as “entrainment,” requires sensory-motor coupling (Phillips-Silver et al., 2010). In most of the fields of cognitive science, action-perception and agent-world coupling views are replacing the classical stimulus-response dichotomy (Marsh et al., 2009; Silberstein and Chemero, 2012; Schilbach et al., 2013; Novembre and Keller, 2014). Such conceptual frameworks are well suited to study coordination phenomena as they emphasize the dynamical nature of cognition (Varela et al., 1993; Kelso, 1995; Buzsáki and Draguhn, 2004; Lehmann and Schönwiesner, 2014). Moreover, they leave room for the balance of autonomy, a central feature of complex biological systems, and interactive coupling, through which such systems relate to—and make sense of—their environment (Di Paolo, 2005; Barandiaran et al., 2009; Buhrmann et al., 2013). A naturalistic study of autonomy and coupling requires both embracing ecological situations and considering first-person perspective. Furthermore, many social coordination phenomena cannot be observed in the laboratory without the interaction of at least two subjects. We propose to consider linking first- and third-person measures, and even relate them across multiple interacting individuals. We will discuss how these concepts are intertwined in coordination phenomena, and outline existing methods to address those issues.
Coupling and autonomy, the two faces of coordination
Coordination is related to two complementary aspects: autonomy and interactive coupling (Clayton et al., 2004). Autonomy refers to the intrinsic laws of organization of a living system (Varela, 1979). In turn, such laws define the environment to which a living system can couple. Autonomy thus provides a system with a dynamical background in the context of which sensory perturbations occur; it provides a perspective that shapes the lived world. In turn, as a result of their modulation, internal dynamics carry the imprint of the system's own environment. In other words, internal dynamics depend on agent~world relational dynamics (i.e., they depend on the way the relation between agent and world evolves). In short, complex systems and their environment co-determine each other. Patterns of coordination thus emerge dynamically from agent~world coupling and are therefore both autonomous and relational. By emergence, we mean that despite the fact that coordination is a by-product of intra-individual processes (Ross and Balasubramaniam, 2014), it is not reducible to them. In this opinion, we emphasize the inter-individual dimension of coordination, especially in the case of the human specific activities of music and dance [numerous ways of interacting have been studied in animals and humans (Strogatz, 2003), ranging from flock behavior (Okubo, 1986) to language (Dale et al., 2013; Manson et al., 2013)].
Rhythmic coordination of movements emerges from the dynamics of interaction between multiple component processes (Kelso, 1995; see also Van Orden et al., 2003); those interactions bring forth a dynamical landscape that pre-orients behavior and that can be modulated by intention (Kelso, 2002). For instance, rhythmic coordination of two limbs is driven by the dynamics of their relation, rather than by their sole intrinsic properties (Kelso, 1984). Coordination of one limb to an external pacer is governed by their relational dynamics as well (Kelso, 1981). Such dynamical interactions lead to coordination of activity across multiple scales of brain and behavior (Ihlen and Vereijken, 2010; Kelso et al., 2013). Human subjects can embody both pluri-frequential rhythms (Toiviainen et al., 2010) and the complexity of their fluctuations (Rankin et al., 2009; Marmelat et al., 2014). Thanks to the intrinsic complexity that underlies behavioral coordination, coupling allows coordination with the environment across scales (Laroche et al., 2014). If relational dynamics play a role in rhythmic coordination they may also play a key role in social interaction. Indeed, interacting with others is organized rhythmically and at multiple timescales since infancy (Gratier and Apter-Danon, 2009). The development of our capacity to coordinate rhythmically might lie in the dynamics of social interactions, rather than in purely intra-individual processes (Dumas, 2011): while we obviously need intra-individual capacities (e.g., vision) to handle social interaction, the development of our coordination capacities is shaped by those interactions as well (De Jaegher et al., 2010; Dumas et al., 2014b; Froese et al., 2014b)
Two-body approaches—at least two participants interacting in real-time—demonstrate that interactive contexts bring forth different qualities of coordination, in comparison with perceptive contexts in which participants' behaviors do not have any impact on their environment. Indeed, mutual interaction allows for more accurate/stable coordination than unilateral situations where only one partner is responsive to the other (Cummins, 2009; Konvalinka et al., 2010; Noy et al., 2011). Dynamics that are properly collective thus arise in mutual interaction, and they can attract and coordinate individual behaviors (De Jaegher and Di Paolo, 2007; Auvray et al., 2009; Lenay and Stewart, 2012; Laroche and Kaddouch, 2014). Such dynamics are therefore irreducible to purely intra-individual processes. Interacting subjects can yet rely on those relational dynamics; they can jointly regulate them. Overall, neither the dynamics of the interaction process nor their co-regulation can be observed when subjects are isolated from each other. It is thus important to study the dynamical properties of the interaction process itself in order to understand how we co-regulate them. More individually-centered processes of coordination might derive from recurrent social interactions. Recent methodological and technological advances make this change of paradigm possible.
Deciphering factors enabling coordination through an ecological and dynamical approach
Recent technological improvements made the continuous tracking of complex movements possible. For instance, whole-body motion capture through multi-camera settings showed that subjects can coordinate their movements simultaneously to multiple timescales of musical events (Toiviainen et al., 2010). Low-cost motion capture devices such as wireless accelerometers from video-game devices have been used to ecologically investigate interpersonal coordination between listeners dancing to music (De Bruyn et al., 2009). Recent analytical tools can deal with non-linear dynamics of movements and interpersonal coordination across multiple timescales [e.g., windowed-cross correlation (Boker et al., 2002); cross-wavelet transform (Issartel et al., 2007); frame-differencing methods (Paxton and Dale, 2013); cross-recurrence quantification analysis (Coco and Dale, 2014; Demos et al., 2014); detrended cross-correlation analysis (Hennig, 2014); multifractal detrended fluctuation analysis (Bedia et al., 2014)]. Such behavioral measures can discriminate between roles (i.e., leader/follower, Sacheli et al., 2013), individual strategies of regulation of coupling (Fairhurst et al., 2014), types of personality (Schmidt et al., 1994) or can identify signatures of social disorders (Varlet et al., 2014). With tools grasping the complexity of movements, more ecological experiments are within reach.
Several innovations in brain-imaging methods can be readily applied to the study of rhythmic coordination in music and dance contexts. Through a careful design of control conditions, the use of ecological musical stimuli is possible in fMRI (Blood and Zatorre, 2001) as well as in EEG with the Steady-States Evoked Potential (SS-EP) technique. Traditional event-related potential approaches require numerous stimuli repetitions; hence stimuli durations are usually kept to a minimum. With the SS-EP technique, a continuous stimulus such as music that has a periodic structure (or is frequency-tagged) can be presented and requires very few repetitions. It has been used successfully to demonstrate neural oscillations underlying listening and tapping to synthetic beats (Nozaradan et al., 2011, 2012, 2013) and could be extended to study ecological musical beats. Adequately studying natural cognition may require the integration of multiple modality and sensing techniques, while participants move freely (Makeig et al., 2009; Gramann et al., 2014). The recent years have seen the development of wearable devices for electrophysiological (Codrons et al., 2014), as well as EEG recordings (Debener et al., 2012; De Vos et al., 2014), allowing to experiment in contexts more ecological than the laboratory (e.g., a concert venue). As smartphones get powerful enough to process brain signals in real-time, conducting in-field or at-home EEG protocols is becoming feasible (Stopczynski et al., 2014). Because those systems are low-cost and yet can provide research-grade quality signals (Badcock et al., 2013), they can easily scale up to record multiple participants.
Over the last decade, social neuroscience took an interactive turn. Two-body and second-person neuroscience have especially been supporting the use of ecological paradigms for understanding the neural underpinning of social interaction (Schilbach et al., 2013). This ongoing interactive turn relies on the development of new methods. Hyperscanning, for instance, allows recording the brain activity of multiple individuals engaged in an interaction. This approach already demonstrated differential effects of social context (e.g., induced/spontaneous, see Dumas et al., 2012a) and role (e.g., leader/follower, see Dumas et al., 2012a; Sänger et al., 2013; Konvalinka et al., 2014) during interpersonal coordination. Music is an ideal ecological context for the study of coordination and has been used in the burgeoning field of social interaction neuroscience. The work of Lindenberger and colleagues has for instance revealed the inter-individual brain dynamics of joint improvisation (Müller et al., 2013) and how the global system should be described through both intra- and inter-individual processes (Sänger et al., 2012). An open question is how much does the observed inter-brain relationships rely on shared biological structure (Dumas et al., 2012b), task and environment (Burgess, 2013), or even cultural background (Vogeley and Roepstorff, 2009; Kitayama and Park, 2010). For instance, heart rate coordination can be induced by a common task (e.g., singing the same song in Vickhoff et al., 2013) or socially modulated coupling (Konvalinka et al., 2011). Neurocomputational modeling has already helped to measure the potential contribution of similarity at both anatomical and dynamical levels to our propensity to coordinate with others (Dumas et al., 2012b). Such empirically grounded models can moreover be combined with experiments through human-machine interaction (Dumas et al., 2014a). This is especially interesting for operationalizing real time and reciprocal social interactions while keeping a rigorous experimental control (e.g., parametrically manipulate coupling).
While at the objective level, many approaches have been proposed to relate intra- and inter-individual dynamics (Hasson et al., 2012; Konvalinka and Roepstorff, 2012; Dumas et al., 2014b), the link between the third (objective) and first person (subjective) accounts remains unclear. How can interaction help escaping this dichotomy? We argue that closing this gap requires a joint study of intrinsic and relational dynamics. Introspection in experimental psychology has been heavily criticized in the past decades, but more rigorous approaches are now being designed to study subjective experience while overcoming previous limitations (Bockelman et al., 2013; Petitmengin and Lachaux, 2013). Recent work has managed to question the lived experience of the intersubjective dimension of coordination (Froese et al., 2014a). The neurophenomenological approach demonstrates the feasibility of integrating first-person data with objective measures from cognitive neuroscience (Lutz et al., 2002; Fox et al., 2013). Despite those promising advances, the joint study of first and third person perspectives remains underrepresented in the literature. Music can provide a nice entry point to bridge the gap between the intimate subjective experience and objective brain~body processes (Flaig and Large, 2014). For instance, listeners experience modulates both emotional and neural responses (Chapin et al., 2010) and expert dancers display greater coherence between their subjective and physiological aspects of emotion (Sze et al., 2010).
Conclusion
We have seen how rhythmic and social coordination rely on both intrinsic and relational dynamics, namely autonomy and coupling. The complementarity of those two aspects of coordination has been empirically demonstrated, but many challenges remain. Two issues appear at reach with available methodologies: (1) quantifying how interactions with the environment and others have a causal role at the intra-individual level and non-additive consequences at the inter-individual level, and (2) deciphering the different factors of coupling across neural, behavioral and cultural scales. A last challenge is to embrace the ongoing change of paradigm that shall bridge the gap between the lived and the observed experience of social coordination in ecological contexts. Taken together, integrating first-, second- and third-person perspectives is a required move to accurately study natural human coordination phenomena.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Statements
Acknowledgments
Guillaume Dumas thanks Craig Nordham and Karl Lerud for early discussions about this topic. The authors thank Michael Zager and Alvin Fields for inspiring the title of this article with their song “Let's all chant” (1977).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
1
AuvrayM.LenayC.StewartJ. (2009). Perceptual interactions in a minimalist virtual environment. New Ideas Psychol. 27, 32–47. 10.1016/j.newideapsych.2007.12.002
2
BadcockN. A.MousikouP.MahajanY.de LissaP.ThieJ.McArthurG. (2013). Validation of the Emotiv EPOC(®) EEG gaming system for measuring research quality auditory ERPs. PeerJ. 1:e38. 10.7717/peerj.38
3
BarandiaranX. E.Di PaoloE.RohdeM. (2009). Defining agency: individuality, normativity, asymmetry, and spatio-temporality in action. Adapt. Behav. 17, 367–386. 10.1177/1059712309343819
4
BediaM. G.AguileraM.GómezT.LarrodeD. G.SeronF. (2014). Quantifying long-range correlations and 1/f patterns in a minimal experiment of social interaction. Front. Psychol. 5:1281. 10.3389/fpsyg.2014.01281
5
BloodA. J.ZatorreR. J. (2001). Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion. Proc. Natl. Acad. Sci. U.S.A. 98, 11818–11823. 10.1073/pnas.191355898
6
BockelmanP.Reinerman-JonesL.GallagherS. (2013). Methodological lessons in neurophenomenology: review of a baseline study and recommendations for research approaches. Front. Hum. Neurosci. 7:608. 10.3389/fnhum.2013.00608
7
BokerS. M.RotondoJ. L.XuM.KingK. (2002). Windowed cross-correlation and peak picking for the analysis of variability in the association between behavioral time series. Psychol. Methods7:338. 10.1037/1082-989X.7.3.338
8
BuhrmannT.Di PaoloE. A.BarandiaranX. (2013). A dynamical systems account of sensorimotor contingencies. Front. Psychol. 4:285. 10.3389/fpsyg.2013.00285
9
BurgessA. P. (2013). On the interpretation of synchronization in EEG hyperscanning studies: a cautionary note. Front. Hum. Neurosci. 7:881. 10.3389/fnhum.2013.00881
10
BuzsákiG.DraguhnA. (2004). Neuronal oscillations in cortical networks. Science304, 1926–1929. 10.1126/science.1099745
11
ChapinH.JantzenK.KelsoJ. S.SteinbergF.LargeE. (2010). Dynamic emotional and neural responses to music depend on performance expression and listener experience. PLoS ONE5:e13812. 10.1371/journal.pone.0013812
12
ClaytonM.SagerR.WillU. (2004). In time with the music: the concept of entrainment and its significance for ethnomusicology. ESEM Counterpoint1, 1–82. Available online at: oro.open.ac.uk/2661/?LKCAMPAIGN=it001_res&MEDIA=it001_c03
13
CocoM. I.DaleR. (2014). Cross-recurrence quantification analysis of categorical and continuous time series: an R package. Front. Psychol. 5:510. 10.3389/fpsyg.2014.00510
14
CodronsE.BernardiN. F.VandoniM.BernardiL. (2014). Spontaneous group synchronization of movements and respiratory rhythms. PLoS ONE9:e107538. 10.1371/journal.pone.0107538
15
CumminsF. (2009). Rhythm as entrainment: the case of synchronous speech. J. Phon. 37, 16–28. 10.1016/j.wocn.2008.08.003
16
DaleR.FusaroliR.DuranN.RichardsonD. C. (2013). The self-organization of human interaction. Psychol. Learn. Motiv. 59, 43–95. 10.1016/B978-0-12-407187-2.00002-2
17
DebenerS.MinowF.EmkesR.GandrasK.de VosM. (2012). How about taking a low-cost, small, and wireless EEG for a walk?Psychophysiology49, 1617–1621. 10.1111/j.1469-8986.2012.01471.x
18
De BruynL.LemanM.MoelantsD.DemeyM. (2009). Does social interaction activate music listeners?, in Computer Music Modeling and Retrieval. Genesis of Meaning in Sound and Music, eds SølviY.RichardK.-M.KristofferJ. (Berlin; Heidelberg: Springer), 93–106.
19
De JaegherH.Di PaoloE. (2007). Participatory sense-making. Phenomenol. Cogn. Sci. 6, 485–507. 10.1007/s11097-007-9076-9
20
De JaegherH.Di PaoloE.GallagherS. (2010). Can social interaction constitute social cognition?Trends. Cogn. Sci. 14, 441–447. 10.1016/j.tics.2010.06.009
21
DemosA. P.ChaffinR.KantV. (2014). Toward a dynamical theory of body movement in musical performance. Front. Psychol. 5:477. 10.3389/fpsyg.2014.00477
22
De VosM.GandrasK.DebenerS. (2014). Towards a truly mobile auditory brain–computer interface: exploring the P300 to take away. Int. J. Psychophysiol. 91, 46–53. 10.1016/j.ijpsycho.2013.08.010
23
Di PaoloE. A. (2005). Autopoiesis, adaptivity, teleology, agency. Phenomenol. Cogn. Sci. 4, 429–452. 10.1007/s11097-005-9002-y
24
DumasG. (2011). Towards a two-body neuroscience. Commun. Integr. Biol. 4, 349. 10.4161/cib.4.3.15110
25
DumasG.ChavezM.NadelJ.MartinerieJ. (2012b). Anatomical connectivity influences both intra-and inter-brain synchronizations. PLoS ONE7:e36414. 10.1371/journal.pone.0036414
26
DumasG.de GuzmanG. C.TognoliE.KelsoJ. A. S. (2014a). The human dynamic clamp as a paradigm for social interaction. Proc. Natl. Acad. Sci. U.S.A. 111, E3726–E3734. 10.1073/pnas.1407486111
27
DumasG.KelsoJ. A. S.NadelJ. (2014b). Tackling the social cognition paradox through multi-scale approaches. Front. Psychol. 5:882. 10.3389/fpsyg.2014.00882
28
DumasG.MartinerieJ.SoussignanR.NadelJ. (2012a). Does the brain know who is at the origin of what in an imitative interaction?Front. Hum. Neurosci. 6:128. 10.3389/fnhum.2012.00128
29
FairhurstM. T.JanataP.KellerP. E. (2014). Leading the follower: an fMRI investigation of dynamic cooperativity and leader–follower strategies in synchronization with an adaptive virtual partner. Neuroimage84, 688–697. 10.1016/j.neuroimage.2013.09.027
30
FlaigN. K.LargeE. W. (2014). Dynamic musical communication of core affect. Front. Psychol. 5:72. 10.3389/fpsyg.2014.00072
31
FoxK. C. R.NijeboerS.SolomonovaE.DomhoffG. W.ChristoffK. (2013). Dreaming as mind wandering: evidence from functional neuroimaging and first-person content reports. Front. Hum. Neurosci. 7:412. 10.3389/fnhum.2013.00412
32
FroeseT.IizukaH.IkegamiT. (2014a). Embodied social interaction constitutes social cognition in pairs of humans: a minimalist virtual reality experiment. Sci. Reports4:3672. 10.1038/srep03672
33
FroeseT.IizukaH.IkegamiT. (2014b). Using minimal human-computer interfaces for studying the interactive development of social awareness. Front. Psychol. 5:1061. 10.3389/fpsyg.2014.01061
34
GramannK.JungT.-P.FerrisD. P.LinC.-T.MakeigS. (2014). Toward a new cognitive neuroscience: modeling natural brain dynamics. Front. Hum. Neurosci. 8:444. 10.3389/fnhum.2014.00444
35
GratierM.Apter-DanonG. (2009). The improvised musicality of belonging: Repetition and variation in mother-infant vocal interaction, in Communicative musicality: Exploring the basis of human companionship, eds MallochS.TrevarthenC. (Oxford: Oxford University Press), 301–327.
36
HassonU.GhazanfarA. A.GalantucciB.GarrodS.KeysersC. (2012). Brain-to-brain coupling: a mechanism for creating and sharing a social world. Trends Cogn. Sci. 16, 114–121. 10.1016/j.tics.2011.12.007
37
HennigH. (2014). Synchronization in human musical rhythms and mutually interacting complex systems. Proc. Natl. Acad. Sci. U.S.A. 111, 12974–12979. 10.1073/pnas.1324142111
38
IhlenE. A.VereijkenB. (2010). Interaction-dominant dynamics in human cognition: beyond 1/f α fluctuation. J. Exp. Psychol. Gen. 139, 436. 10.1037/a0019098
39
IssartelJ.MarinL.CadopiM. (2007). Unintended interpersonal co-ordination: “can we march to the beat of our own drum?Neurosci. Lett. 411, 174–179. 10.1016/j.neulet.2006.09.086
40
KelsoJ. A. S. (1981). On the oscillatory basis of movement. Bull. Psychon. Soc. 18, 63–63.
41
KelsoJ. A. S. (1984). Phase transitions and critical behavior in human bimanual coordination. Am. J. Physiol. 246(6 pt 2), R1000–R1004.
42
KelsoJ. A. S. (1995). Dynamic Patterns: The Self-Organization of Brain and Behavior. Cambridge, MA: A Bradford Book.
43
KelsoJ. A. S. (2002). The complementary nature of coordination dynamics: self-organization and agency. Nonlinear Phenomena Complex Systems5, 364–371.
44
KelsoJ. A.DumasG.TognoliE. (2013). Outline of a general theory of behavior and brain coordination. Neural Netw. 37, 120–131. 10.1016/j.neunet.2012.09.003
45
KitayamaS.ParkJ. (2010). Cultural neuroscience of the self: understanding the social grounding of the brain. Soc. Cogn. Affect. Neurosci. 5, 111–129. 10.1093/scan/nsq052
46
KonvalinkaI.BauerM.StahlhutC.HansenL. K.RoepstorffA.FrithC. D. (2014). Frontal alpha oscillations distinguish leaders from followers: multivariate decoding of mutually interacting brains. NeuroImage94, 79–88. 10.1016/j.neuroimage.2014.03.003
47
KonvalinkaI.RoepstorffA. (2012). The two-brain approach: how can mutually interacting brains teach us something about social interaction?Front. Hum. Neurosci. 6:215. 10.3389/fnhum.2012.00215
48
KonvalinkaI.VuustP.RoepstorffA.FrithC. D. (2010). Follow you, follow me: continuous mutual prediction and adaptation in joint tapping. Q. J. Exp. Psychol. A. 63, 2220–2230. 10.1080/17470218.2010.497843
49
KonvalinkaI.XygalatasD.BulbuliaJ.SchjødtU.JegindøE. M.WallotS.et al. (2011). Synchronized arousal between performers and related spectators in a fire-walking ritual. Proc. Natl. Acad. Sci. U.S.A. 108, 8514–8519. 10.1073/pnas.1016955108
50
LarocheJ.BerardiA.BrangierE. (2014). Embodiment of intersubjective time: relational dynamics as attractors in the temporal coordination of interpersonal behaviors and experiences. Front. Psychol. 5:1180. 10.3389/fpsyg.2014.01180
51
LarocheJ.KaddouchI. (2014). Enacting teaching and learning in the interactionprocess: “Keys” for developing skills in piano lessons through four-hand improvisations. J. Pedagogy5, 24–47. 10.2478/jped-2014-0002
52
LehmannA.SchönwiesnerM. (2014). Selective attention modulates human auditory brainstem responses: relative contributions of frequency and spatial cues. PLoS ONE9:e85442. 10.1371/journal.pone.0085442
53
LenayC.StewartJ. (2012). Minimalist approach to perceptual interactions. Front. Hum. Neurosci. 6:98. 10.3389/fnhum.2012.00098
54
LutzA.LachauxJ. P.MartinerieJ.VarelaF. J. (2002). Guiding the study of brain dynamics by using first-person data: synchrony patterns correlate with ongoing conscious states during a simple visual task. Proc. Natl. Acad. Sci. U.S.A. 99, 1586–1591. 10.1073/pnas.032658199
55
MakeigS.GramannK.JungT. P.SejnowskiT. J.PoiznerH. (2009). Linking brain, mind and behavior. Int. J. Psychophysiol. 73, 95–100. 10.1016/j.ijpsycho.2008.11.008
56
MansonJ. H.BryantG. A.GervaisM. M.KlineM. A. (2013). Convergence of speech rate in conversation predicts cooperation. Evol. Hum. Behav. 34, 419–426. 10.1016/j.evolhumbehav.2013.08.001
57
MarmelatV.TorreK.BeekP. J.DaffertshoferA. (2014). Persistent fluctuations in stride intervals under fractal auditory stimulation. PLoS ONE9:e91949. 10.1371/journal.pone.0091949
58
MarshK. L.JohnstonL.RichardsonM. J.SchmidtR. C. (2009). Toward a radically embodied, embedded social psychology. Eur. J. Soc. Psychol. 39, 1217–1225. 10.1002/ejsp.666
59
MüllerV.SängerJ.LindenbergerU. (2013). Intra- and inter-brain synchronization during musical improvisation on the guitar. PLoS ONE8:e73852. 10.1371/journal.pone.0073852
60
NadelJ.CarchonI.KervellaC.MarcelliD.Réserbat−PlanteyD. (1999). Expectancies for social contingency in 2-month-olds. Dev. Sci. 2, 164–173. 10.1111/1467-7687.00065
61
NovembreG.KellerP. E. (2014). A conceptual review on action-perception coupling in the musicians' brain: what is it good for?Front. Hum. Neurosci. 8:603. 10.3389/fnhum.2014.00603
62
NoyL.DekelE.AlonU. (2011). The mirror game as a paradigm for studying the dynamics of two people improvising motion together. Proc. Natl. Acad. Sci. U.S.A. 108, 20947–20952. 10.1073/pnas.1108155108
63
NozaradanS.PeretzI.MissalM.MourauxA. (2011). Tagging the neuronal entrainment to beat and meter. J. Neurosci. 31, 10234–10240. 10.1523/JNEUROSCI.0411-11.2011
64
NozaradanS.PeretzI.MourauxA. (2012). Selective neuronal entrainment to the beat and meter embedded in a musical rhythm. J. Neurosci. 32, 17572–17581. 10.1523/JNEUROSCI.3203-12.2012
65
NozaradanS.ZeroualiY.PeretzI.MourauxA. (2013). Capturing with EEG the neural entrainment and coupling underlying sensorimotor synchronization to the beat. Cereb. Cortex. [Epub ahead of print]. 10.1093/cercor/bht261
66
OkuboA. (1986). Dynamical aspects of animal grouping: swarms, schools, flocks, and herds. Adv. Biophys. 22, 1–94. 10.1016/0065-227X(86)90003-1
67
PaxtonA.DaleR. (2013). Frame-differencing methods for measuring bodily synchrony in conversation. Behav. Res. Methods45, 329–343. 10.3758/s13428-012-0249-2
68
PetitmenginC.LachauxJ. P. (2013). Microcognitive science: bridging experiential and neuronal microdynamics. Front. Hum. Neurosci. 7:617. 10.3389/fnhum.2013.00617
69
Phillips-SilverJ.AktipisC. A.BryantG. A. (2010). The ecology of entrainment: foundations of coordinated rhythmic movement. Music Percept. 28:3. 10.1525/mp.2010.28.1.3
70
RankinS. K.EdwardW.FinkP. W. (2009). Fractal tempo fluctuation and pulse prediction. Music Percept. 26, 401–413. 10.1525/mp.2009.26.5.401
71
RossJ. M.BalasubramaniamR. (2014). Physical and neural entrainment to rhythm: human sensorimotor coordination across tasks and effector systems. Front. Hum. Neurosci. 8:576. 10.3389/fnhum.2014.00576
72
SacheliL. M.TidoniE.PavoneE. F.AgliotiS. M.CandidiM. (2013). Kinematics fingerprints of leader and follower role-taking during cooperative joint actions. Exp. Brain Res. 226, 473–486. 10.1007/s00221-013-3459-7
73
SängerJ.MüllerV.LindenbergerU. (2012). Intra- and interbrain synchronization and network properties when playing guitar in duets. Front. Hum. Neurosci. 6:312. 10.3389/fnhum.2012.00312
74
SängerJ.MüllerV.LindenbergerU. (2013). Directionality in hyperbrain networks discriminates between leaders and followers in guitar duets. Front. Hum. Neurosci. 7:234. 10.3389/fnhum.2013.00234
75
SchilbachL.TimmermansB.ReddyV.CostallA.BenteG.SchlichtT.et al. (2013). Toward a second-person neuroscience. Behav. Brain Sci. 36, 393–414. 10.1017/S0140525X12000660
76
SchmidtR. C.ChristiansonN.CarelloC.BaronR. (1994). Effects of social and physical variables on between-person visual coordination. Ecol. Psychol. 6, 159–183. 10.1207/s15326969eco0603_1
77
SilbersteinM.ChemeroA. (2012). Complexity and extended phenomenological−cognitive systems. Topics Cogn. Sci. 4, 35–50. 10.1111/j.1756-8765.2011.01168.x
78
StopczynskiA.StahlhutC.PetersenM. K.LarsenJ. E.JensenC. F.IvanovaM. G.et al. (2014). Smartphones as pocketable labs: visions for mobile brain imaging and neurofeedback. Int. J. Psychophysiol. 91, 54–66. 10.1016/j.ijpsycho.2013.08.007
79
StrogatzS. (2003). Sync: The Emerging Science of Spontaneous Order. New York, NY: Theia (Hyperion).
80
SzeJ. A.GyurakA.YuanJ. W.LevensonR. W. (2010). Coherence between emotional experience and physiology: does body awareness training have an impact?Emotion10, 803–814. 10.1037/a0020146
81
ToiviainenP.LuckG.ThompsonM. R. (2010). Embodied meter: hierarchical eigenmodes in music-induced movement. Music Percept. 28, 59–70. 10.1525/mp.2010.28.1.59
82
Van OrdenG. C.HoldenJ. G.TurveyM. T. (2003). Self-organization of cognitive performance. J. Exp. Psychol. Gen. 132, 331–350. 10.1037/0096-3445.132.3.331
83
VarelaF. J. (1979). Principles of Biological Autonomy. New York, NY: North Holland.
84
VarelaF. J.RoschE.ThompsonE. (1993). The Embodied Mind: Cognitive Science and Human Experience. Cambridge, MA: MIT press.
85
VarletM.MarinL.CapdevielleD.Del-MonteJ.SchmidtR. C.SalesseR. N.et al. (2014). Difficulty leading interpersonal coordination: towards an embodied signature of social anxiety disorder. Front. Behav. Neurosci. 8:29. 10.3389/fnbeh.2014.00029
86
VickhoffB.MalmgrenH.AströmR.NybergG.EkströmS.-R.EngwallM.et al. (2013). Music structure determines heart rate variability of singers. Front. Psychol. 4:334. 10.3389/fpsyg.2013.00334
87
VogeleyK.RoepstorffA. (2009). Contextualising culture and social cognition. Trends Cogn. Sci. 13, 511–516. 10.1016/j.tics.2009.09.006
Summary
Keywords
coupling, coordination, interpersonal, dynamical systems, human interaction
Citation
Dumas G, Laroche J and Lehmann A (2014) Your body, my body, our coupling moves our bodies. Front. Hum. Neurosci. 8:1004. doi: 10.3389/fnhum.2014.01004
Received
23 September 2014
Accepted
25 November 2014
Published
16 December 2014
Volume
8 - 2014
Edited by
Jessica Phillips-Silver, Georgetown University Medical Center, USA
Reviewed by
Gregory Bryant, University of California, Los Angeles, USA
Copyright
© 2014 Dumas, Laroche and Lehmann.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: guillaume.dumas@pasteur.fr
This article was submitted to the journal Frontiers in Human Neuroscience.
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.