Brandão, T., Matias, M., Ferreira, T., Vieira, J., Schulz, M.S., Matos, P.M.: Attachment, emotion regulation, and well-being in couples: intrapersonal and interpersonal associations. J. Pers. 88(4), 748–761 (2020). https://doi.org/10.1111/jopy.12523
Article
Google Scholar
Karreman, A., Vingerhoets, A.J.J.M.: Attachment and well-being: the mediating role of emotion regulation and resilience. Personal. Individ. Differ. 53(7), 821–826 (2012). https://doi.org/10.1016/j.paid.2012.06.014
Article
Google Scholar
Caruana, N., McArthur, G., Woolgar, A., Brock, J.: Simulating social interactions for the experimental investigation of joint attention. Neurosci. Biobehav. Rev. 74, 115–125 (2017). https://doi.org/10.1016/j.neubiorev.2016.12.022
Article
Google Scholar
Innocenti, A., De Stefani, E., Bernardi, N.F., Campione, G.C., Gentilucci, M.: Gaze direction and request gesture in social interactions. PLoS ONE 7(5), e36390 (2012). https://doi.org/10.1371/journal.pone.0036390
Article
Google Scholar
Treger, S., Sprecher, S., Erber, R.: Laughing and liking: Exploring the interpersonal effects of humor use in initial social interactions. Eur. J. Soc. Psychol. 43(6), 532–543 (2013). https://doi.org/10.1002/ejsp.1962
Article
Google Scholar
Brehm, J.M.: Community attachment: the complexity and consequence of the natural environment facet. Hum. Ecol. 35(4), 477–488 (2007). https://doi.org/10.1007/s10745-006-9104-3
Article
Google Scholar
Cacioppo, J.T., Berntson, G.G.: Social Neuroscience: Key Readings. Psychology Press (2005)
Google Scholar
Pönkänen, L.M., Peltola, M.J., Hietanen, J.K.: The observer observed: frontal EEG asymmetry and autonomic responses differentiate between another person’s direct and averted gaze when the face is seen live. Int. J. Psychophysiol. 82(2), 180–187 (2011). https://doi.org/10.1016/j.ijpsycho.2011.08.006
Article
Google Scholar
Pönkänen, L.M., Hietanen, J.K.: Eye contact with neutral and smiling faces: effects on autonomic responses and frontal EEG asymmetry. Front. Hum. Neurosci. 6 (2012). https://doi.org/10.3389/fnhum.2012.00122
Hasson, U., Ghazanfar, A.A., Galantucci, B., Garrod, S., Keysers, C.: Brain-to-brain coupling: a mechanism for creating and sharing a social world. Trends Cogn. Sci. 16(2), 114–121 (2012). https://doi.org/10.1016/j.tics.2011.12.007
Article
Google Scholar
Kelsen, B.A., Sumich, A., Kasabov, N., Liang, S.H.Y., Wang, G.Y.: What has social neuroscience learned from hyperscanning studies of spoken communication? A systematic review. Neurosci. Biobehav. Rev. 132, 1249–1262 (2022). https://doi.org/10.1016/j.neubiorev.2020.09.008
Article
Google Scholar
Baccalá, L.A., Sameshima, K.: Partial directed coherence: a new concept in neural structure determination. Biol. Cybern. 84(6), 463–474 (2001). https://doi.org/10.1007/PL00007990
Article
Google Scholar
Lachaux, J.-P., Rodriguez, E., Martinerie, J., Varela, F.J.: Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8(4), 194–208 (1999). https://doi.org/10.1002/(SICI)1097-0193(1999)8:4%3c194::AID-HBM4%3e3.0.CO;2-C
Article
Google Scholar
Ménoret, M., et al.: Neural correlates of non-verbal social interactions: a dual-EEG study. Neuropsychologia 55, 85–97 (2014). https://doi.org/10.1016/j.neuropsychologia.2013.10.001
Article
Google Scholar
Czeszumski, A., et al.: Hyperscanning: a valid method to study neural inter-brain underpinnings of social interaction. Front. Hum. Neurosci. 14, 39 (2020)
Article
Google Scholar
Shamay-Tsoory, S.G., Mendelsohn, A.: Real-life neuroscience: an ecological approach to brain and behavior research. Perspect. Psychol. Sci. 14(5), 841–859 (2019). https://doi.org/10.1177/1745691619856350
Article
Google Scholar
Kasai, K., Fukuda, M., Yahata, N., Morita, K., Fujii, N.: The future of real-world neuroscience: Imaging techniques to assess active brains in social environments. Neurosci. Res. 90, 65–71 (2015). https://doi.org/10.1016/j.neures.2014.11.007
Article
Google Scholar
Müller, V., Lindenberger, U.: Intra- and interbrain synchrony and hyperbrain network dynamics of a guitarist quartet and its audience during a concert. Ann. N. Y. Acad. Sci. 1523(1), 74–90 (2023). https://doi.org/10.1111/nyas.14987
Article
Google Scholar
Toppi, J., et al.: Investigating cooperative behavior in ecological settings: an EEG hyperscanning study. PLOS ONE, 11(4), e0154236 (2016). https://doi.org/10.1371/journal.pone.0154236
Gugnowska, K., Novembre, G., Kohler, N., Villringer, A., Keller, P.E., Sammler, D.: Endogenous sources of interbrain synchrony in duetting pianists. Cereb. Cortex 32(18), 4110–4127 (2022)
Article
Google Scholar
Dumas, G., Nadel, J., Soussignan, R., Martinerie, J., Garnero, L.: Inter-brain synchronization during social interaction. PLoS ONE 5(8), e12166 (2010)
Article
Google Scholar
D. B. Stone et al., “Hyperscanning of Interactive Juggling: Expertise Influence on Source Level Functional Connectivity,” Front. Hum. Neurosci., vol. 13, Sep. 2019, https://doi.org/10.3389/fnhum.2019.00321
D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-world’networks,” nature, vol. 393, no. 6684, pp. 440–442, 1998
Google Scholar
Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186–198 (2009)
Article
Google Scholar
Bassett, D.S., Bullmore, E.T.: Small-world brain networks revisited. Neuroscientist 23(5), 499–516 (2017). https://doi.org/10.1177/1073858416667720
Article
Google Scholar
Martinet, L.-E., et al.: Robust dynamic community detection with applications to human brain functional networks. Nat. Commun. 11(1), 2785 (2020). https://doi.org/10.1038/s41467-020-16285-7
Article
Google Scholar
Horswill, M.S., Helman, S.: A behavioral comparison between motorcyclists and a matched group of non-motorcycling car drivers: factors influencing accident risk. Accid. Anal. Prev. 35(4), 589–597 (2003). https://doi.org/10.1016/S0001-4575(02)00039-8
Article
Google Scholar
Cao, Y., Philp, J.: Interactional context and willingness to communicate: a comparison of behavior in whole class, group and dyadic interaction. System 34(4), 480–493 (2006). https://doi.org/10.1016/j.system.2006.05.002
Article
Google Scholar
Harada, T.: Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach. PLoS ONE 16(6), e0252122 (2021). https://doi.org/10.1371/journal.pone.0252122
Article
Google Scholar
Astolfi, L., et al.: Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum. Brain Mapp. 28(2), 143–157 (2007). https://doi.org/10.1002/hbm.20263
Article
Google Scholar
Wolfe, J.D., Kirkland, S.: Dyad/Triad Studies. I: Encyclopedia of Gerontology and Population Aging, pp. 1536–1540. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-22009-9_579
Zhou, L., Zhang, D.: A comparison of deception behavior in dyad and triadic group decision making in synchronous computer-mediated communication. Small Group Res. 37(2), 140–164 (2006). https://doi.org/10.1177/1046496405285125
Article
Google Scholar
Yoon, J., Thye, S.R., Lawler, E.J.: Exchange and cohesion in dyads and triads: a test of Simmel’s hypothesis. Soc. Sci. Res. 42(6), 1457–1466 (2013). https://doi.org/10.1016/j.ssresearch.2013.06.003
Article
Google Scholar
O’Riordan, C., Sorensen, H.: Stable cooperation in the N-player prisoner’s dilemma: the importance of community structure. In: Tuyls, K., Nowe, A., Guessoum, Z., Kudenko, D., (eds.) Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning, pp. 157–168. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-77949-0_12
Newman, L.A., Cao, M., Täuber, S., van Vugt, M.K.: Mapping inter-brain synchronization results onto experimental design and analysis methods: a review on EEG hyperscanning. OSF (2024).https://doi.org/10.31234/osf.io/5vgp9
Dikker, S., et al.: Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom. Curr. Biol. 27(9), 1375–1380 (2017). https://doi.org/10.1016/j.cub.2017.04.002
Article
Google Scholar
Bevilacqua, D., et al.: Brain-to-brain synchrony and learning outcomes vary by student-teacher dynamics: evidence from a real-world classroom electroencephalography study. J. Cogn. Neurosci. 31(3), 401–411 (2019). https://doi.org/10.1162/jocn_a_01274
Article
Google Scholar
Silfwerbrand, L., Koike, Y., Nyström, P., Gingnell, M.: Directed causal effect with PCMCI in hyperscanning EEG time series. Front. Neurosci. 18 (2024). https://doi.org/10.3389/fnins.2024.1305918
Bi, X., Cui, H., Ma, Y.: Hyperscanning studies on interbrain synchrony and child development: a narrative review. Neuroscience 530, 38–45 (2023). https://doi.org/10.1016/j.neuroscience.2023.08.035
Article
Google Scholar
“Group decision-making behavior in social dilemmas: Inter-brain synchrony and the predictive role of personality traits.,” APA PsycNET. https://psycnet.apa.org/record/2020-80253-001. Accessed 25 Jan 25 2025
Blin, J.-M., Satterthwaite, M.A.: Individual decisions and group decisions: the fundamental differences. J. Public Econ. 10(2), 247–267 (1978). https://doi.org/10.1016/0047-2727(78)90037-3
Article
Google Scholar
Cason, T.N., Mui, V.: A laboratory study of group polarisation in the team dictator game. Econ. J. 107(444), 1465–1483 (1997). https://doi.org/10.1111/j.1468-0297.1997.tb00058.x
Article
Google Scholar
Cooper, D.J., Kagel, J.H.: Are two heads better than one? Team versus Individual play in signaling games. Am. Econ. Rev. 95(3), 477–509 (2005). https://doi.org/10.1257/0002828054201431
Article
Google Scholar
Camerer, C.F.: Behavioral Game Theory: Experiments in Strategic Interaction. Princeton University Press, Princeton (2011)
Google Scholar
Astolfi, L., et al.: Neuroelectrical hyperscanning measures simultaneous brain activity in humans. Brain Topogr. 23(3), 243–256 (2010). https://doi.org/10.1007/s10548-010-0147-9
Article
Google Scholar
Babiloni, F., et al.: Cortical activity and connectivity of human brain during the prisoner’s dilemma: an EEG hyperscanning study. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4953–4956 (2007).https://doi.org/10.1109/IEMBS.2007.4353452
Jahng, J., Kralik, J.D., Hwang, D.-U., Jeong, J.: Neural dynamics of two players when using nonverbal cues to gauge intentions to cooperate during the prisoner’s dilemma game. Neuroimage 157, 263–274 (2017)
Article
Google Scholar
Astolfi, L., et al.: Estimation of the cortical activity from simultaneous multi-subject recordings during the prisoner’s dilemma. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1937–1939 (2009). https://doi.org/10.1109/IEMBS.2009.5333456
De Vico Fallani, F., et al.: Defecting or not defecting: how to “read” human behavior during cooperative games by EEG measurements. PLoS ONE 5(12), e14187 (2010)
Article
Google Scholar
Cervantes Constantino, F., et al.: Neural processing of iterated prisoner’s dilemma outcomes indicates next-round choice and speed to reciprocate cooperation. Soc. Neurosci. 16(2), 103–120 (2021). https://doi.org/10.1080/17470919.2020.1859410
Article
Google Scholar
Papageorgiou, C., Karanasiou, I.S., Tsianaka, E.I., Kyprianou, M., Papadimitriou, G.N., Uzunoglu, N.K.: Motive related positivity: decision-making during a prisoners’ dilemma task. J. Integr. Neurosci. 12(02), 183–199 (2013). https://doi.org/10.1142/S0219635213500106
Article
Google Scholar
Rezaei, G., Kirley, M.: Dynamic social networks facilitate cooperation in the N<math><mi is=“true”>N</mi></math>-player prisoner’s dilemma. Phys. Stat. Mech. Appl. 391(23), 6199–6211 (2012). https://doi.org/10.1016/j.physa.2012.06.071
Article
Google Scholar
Rezaei, G., Kirley, M., Pfau, J.: Evolving cooperation in the N-player prisoner’s dilemma: a social network model. In: Korb, K., Randall, M., Hendtlass, T., (eds.) Artificial Life: Borrowing from Biology, pp. 43–52. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10427-5_5
OpenViBE: An Open-Source Software Platform to Design, Test, and Use Brain–Computer Interfaces in Real and Virtual Environments. https://ieeexplore.ieee.org/abstract/document/6797525. Accessed 25 Jan 2025
Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)
Article
Google Scholar
Essam, E.S., Elshobaky, E.M., Soliman, K.M.: Two population three-player prisoner’s dilemma game. Appl. Math. Comput. 277, 44–53 (2016). https://doi.org/10.1016/j.amc.2015.12.047
Article
MathSciNet
Google Scholar
Taha, M.A., Ghoneim, A.: Zero-determinant strategies in infinitely repeated three-player prisoner’s dilemma game. Chaos Solitons Fractals 152, 111408 (2021). https://doi.org/10.1016/j.chaos.2021.111408
Article
MathSciNet
Google Scholar
Maris, E., Oostenveld, R.: Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164(1), 177–190 (2007). https://doi.org/10.1016/j.jneumeth.2007.03.024
Article
Google Scholar
Oostenveld, R., Fries, P., Maris, E., Schoffelen, J.-M.: FieldTrip: open-source software for advanced analysis of MEG, EEG, and invasive electrophysiological data (2011). https://doi.org/10.1155/2011/156869
Guevara, M.A., Corsi-Cabrera, M.: EEG coherence or EEG correlation? Int. J. Psychophysiol. 23(3), 145–153 (1996)
Article
Google Scholar
Burgess, A.P.: On the interpretation of synchronization in EEG hyperscanning studies: a cautionary note. Front. Hum. Neurosci. 7, 881 (2013)
Article
Google Scholar
Duan, L., Dai, R.-N., Xiao, X., Sun, P.-P., Li, Z., Zhu, C.-Z.: Cluster imaging of multi-brain networks (CIMBN): a general framework for hyperscanning and modeling a group of interacting brains. Front. Neurosci. 9, 267 (2015)
Article
Google Scholar
Van Wijk, B.C.M., Stam, C.J., Daffertshofer, A.: Comparing brain networks of different size and connectivity density using graph theory. PLoS ONE 5(10), e13701 (2010). https://doi.org/10.1371/journal.pone.0013701
Article
Google Scholar
Rubinov, M., Sporns, O.: Weight-conserving characterization of complex functional brain networks. Neuroimage 56(4), 2068–2079 (2011). https://doi.org/10.1016/j.neuroimage.2011.03.069
Article
Google Scholar
Battiston, F., Nicosia, V., Latora, V.: Structural measures for multiplex networks. Phys. Rev. E 89(3), 032804 (2014). https://doi.org/10.1103/PhysRevE.89.032804
Article
Google Scholar
Muller, R.U., Stead, M., Pach, J.: The hippocampus as a cognitive graph. J. Gen. Physiol. 107(6), 663–694 (1996). https://doi.org/10.1085/jgp.107.6.663
Article
Google Scholar
Stam, C.J.: Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’ network? Neurosci. Lett. 355(1), 25–28 (2004). https://doi.org/10.1016/j.neulet.2003.10.063
Article
Google Scholar
Bassett, D.S., Meyer-Lindenberg, A., Achard, S., Duke, T., Bullmore, E.: Adaptive reconfiguration of fractal small-world human brain functional networks. Proc. Natl. Acad. Sci. 103(51), 19518–19523 (2006). https://doi.org/10.1073/pnas.0606005103
Article
Google Scholar
Achard, S., Salvador, R., Whitcher, B., Suckling, J., Bullmore, E.: A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci. 26(1), 63–72 (2006). https://doi.org/10.1523/JNEUROSCI.3874-05.2006
Article
Google Scholar
Astolfi, L., et al.: Simultaneous estimation of cortical activity during social interactions by using EEG hyperscannings. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 2814–2817 (2010).https://doi.org/10.1109/IEMBS.2010.5626555
Astolfi, L., et al.: Imaging the social brain by simultaneous hyperscanning during subject interaction. IEEE Intell. Syst. 26(5), 38–45 (2011). https://doi.org/10.1109/MIS.2011.61
Article
Google Scholar
Kawasaki, M., Kitajo, K., Yamaguchi, Y.: Dynamic links between theta executive functions and alpha storage buffers in auditory and visual working memory. https://onlinelibrary.wiley.com/doi/10.1111/j.1460-9568.2010.07217.x. Accessed 25 Jan 2025
Perry, A., et al.: Intranasal oxytocin modulates EEG mu/alpha and beta rhythms during perception of biological motion. Psychoneuroendocrinology 35(10), 1446–1453 (2010)
Article
Google Scholar
Kawasaki, M., Kitajo, K., Yamaguchi, Y.: Dynamic links between theta executive functions and alpha storage buffers in auditory and visual working memory. Eur. J. Neurosci. 31(9), 1683–1689 (2010)
Article
Google Scholar
Dumas, G., Nadel, J., Soussignan, R., Martinerie, J., Garnero, L.: Inter-brain synchronization during social interaction. PloS one 5(8), e12166 (2010)
Article
Google Scholar
Astolfi, L., et al.: Imaging the social brain by simultaneous hyperscanning during subject interaction. IEEE Intell. Syst. 26(5), 38 (2011)
Article
Google Scholar
Babiloni, C., et al.: Brains “in concert”: frontal oscillatory alpha rhythms and empathy in professional musicians. Neuroimage 60(1), 105–116 (2012)
Article
Google Scholar
Kawasaki, M., Yamada, Y., Ushiku, Y., Miyauchi, E., Yamaguchi, Y.: Inter-brain synchronization during coordination of speech rhythm in human-to-human social interaction. Sci. Rep. 3(1), 1692 (2013)
Article
Google Scholar