Abstract
Interpersonal synchrony usually means that people mutually adapt their behavior to each other over time. Such behavioral adaptivity is assumed to be driven by some form of subjective internal synchrony detection. In contrast to objective synchrony detection by an external (third-party) observer, subjective synchrony detection relies solely on information that is perceived by each of the synchronizing persons. Simultaneous actions of the two persons in principle cannot be sensed instantaneously by one of the two persons, but will involve time lags. These time lags reflect the time differences between a person’s own actions and the sensing of the actions of the other person. In the computational agent model described in this paper, we explore the role of time lags in different types of subjective synchrony detection and its involvement in behavioral adaptivity. Multiple simulation experiments show expected types of patterns of subjective time-lagged synchrony detection and related behavioral adaptivity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abraham, W.C., Bear, M.F.: Metaplasticity: the plasticity of synaptic plasticity. Trends Neurosci. 19(4), 126–130 (1996)
Accetto, M., Treur, J., Villa, V.: An adaptive cognitive-social model for mirroring and social bonding during synchronous joint action. Proc. BICA 2018. Procedia Computer Science, vol. 145, pp. 3–12 (2018)
Altmann, U.: Investigation of movement synchrony using windowed cross-lagged regression. In: Esposito, A., Vinciarelli, A., Vicsi, K., Pelachaud, C., Nijholt, A. (eds.) Analysis of verbal and nonverbal communication and enactment. The processing issues. LNCS, vol. 6800, pp. 335–345. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25775-9_31
Behrens, F., Moulder, R.G., Boker, S.M., Kret, M.E.: Quantifying physiological synchrony through windowed cross-correlation analysis: Statistical and theoretical considerations. BioRxiv (2020)
Boot, N., Baas, M., Van Gaal, S., Cools, R., De Dreu, C.K.W.: Creative cognition and dopaminergic modulation of fronto-striatal networks: Integrative review and research agenda. Neurosci. Biobehav. Rev. 78, 13–23 (2017)
Chandra, N., Barkai, E.: A non-synaptic mechanism of complex learning: modulation of intrinsic neuronal excitability. Neurobiol. Learn. Mem. 154, 30–36 (2018)
Damasio, A.R.: The feeling of what happens: Body and emotion in the making of consciousness. Houghton Mifflin Harcourt (1999)
Debanne, D., Inglebert, Y., Russier, M.: Plasticity of intrinsic neuronal excitability. Curr. Opin. Neurobiol. 54, 73–82 (2019)
Dhamala, M., Assisi, C.G., Jirsa, V.K., Steinberg, F.L., Kelso, J.A.S.: Multisensory integration for timing engages different brain networks. NeuroImage 34, 764–773 (2007)
Fairhurst, M.T., Janata, P., Keller, P.E.: Being and feeling in sync with an adaptive virtual partner: Brain mechanisms underlying dynamic cooperativity. Cereb. Cortex 23(11), 2592–2600 (2013). https://doi.org/10.1093/cercor/bhs243
Grandjean, D., Sander, D., Scherer, K.R.: Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization. Conscious. Cogn. 17(2), 484–495 (2008)
Hebb, D.O.: The organization of behavior: A neuropsychological theory. John Wiley and Sons, New York (1949)
Hendrikse, S.C.F., Kluiver, S., Treur, J., Wilderjans, T.F., Dikker, S., Koole, S.L.: How virtual agents can learn to synchronize: an adaptive joint decision-making model of psychotherapy. Proceedings of BICA 2021 (2022a, to appear)
Hendrikse, S.C.F., Treur, J., Wilderjans, T.F., Dikker, S., Koole, S.L.: On the same wavelengths: emergence of multiple synchronies among multiple agents. In: Van Dam, K.H., Verstaevel, N. (eds.) MABS 2021. LNCS (LNAI), vol. 13128, pp. 57–71. Springer, Cham (2022b). https://doi.org/10.1007/978-3-030-94548-0_5
Hendrikse, S.C.F., Treur, J., Wilderjans, T.F., Dikker, S., Koole, S.L.: A second-order multi-adaptive neural agent model for synchrony-related short- and long-term affiliation. In: Proceedings of the 18th International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022. Advances in Information and Communication Technology, Springer Nature (2022c, in press)
Hesslow, G.: Conscious thought as simulation of behaviour and perception. Trends Cogn. Sci. 6, 242–247 (2002)
Hove, M.J., Risen, J.L.: It’s all in the timing: interpersonal synchrony increases affiliation. Soc. Cogn. 27(6), 949–960 (2009)
Hu, Yin., Cheng, X., Pan,Y., Hu, Yi.: The intrapersonal and interpersonal consequences of interpersonal synchrony. Acta Psychol. 224, e103513 (2022)
Izawa, J., Rane, T., Donchin, O., Shadmehr, R.: Motor adaptation as a process of reoptimization. J. Neurosci.: Offic. J. Soc. Neurosci. 28(11), 2883–2891 (2008). https://doi.org/10.1523/JNEUROSCI.5359-07.2008
Keller, P.E., Novembre, G., Hove, M.J.: Rhythm in joint action: psychological and neurophysiological mechanisms for real-time interpersonal coordination. Philosoph. Trans. Roy. Soc. London, Ser. B. Biol. Sci. 369(1658), Article 20130394 (2014)
Kirschner, S., Tomasello, M.: Joint music making promotes prosocial behavior in 4-year-old children. Evol. Hum. Behav. 31, 354–364 (2010)
Koole, S.L., Tschacher, W.: Synchrony in psychotherapy: a review and an integrative framework for the therapeutic alliance. Front. Psychol. 7, 862 (2016)
Koole, S.L., Tschacher, W., Butler, E., Dikker, S., Wilderjans, T.F.: In sync with your shrink. In: Forgas, J.P., Crano, W.D., Fiedler, K. (eds.) Applications of Social Psychology, pp. 161–184. Taylor and Francis, Milton Park (2020)
Pacherie, E.: The phenomenology of joint action: self-agency vs. joint-agency. In: A. Seeman (ed.), Joint Attention: New Developments, pp. 343–389. MIT Press (2012)
Palumbo, R.V., et al.: Interpersonal autonomic physiology: a systematic review of the literature. Pers. Soc. Psychol. Rev. 21(2), 99–141 (2017)
Prince, K., Brown, S.: Neural correlates of partnered interaction as revealed by cross-domain ALE meta-analysis. Psychol. Neurosci. 15(1), 1–13 (2022)
Ramseyer, F., Tschacher, W.: Nonverbal synchrony in psychotherapy: coordinated body movement reflects relationship quality and outcome. J. Consult. Clin. Psychol. 79, 284–295 (2011)
Rayner, K., Smith, T.J., Malcolm, G.L., Henderson, J.M.: Eye movements and visual encoding during scene perception. Psychol. Sci. 20(1), 6–10 (2009)
Robinson, B.L., Harper, N.S., McAlpine, D.: Meta-adaptation in the auditory midbrain under cortical influence. Nat. Commun. 7, e13442 (2016)
Schoenherr, D., et al.: Identification of movement synchrony: validation of windowed cross-lagged correlation and -regression with peak-picking algorithm. PLoS ONE 14(2), e0211494 (2019). https://doi.org/10.1371/journal.pone.0211494
Schoenherr, D., et al.: Quantification of nonverbal synchrony using linear time series analysis methods: lack of convergent validity and evidence for facets of synchrony. Behav. Res. Methods 51(1), 361–383 (2018). https://doi.org/10.3758/s13428-018-1139-z
Sharpley, C.F., Halat, J., Rabinowicz, T., Weiland, B., Stafford, J.: Standard posture, postural mirroring and client-perceived rapport. Couns. Psychol. Q. 14, 267–280 (2001). https://doi.org/10.1080/09515070110088843
Shatz, C.J.: The developing brain. Sci. Am. 267, 60–67 (1992)
Shelton, J., Kumar, G.: Comparison between auditory and visual simple reaction times. Neurosci. Med. 1(1), 30–32 (2010)
Tarr, B., Launay, J., Dunbar, R.I.M.: Silent disco: dancing in synchrony leads to elevated pain thresholds and social closeness. Evol. Hum. Beh. 37(5), 343–349 (2016)
Thompson, J.J., Blair, M.R., Henrey, A.J.: Over the hill at 24: persistent age-related cognitive-motor decline in reaction times in an ecologically valid video game task begins in early adulthood. PLoS ONE 9(4), e94215 (2014)
Treur, J.: Network-oriented modeling for adaptive networks: designing higher-order adaptive biological, mental and social network models. SSDC, vol. 251. Springer, Cham (2020a). https://doi.org/10.1007/978-3-030-31445-3
Treur, J.: Modeling multi-order adaptive processes by self-modeling networks (Keynote Speech). In: Tallón-Ballesteros, A.J., Chen, C.-H. (eds.) Proceedings of the 2nd International Conference on Machine Learning and Intelligent Systems, MLIS 2020. Frontiers in Artificial Intelligence and Applications, vol. 332, p. 206–217. IOS Press (2020b)
Treur, J.: On the dynamics and adaptivity of mental processes: relating adaptive dynamical systems and self-modeling network models by mathematical analysis. Cogn. Syst. Res. 70, 93–100 (2021)
Tschacher, W., Ramseyer, F., Koole, S.L.: Sharing the now in the social present: Duration of nonverbal synchrony is linked with personality. J. Pers. 86(2), 129–138 (2018)
Valdesolo, P., DeSteno, D.: Synchrony and the social tuning of compassion. Emotion 11, 262 (2011). https://doi.org/10.1037/a0021302
Valdesolo, P., Ouyang, J., DeSteno, D.: The rhythm of joint action: Synchrony promotes cooperative ability. J. Exp. Soc. Psychol. 46(4), 693–695 (2010)
Wiltermuth, S.S., Heath, C.: Synchrony and cooperation. Psychol. Sci. 20(1), 1–5 (2009)
Zhang, A., Li, X., Gao, Y., Niu, Y.: Event-Driven Intrinsic Plasticity for Spiking Convolutional Neural Networks.IEEE Trans. Neural Netw. Learn. Syst. (2021). https://doi.org/10.1109/tnnls.2021.3084955
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Hendrikse, S.C.F., Treur, J., Wilderjans, T.F., Dikker, S., Koole, S.L. (2022). Becoming Attuned to Each Other Over Time: A Computational Neural Agent Model for the Role of Time Lags in Subjective Synchrony Detection and Related Behavioral Adaptivity. In: Mahmud, M., He, J., Vassanelli, S., van Zundert, A., Zhong, N. (eds) Brain Informatics. BI 2022. Lecture Notes in Computer Science(), vol 13406. Springer, Cham. https://doi.org/10.1007/978-3-031-15037-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-15037-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15036-4
Online ISBN: 978-3-031-15037-1
eBook Packages: Computer ScienceComputer Science (R0)