Hostname: page-component-6766d58669-88psn Total loading time: 0 Render date: 2026-05-19T11:35:34.271Z Has data issue: false hasContentIssue false
  • English
  • Français

From synthetic modeling of social interaction to dynamic theories of brain–body–environment–body–brain systems

Published online by Cambridge University Press:  25 July 2013

Tom Froese ,
Hiroyuki Iizuka  and
Takashi Ikegami
Tom Froese
Affiliation:
Ikegami Laboratory, Department of General Systems Studies, Graduate School of Arts and Sciences, University of Tokyo, Tokyo 153 8902, Japan. t.froese@gmail.com http://froese.wordpress.com ikeg@sacral.c.u-tokyo.ac.jp http://sacral.c.u-tokyo.ac.jp/index.html Departamento de Ciencias de la Computación, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Apdo. 20-726, 01000 Mexico D.F., Mexico
Hiroyuki Iizuka
Affiliation:
Department of Bioinformatic Engineering, Human Information Engineering Laboratory, Graduate School of Information Science and Technology, University of Osaka, Osaka 565-0871, Japan. iizuka@ist.osaka-u.ac.jp http://www-hiel.ist.osaka-u.ac.jp/~iizuka/Hiroyuki_Iizuka.html
Takashi Ikegami
Affiliation:
Ikegami Laboratory, Department of General Systems Studies, Graduate School of Arts and Sciences, University of Tokyo, Tokyo 153 8902, Japan. t.froese@gmail.com http://froese.wordpress.com ikeg@sacral.c.u-tokyo.ac.jp http://sacral.c.u-tokyo.ac.jp/index.html
Get access Rights & Permissions [Opens in a new window]

Abstract

Synthetic approaches to social interaction support the development of a second-person neuroscience. Agent-based models and psychological experiments can be related in a mutually informing manner. Models have the advantage of making the nonlinear brain–body–environment–body–brain system as a whole accessible to analysis by dynamical systems theory. We highlight some general principles of how social interaction can partially constitute an individual's behavior.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 4 , August 2013 , pp. 420 - 421
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Beer, R. D. (2000) Dynamical approaches to cognitive science. Trends in Cognitive Sciences 4(3):91–99.Google ScholarPubMed
De Jaegher, H., Di Paolo, E. & Gallagher, S. (2010) Can social interaction constitute social cognition? Trends in Cognitive Sciences 14(10):441–47. Available at: http://dx.doi.org/10.1016/j.tics.2010.06.009.CrossRefGoogle ScholarPubMed
De Jaegher, H. & Froese, T. (2009) On the role of social interaction in individual agency. Adaptive Behavior 17(5):444–60.CrossRefGoogle Scholar
Di Paolo, E. A., Rohde, M. & Iizuka, H. (2008) Sensitivity to social contingency or stability of interaction? Modelling the dynamics of perceptual crossing. New Ideas in Psychology 26(2):278–94.Google Scholar
Froese, T. & Di Paolo, E. A. (2008) Stability of coordination requires mutuality of interaction in a model of embodied agents. In: From animals to animats 10: 10th International Conference on Simulation of Adaptive Behavior, SAB 2008, ed. Asada, M., Hallam, J. C. T., Meyer, J.-A. & Tani, J., pp. 52–61. Springer-Verlag.Google Scholar
Froese, T. & Di Paolo, E. A. (2010) Modeling social interaction as perceptual crossing: An investigation into the dynamics of the interaction process. Connection Science 22(1):43–68. Available at: http://dx.doi.org/10.1080/09540090903197928.Google Scholar
Froese, T. & Di Paolo, E. A. (2011a) The enactive approach: Theoretical sketches from cell to society. Pragmatics and Cognition 19(1):1–36.Google Scholar
Froese, T. & Di Paolo, E. A. (2011b) Toward minimally social behavior: Social psychology meets evolutionary robotics. In: Advances in artificial life: Darwin meets von Neumann. 10th European Conference, ECAL 2009, ed. Kampis, G., Karsai, I. & Szathmáry, E., pp. 426–33. Springer-Verlag.Google Scholar
Froese, T. & Fuchs, T. (2012) The extended body: A case study in the neurophenomenology of social interaction. Phenomenology and the Cognitive Sciences 11(2):205–35.Google Scholar
Froese, T. & Gallagher, S. (2010) Phenomenology and artificial life: Toward a technological supplementation of phenomenological methodology. Husserl Studies 26(2):83–106.CrossRefGoogle Scholar
Froese, T. & Gallagher, S. (2012) Getting interaction theory (IT) together: Integrating developmental, phenomenological, enactive, and dynamical approaches to social interaction. Interaction Studies 13(3):436–68.CrossRefGoogle Scholar
Froese, T., Lenay, C. & Ikegami, T. (2012) Imitation by social interaction? Analysis of a minimal agent-based model of the correspondence problem. Frontiers in Human Neuroscience 6:202. doi: 10.3389/fnhum.2012.00202.CrossRefGoogle ScholarPubMed
Iizuka, H. & Di Paolo, E. A. (2007) Minimal agency detection of embodied agents. In: Advances in artificial life: 9th European Conference, ECAL 2007, ed. Almeida e Costa, F., Rocha, L. M., Costa, E., Harvey, I. & Coutinho, A., pp. 485–94. Springer-Verlag.Google Scholar
Ikegami, T. & Iizuka, H. (2007) Turn-taking interaction as a cooperative and co-creative process. Infant Behavior and Development 30(2):278–88.Google Scholar
Murray, L. & Trevarthen, C. (1985) Emotional regulation of interactions between two-month-olds and their mothers. In: Social perception in infants, ed. Field, T. M. & Fox, N. A., pp. 177–97. Ablex.Google Scholar
Quinn, M., Smith, C., Mayley, G.. & Husbands, P. (2003) Evolving controllers for a homogeneous system of physical robots: Structured cooperation with minimal sensors. Philosophical Transactions of the Royal Society of London, A: Mathematical, Physical and Engineering Sciences 361(1811):2321–43.CrossRefGoogle ScholarPubMed