Skip to main content Accessibility help
×
Home

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

  • Tom Froese (a1) (a2), Hiroyuki Iizuka (a3) and Takashi Ikegami (a1)

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.

Copyright

References

Hide All
Beer, R. D. (2000) Dynamical approaches to cognitive science. Trends in Cognitive Sciences 4(3):9199.
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.
De Jaegher, H. & Froese, T. (2009) On the role of social interaction in individual agency. Adaptive Behavior 17(5):444–60.
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.
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. 5261. Springer-Verlag.
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):4368. Available at: http://dx.doi.org/10.1080/09540090903197928.
Froese, T. & Di Paolo, E. A. (2011a) The enactive approach: Theoretical sketches from cell to society. Pragmatics and Cognition 19(1):136.
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.
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.
Froese, T. & Gallagher, S. (2010) Phenomenology and artificial life: Toward a technological supplementation of phenomenological methodology. Husserl Studies 26(2):83106.
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.
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.
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.
Ikegami, T. & Iizuka, H. (2007) Turn-taking interaction as a cooperative and co-creative process. Infant Behavior and Development 30(2):278–88.
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.
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.

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed