1887
Volume 18, Issue 2
  • ISSN 1572-0373
  • E-ISSN: 1572-0381
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Abstract

The classical experimental methodology is ill-suited for the investigation of the behavioral and physiological correlates of natural social interactions. A new experimental approach combining a natural conversation between two persons with control conditions is proposed in this paper. Behavior, including gaze direction and speech, and physiology, including electrodermal activity, are recorded during a discussion between two participants through videoconferencing. Control for the social aspect of the interaction is provided by the use of an artificial agent and of videoed conditions. A cover story provides spurious explanations for the purpose of the experiment and for the recordings, as well as a controlled and engaging topic of discussion. Preprocessing entails transforming raw measurements into boxcar and delta functions time series indicating when a certain behaviour or physiological event is present. The preliminary analysis presented here consists in finding statistically significant differences between experimental conditions in the temporal associations between behavioral and physiological time series. Significant results validate the experimental approach and further developments including more elaborate analysis and adaptation of the paradigm to functional MRI are discussed.

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2017-12-08
2018-10-21
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References

  1. Bach, D. R. , & Friston, K. J.
    (2013) Model-based analysis of skin conductance responses: Towards causal models in psychophysiology. Psychophysiology, 50(1), 15–22. doi: 10.1111/j.1469‑8986.2012.01483.x
    https://doi.org/10.1111/j.1469-8986.2012.01483.x [Google Scholar]
  2. Benedek, M. , & Kaernbach, C.
    (2010) Decomposition of skin conductance data by means of nonnegative deconvolution. Psychophysiology, 47(4), 647–658.
    [Google Scholar]
  3. Bigi, B. , Watanabe, T. , & Prévot, L.
    (2014) Representing multimodal linguistics annotated data. Proceedings of 9th International conference on Language Resources and Evaluation (LREC) , Reykjavik (Iceland).
    [Google Scholar]
  4. Chaminade, T. , Hodgins, J. , & Kawato, M.
    (2007) Anthropomorphism influences perception of computer-animated characters' actions. Social Cognitive and Affective Neuroscience, 2(3), 206–216. doi: 10.1093/scan/nsm017
    https://doi.org/10.1093/scan/nsm017 [Google Scholar]
  5. Chaminade, T. , & Cheng, G.
    (2009) Social cognitive neuroscience and humanoid robotics. Journal of Physiology-Paris, 103(3-5), 286–295. doi: 10.1016/j.jphysparis.2009.08.011
    https://doi.org/10.1016/j.jphysparis.2009.08.011 [Google Scholar]
  6. Chaminade, T. , Rosset, D. , Fonseca, D. D. , Nazarian, B. , Lutcher, E. , Cheng, G. , & Deruelle, C.
    (2012) How do we think machines think? An fMRI study of alleged competition with an artificial intelligence. Frontiers in Human Neuroscience, 6, 103.
    [Google Scholar]
  7. Chaminade, T. , Fonseca, D. , Rosset, D. , Cheng, G. , & Deruelle, C.
    (2015) Atypical modulation of hypothalamic activity by social context in ASD. Research in Autism Spectrum Disorders, 10, 41–50. doi: 10.1016/j.rasd.2014.10.015
    https://doi.org/10.1016/j.rasd.2014.10.015 [Google Scholar]
  8. Chartrand, T. L. , & Bargh, J. A.
    (1999) The chameleon effect: The perception–behavior link and social interaction. Journal of personality and social psychology, 76(6), 893–910. doi: 10.1037/0022‑3514.76.6.893
    https://doi.org/10.1037/0022-3514.76.6.893 [Google Scholar]
  9. Dawson, M. E. , Schell, A. M. , & Filion, D. L.
    (2007) The electrodermal system. In J. T. Cacioppo , L. G. Tassinary , & G. G. Berntson (Eds.). Handbook of psychophysiology (3rd ed.; pp.159–181). Cambridge, UK: Cambridge University Press. doi: 10.1017/CBO9780511546396.007
    https://doi.org/10.1017/CBO9780511546396.007 [Google Scholar]
  10. Dennett, D. C.
    (1996) The Intentional Stance (6th printing). Cambridge, MA, USA: The MIT Press.
    [Google Scholar]
  11. Diehl, J. J. , Schmitt, L. M. , Villano, M. , & Crowell, C. R.
    (2012) The clinical use of robots for individuals with Autism Spectrum Disorders: A critical review. Research in Autism Spectrum Disorders, 6(1), 249–262. doi: 10.1016/j.rasd.2011.05.006
    https://doi.org/10.1016/j.rasd.2011.05.006 [Google Scholar]
  12. French, D. P. , & Sutton, S.
    (2010) Reactivity of measurement in health psychology: How much of a problem is it? What can be done about it?British Journal of Health Psychology, 15(3), 453–468. doi: 10.1348/135910710X492341
    https://doi.org/10.1348/135910710X492341 [Google Scholar]
  13. Frith, C. D. , & Allen, H. A.
    (1983) The skin conductance orienting response as an index of attention. Biological psychology, 17(1), 27–39. doi: 10.1016/0301‑0511(83)90064‑9
    https://doi.org/10.1016/0301-0511(83)90064-9 [Google Scholar]
  14. Hauber, J. , Regenbrecht, H. , Hills, A. , Cockburn, A. , & Billinghurst, M.
    (2005) Social presence in two-and three-dimensional videoconferencing. Proceedings of 8th Annual International Workshop on Presence , London (UK), 189–198.
    [Google Scholar]
  15. Khalfa, S. , Isabelle, P. , Jean-Pierre, B. , & Manon, R.
    (2002) Event-related skin conductance responses to musical emotions in humans. Neuroscience letters, 328(2), 145–149. doi: 10.1016/S0304‑3940(02)00462‑7
    https://doi.org/10.1016/S0304-3940(02)00462-7 [Google Scholar]
  16. Kilpatrick, D. G.
    (1972) Differential responsiveness of two electrodermal indices to psychological stress and performance of a complex cognitive task. Psychophysiology, 9(2), 218–226. doi: 10.1111/j.1469‑8986.1972.tb00756.x
    https://doi.org/10.1111/j.1469-8986.1972.tb00756.x [Google Scholar]
  17. Krach, S. , Hegel, F. , Wrede, B. , Sagerer, G. , Binkofski, F. , & Kircher, T.
    (2008) Can Machines Think? Interaction and Perspective Taking with Robots Investigated via fMRI. PLoS ONE, 3(7), e2597. doi: 10.1371/journal.pone.0002597
    https://doi.org/10.1371/journal.pone.0002597 [Google Scholar]
  18. Kuster, C. , Popa, T. , Bazin, J.-C. , Gotsman, C. , & Gross, M.
    (2012) Gaze correction for home video conferencing. ACM Transactions on Graphics, 31(6), 174. doi: 10.1145/2366145.2366193
    https://doi.org/10.1145/2366145.2366193 [Google Scholar]
  19. Laming, D. R. J.
    (1968) Information theory of choice-reaction times. London: Academic Press.
    [Google Scholar]
  20. Nomura, T. , Suzuki, T. , Kanda, T. , & Kato, K.
    (2006) Measurement of negative attitudes toward robots. Interaction Studies, 7(3), 437–454 doi: 10.1075/is.7.3.14nom
    https://doi.org/10.1075/is.7.3.14nom [Google Scholar]
  21. Ochs, M. , Niewiadomski, R. , Brunet, P. , & Pelachaud, C.
    (2012) Smiling virtual agent in social context. Cognitive Processing, 13(S2), 519–532. doi: 10.1007/s10339‑011‑0424‑x
    https://doi.org/10.1007/s10339-011-0424-x [Google Scholar]
  22. Pelachaud, C.
    (2009) Modelling multimodal expression of emotion in a virtual agent. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3539–3548. doi: 10.1098/rstb.2009.0186
    https://doi.org/10.1098/rstb.2009.0186 [Google Scholar]
  23. Pfungst, O.
    (1911) Clever Hans (The horse of Mr. von Osten): A contribution to experimental animal and human psychology (Trans. C. L. Rahn ). New York: Henry Holt. doi: 10.5962/bhl.title.56164
    https://doi.org/10.5962/bhl.title.56164 [Google Scholar]
  24. Riek, L. D.
    (2012) Wizard of oz studies in hri: A systematic review and new reporting guidelines. Journal of Human-Robot Interaction1.
    [Google Scholar]
  25. Rolison, M. J. , Naples, A. J. , & McPartland, J. C.
    (2015) Interactive Social Neuroscience to Study Autism Spectrum Disorder. The Yale Journal of Biology and Medicine, 88(1), 17–24.
    [Google Scholar]
  26. Roth, W. T. , Dawson, M. E. , & Filion, D. L.
    (2012) Publication recommendations for electrodermal measurements. Psychophysiology, 49, 1017–1034. doi: 10.1111/j.1469‑8986.2011.01292.x
    https://doi.org/10.1111/j.1469-8986.2011.01292.x [Google Scholar]
  27. Schilbach, L.
    (2010) A second-person approach to other minds. Nature Reviews Neuroscience, 11(6), 449–449. doi: 10.1038/nrn2805‑c1
    https://doi.org/10.1038/nrn2805-c1 [Google Scholar]
  28. Schilbach, L. , Timmermans, B. , Reddy, V. , Costall, A. , Bente, G. , Schlicht, T. , & Vogeley, K.
    (2013) Toward a second-person neuroscience. Behavioral And Brain Sciences, 36(4), 393–414. doi: 10.1017/S0140525X12000660
    https://doi.org/10.1017/S0140525X12000660 [Google Scholar]
  29. Senju, A. , & Johnson, M. H.
    (2009) The eye contact effect: Mechanisms and development. Trends in Cognitive Sciences, 13(3), 127–134. doi: 10.1016/j.tics.2008.11.009
    https://doi.org/10.1016/j.tics.2008.11.009 [Google Scholar]
  30. Turing, A.
    (1950), Computing machinery and intelligence. Mind, 59(236), 433–460. doi: 10.1093/mind/LIX.236.433
    https://doi.org/10.1093/mind/LIX.236.433 [Google Scholar]
  31. Wykowska, A. , Chaminade, T. , & Cheng, G.
    (2016) Embodied artificial agents for understanding human social cognition. Philosophical Transactions of the Royal Society Biological Sciences, 371(1693), 20150375. doi: 10.1098/rstb.2015.0375
    https://doi.org/10.1098/rstb.2015.0375 [Google Scholar]
  32. Xiong, X. , & De la Torre, F.
    (2013) Supervised descent method and its application to face alignment. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [Google Scholar]
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