The Impact of a Robot Game Partner When Studying Deception During a Card Game

  • David-Octavian IacobEmail author
  • Adriana Tapus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11876)


Our previous work in detecting deception in HRI was based on research findings from the psychology of inter-human interactions. Nonetheless, these conclusions may or may not be directly applied in HRI, as humans may not behave similarly when deceiving a robot. This paper studies the differences between human physiological manifestations during a deception card game scenario when playing it with a human or a robot partner. Our results show the existence of significant differences between the participants’ skin conductance, eye openness, and head pose when playing the game with a robot partner compared to when playing the game with a human partner. These results will then be used to improve the ability of robots to detect deception in HRI.


Robotics Deception Physiology 


  1. 1.
    Iacob, D.O., Tapus, A.: First attempts in deception detection in HRI by using thermal and RGB-D cameras. In: RO-MAN (2018)Google Scholar
  2. 2.
    Duffy, B.R.: Fundamental issues in social robotics. Int. Rev. Inf. Ethics (2006) Google Scholar
  3. 3.
    Hegel, F., Gieselmann, S., Peters, A., Holthaus, P., Wrede, B.: Towards a typology of meaningful signals and cues in social robotics. In: RO-MAN (2011)Google Scholar
  4. 4.
    Krämer, N.C., von der Pütten, A., Eimler, S.: Human-agent and human-robot interaction theory: similarities to and differences from human-human interaction. In: Zacarias, M., de Oliveira, J.V. (eds.) Human-Computer Interaction: The Agency Perspective. SCI, vol. 396, pp. 215–240. Springer, Heidelberg (2012). Scholar
  5. 5.
    Feil-Seifer, D., Mataric, M.: Socially assistive robots. In: ICORR (2005)Google Scholar
  6. 6.
    Malik, N.A., Hanapiah, F.A., Rahman, R.A.A., Yussof, H.: Emergence of socially assistive robotics in rehabilitation for children with cerebral palsy: a review. Int. J. Adv. Robot. Syst. 13(3), 135 (2016)CrossRefGoogle Scholar
  7. 7.
    Mataric, M., Tapus, A., Winstein, C., Eriksson, J.: Socially assistive robotics for stroke and mild TBI rehabilitation. Adv. Technol. Rehabil. 145, 249–262 (2009)Google Scholar
  8. 8.
    Tapus, A., Mataric, M.J.: Socially assistive robotic music therapist for mantaining attention of older adults with cognitive impairments. In: ICORR (2009)Google Scholar
  9. 9.
    Matarić, M., Eriksson, J., Feil-Seifer, D., Winstein, C.: Socially assistive robotics for post-stroke rehabilitation. J. NeuroEng. Rehabil. 4(1), 5 (2007)CrossRefGoogle Scholar
  10. 10.
    Horvath, F., Reid, J.: The reliability of polygraph examiner diagnosis of truth and deception. J. Crim. Law Criminol. 62, 276 (1971)CrossRefGoogle Scholar
  11. 11.
    Gaggioli, A.: Beyond the truth machine: emerging technologies for lie detection. Cyberpsychol. Behav. Soc. Netw. 21(2), 144–144 (2018)CrossRefGoogle Scholar
  12. 12.
    Hossain, M.Z., Gedeon, T., Sankaranarayana, R.: Observer’s galvanic skin response for discriminating real from fake smiles. In: The 27th Australasian Conference on Information Systems (2016)Google Scholar
  13. 13.
    Reid, J.E.: Simulated blood pressure responses in lie-detector tests and a method for their detection. J. Crim. Law Criminol. 36, 201 (1945)Google Scholar
  14. 14.
    Poore, J., Webb, A., Hays, M.J., Trimmer, M.: Emulating sociality: a comparison study of physiological signals from human and virtual social interactions. In: Society for Cognitive and Affective Neuroscience (2012)Google Scholar
  15. 15.
    Willemse, C.J.A.M., Toet, A., van Erp, J.B.F.: Affective and behavioral responses to robot-initiated social touch: toward understanding the opportunities and limitations of physical contact in human-robot interaction. Front. ICT 4, 12 (2017)CrossRefGoogle Scholar
  16. 16.
    Agrigoroaie, R., Cruz Maya, A., Tapus, A.: “Oh! I am so sorry!”: understanding user physiological variation while spoiling a game task. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018)Google Scholar
  17. 17.
    Agrigoroaie, R., Tapus, A.: Cognitive performance and physiological response analysis. Int. J. Soc. Robot. (2019)Google Scholar
  18. 18.
    Stroop, J.: Studies of interference in serial verbal reactions. J. Exploratory Psychol. 18(6), 643 (1935)CrossRefGoogle Scholar
  19. 19.
    Eysenck, S.B.G., Eysenck, H.J., Barrett, P.: A revised version of the psychoticism scale. Pers. Individ. Differ. 6(1), 21–29 (1985)CrossRefGoogle Scholar
  20. 20.
    King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)Google Scholar
  21. 21.
    Bouma, H., Burghouts, G., den Hollander, R., et al.: Measuring cues for stand-off deception detection based on full-body non-verbal features in body-worn cameras. In: SPIE Security + Defence (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Autonomous Systems and Robotics Lab, U2IS, ENSTA Paris, Institut Polytechnique de ParisPalaiseauFrance

Personalised recommendations