The Impact of a Robot Game Partner When Studying Deception During a Card Game
Abstract
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.
Keywords
Robotics Deception PhysiologyReferences
- 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.Duffy, B.R.: Fundamental issues in social robotics. Int. Rev. Inf. Ethics (2006) Google Scholar
- 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.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). https://doi.org/10.1007/978-3-642-25691-2_9CrossRefGoogle Scholar
- 5.Feil-Seifer, D., Mataric, M.: Socially assistive robots. In: ICORR (2005)Google Scholar
- 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.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.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.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.Horvath, F., Reid, J.: The reliability of polygraph examiner diagnosis of truth and deception. J. Crim. Law Criminol. 62, 276 (1971)CrossRefGoogle Scholar
- 11.Gaggioli, A.: Beyond the truth machine: emerging technologies for lie detection. Cyberpsychol. Behav. Soc. Netw. 21(2), 144–144 (2018)CrossRefGoogle Scholar
- 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.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.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.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.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.Agrigoroaie, R., Tapus, A.: Cognitive performance and physiological response analysis. Int. J. Soc. Robot. (2019)Google Scholar
- 18.Stroop, J.: Studies of interference in serial verbal reactions. J. Exploratory Psychol. 18(6), 643 (1935)CrossRefGoogle Scholar
- 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.King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)Google Scholar
- 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