Advertisement

Individual Differences in Attitude Toward Robots Predict Behavior in Human-Robot Interaction

  • Nina-Alisa Hinz
  • Francesca CiardoEmail author
  • Agnieszka Wykowska
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11876)

Abstract

Humans are influenced by the presence of other social agents, sometimes performing better, sometimes performing worse than alone. Humans are also affected by how they perceive the social agent. The present study investigated whether individual differences in the attitude toward robots can predict human behavior in human-robot interaction. Therefore, adult participants played a game with the Cozmo robot (Anki Inc., San Francisco), in which their task was to stop a balloon from exploding. In individual trials, only the participants could stop the balloon inflating, while in joint trials also Cozmo could stop it. Results showed that in joint trials, the balloon exploded less often than in individual trials. However participants stopped the balloon earlier in joint than in individual trials, although this was less beneficial for them. This effect of Cozmo joining the game, nevertheless, was influenced by the negative attitude of the participants toward robots. The more negative they were, the less their behavior was influenced by the presence of the robot. This suggests that robots can influence human behavior, although this influence is modulated by the attitude toward the robot.

Keywords

Individual differences Attitude toward robots Human-robot interaction 

Notes

Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant awarded to AW, titled “InStance: Intentional Stance for Social Attunement.” G.A. No: ERC-2016-StG-715058).

References

  1. 1.
    Flughafen München GmbH: Hi! I’m Josie Pepper. https://www.munich-airport.com/hi-i-m-josie-pepper-3613413
  2. 2.
    Kachouie, R., Sedighadeli, S., Khosla, R., Chu, M.-T.: Socially assistive robots in elderly care. A mixed-method systematic literature review. Int. J. Hum.-Comput. Interact. 30, 369–393 (2014)CrossRefGoogle Scholar
  3. 3.
    Innovative human-robot cooperation in BMW Group Production, Munich, Germany (2013)Google Scholar
  4. 4.
    Zajonc, R.B.: Social facilitation. Science 149, 269–274 (1965)CrossRefGoogle Scholar
  5. 5.
    Myers, D.G., DeWall, C.N.: Psychology (2018)Google Scholar
  6. 6.
    Ciardo, F., Wykowska, A.: Response coordination emerges in cooperative but not competitive joint task. Front. Psychol. 9, 1919 (2018)CrossRefGoogle Scholar
  7. 7.
    Park, S., Catrambone, R.: Social facilitation effects of virtual humans. Hum. Factors 49, 1054–1060 (2007)CrossRefGoogle Scholar
  8. 8.
    Riether, N., Hegel, F., Wrede, B., Horstmann, G.: Social facilitation with social robots? In: Proceedings of the Seventh Annual ACM/IEE International Conference on Human-Robot-Interaction, pp. 41–48 (2012)Google Scholar
  9. 9.
    Kompatsiari, K., Ciardo, F., Tikhanoff, V., Metta, G., Wykowska, A.: On the role of eye contact in gaze cueing. Sci. Rep. 8, 17842 (2018)CrossRefGoogle Scholar
  10. 10.
    Hertz, N., Wiese, E.: Social facilitation with non-human agents. Possible or not? In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 61, pp. 222–225 (2017)CrossRefGoogle Scholar
  11. 11.
    Schellen, E., Wykowska, A.: Intentional mindset toward robots—open questions and methodological challenges. Front. Robot. AI 5, 71 (2019)CrossRefGoogle Scholar
  12. 12.
    Marchesi, S., Ghiglino, D., Ciardo, F., Perez-Osorio, J., Baykara, E., Wykowska, A.: Do we adopt the intentional stance toward humanoid robots? Front. Psychol. 10, 450 (2019)CrossRefGoogle Scholar
  13. 13.
    Syrdal, D.S., Dautenhahn, K., Kony, K.L., Walters, M.L.: The negative attitudes towards robots scale and reactions to robot behaviour in a live human-robot interaction study. In: Adaptive and Emergent Behaviour and Complex Systems (2009)Google Scholar
  14. 14.
    Waytz, A., Cacioppo, J., Epley, N.: Who sees human? The stability and importance of individual differences in anthropomorphism. Perspect. Psychol. Sci. 5, 219–232 (2010)CrossRefGoogle Scholar
  15. 15.
    MacDorman, K.F., Entezari, S.O.: Individual differences predict sensitivity to the uncanny valley. IS 16, 141–172 (2015)CrossRefGoogle Scholar
  16. 16.
    Rossi, S., Staffa, M., Bove, L., Capasso, R., Ercolano, G.: User’s personality and activity influence on HRI comfortable distances. In: Kheddar, A., et al. (eds.) ICSR 2017. LNCS, vol. 10652, pp. 167–177. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70022-9_17CrossRefGoogle Scholar
  17. 17.
    Rossi, S., Santangelo, G., Staffa, M., Varrasi, S., Conti, D., Di Nuovo, A.: Psychometric evaluation supported by a social robot. Personality factors and technology acceptance. In: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 802–807. IEEE (2018)Google Scholar
  18. 18.
    Anki Inc.: Android Debug Bridge (2016)Google Scholar
  19. 19.
    Mathôt, S., Schreij, D., Theeuwes, J.: OpenSesame. An open-source, graphical experiment builder for the social sciences. Behav. Res. 44, 314–324 (2012)CrossRefGoogle Scholar
  20. 20.
    Nomura, T., Sugimoto, K., Syrdal, D.S., Dautenhahn, K.: Social acceptance of humanoid robots in Japan. A survey for development of the frankenstein syndrome questionnaire. In: 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pp. 242–247 (2012)Google Scholar
  21. 21.
    Syrdal, D.S., Nomura, T., Dautenhahn, K.: The frankenstein syndrome questionnaire – results from a quantitative cross-cultural survey. In: Herrmann, G., Pearson, M.J., Lenz, A., Bremner, P., Spiers, A., Leonards, U. (eds.) ICSR 2013. LNCS (LNAI), vol. 8239, pp. 270–279. Springer, Cham (2013).  https://doi.org/10.1007/978-3-319-02675-6_27CrossRefGoogle Scholar
  22. 22.
    Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J.: The robotic social attributes scale (RoSAS). In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 254–262 (2017)Google Scholar
  23. 23.
    Lejuez, C.W., et al.: Evaluation of a behavioral measure of risk taking. The balloon analogue risk task (BART). J. Exp. Psychol.: Appl. 8, 75–84 (2002)Google Scholar
  24. 24.
    R Core Team: R: A language and environment for statistical computing, Vienna (2018)Google Scholar
  25. 25.
    Wickham, H.: ggplot2: Elegant Graphics for Data Analysis. Springer, New York (2016).  https://doi.org/10.1007/978-3-319-24277-4CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.General and Experimental PsychologyLudwig-Maximilians-UniversityMunichGermany
  2. 2.Social Cognition in Human-Robot InteractionItalian Institute of TechnologyGenoaItaly

Personalised recommendations