How Facial Expressions and Small Talk May Influence Trust in a Robot

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9979)


In this study, we address the level of trust that a human being displays during an interaction with a robot under different circumstances. The influencing factors considered are the facial expressions of a robot during the interactions, as well as the ability of making small talk. To examine these influences, we ran an experiment in which a robot tells a story to a participant, and then asks for help in form of donations. The experiment was implemented in four different scenarios in order to examine the two influencing factors on trust. The results showed the highest level of trust gained when the robot starts with small talk and expresses facial expression in the same direction of storytelling expected emotion.


Human Robot Interaction Trust Social robotics Facial expression Small talk 



The authors would like to thank their colleagues in INESC-ID and GAIPS group for their help and support, especially Patrícia Alves-Oliveira, Tiago Ribeiro and Filipa Correia. The first author would like to thank National Council for Scientific and Technological Development (CNPq) program Science without Border process number: 201833/2014-0 – Brazil and University of State of Rio Grande do Norte – Brazil.


  1. 1.
    Wagner, A.R.: The role of trust and relationships in human-robot social interaction. Georgia Institute of Technology (2009)Google Scholar
  2. 2.
    Breazeal, C.: Social robots for health applications. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE (2011)Google Scholar
  3. 3.
    Broadbent, E., Stafford, R., MacDonald, B.: Acceptance of healthcare robots for the older population: review and future directions. Int. J. Soc. Robot. 1(4), 319–330 (2009). doi: 10.1007/s12369-009-0030-6 CrossRefGoogle Scholar
  4. 4.
    The Economist. Trust me, i’m a robot. In: Robotics. The Economist (2006).
  5. 5.
    Deutsch, M.: Cooperation and trust: some theoretical notes. In: Nebraska Symposium on Motivation, Nebraska, pp. 275–318 (1962)Google Scholar
  6. 6.
    Kollock, P.: The emergence of exchange structures: an experimental study of uncertainty, commitment, and trust. Am. J. Sociol. 100(2), 313–345 (1994). doi: 10.1086/230539 CrossRefGoogle Scholar
  7. 7.
    Anderson, J.R.: Concepts, propositions, and schemata: what are the cognitive units. Nebr. Symp. Motiv. 28, 121–162 (1980). DTIC DocumentGoogle Scholar
  8. 8.
    Lee, J.J., Knox, W.B., Wormwood, J.B., Breazeal, C., DeSteno, D.: Computationally modeling interpersonal trust. Front. Psychol. 4, 893 (2013). doi: 10.3389/fpsyg.2013.00893 Google Scholar
  9. 9.
    Freedy, A., DeVisser E., Weltman G., Coeyman N.: Measurement of trust in human-robot collaboration. In: International Symposium on Collaborative Technologies and Systems (CTS 2007), 25 May 2007 (2007)Google Scholar
  10. 10.
    van den Brule, R., Dotsch, R., Bijlstra, G., Wigboldus, D.H.J., Haselager, P.: Do robot performance and behavioral style affect human trust? Int. J. Soc. Robot. 6(4), 519–531 (2014). doi: 10.1007/s12369-014-0231-5 CrossRefGoogle Scholar
  11. 11.
    Youssef, K., De Silva, P.R., Okada, M.: Exploring the four social bonds evolvement for an accompanying minimally designed robot. In: Tapus, A., André, E., Martin, F., Ammi, M. (eds.) Social Robotics. LNCS, vol. 9388. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25554-5_34 CrossRefGoogle Scholar
  12. 12.
    Stanton, C., Stevens, C.J.: Robot pressure: the impact of robot eye gaze and lifelike bodily movements upon decision-making and trust. In: Beetz, M., Johnston, B., Williams, M.-A. (eds.) ICSR 2014. LNCS, vol. 8755, pp. 330–339. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-11973-1_34 Google Scholar
  13. 13.
    Kahn, P.H., Kanda T., Ishiguro H., Gill B.T., Shen S., Gary H.E., et al.: Will people keep the secret of a humanoid robot? pp. 173–80 (2015). doi: 10.1145/2696454.2696486
  14. 14.
    DeSteno, D., Breazeal, C., Frank, R.H., Pizarro, D., Baumann, J., Dickens, L., et al.: Detecting the trustworthiness of novel partners in economic exchange. Psychol. Sci. 23(12), 1549–1556 (2012). doi: 10.1177/0956797612448793 CrossRefGoogle Scholar
  15. 15.
    Bickmore, T., Cassell J.: Relational agents: a model and implementation of building user trust. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Seattle, Washington, USA, pp. 396–403, 365304. ACM (2001)Google Scholar
  16. 16.
    Li, J.: The benefit of being physically present: a survey of experimental works comparing copresent robots, telepresent robots and virtual agents. Int. J. Hum Comput Stud. 77, 23–37 (2015). doi: 10.1016/j.ijhcs.2015.01.001 CrossRefGoogle Scholar
  17. 17.
    Ribeiro, T., Pereira, A., Di Tullio, E., Paiva, A.: The SERA ecosystem: socially expressive robotics architecture for autonomous human-robot interaction. In: AAAI Spring Symposium Series (2016)Google Scholar
  18. 18.
    Ribeiro, T., Pereira, A., Di Tullio, E., Alves-Oliveira P., Paiva A.: From thalamus to skene: high-level behaviour planning and managing for mixed-reality characters. In: Intelligent Virtual Agents - Workshop on Architectures and Standards for IVAs (2014)Google Scholar
  19. 19.
    Ribeiro, T., Paiva A., Dooley D.: Nutty tracks: symbolic animation pipeline for expressive robotics. In: ACM SIGGRAPH, p. 1 (2013)Google Scholar
  20. 20.
    Ekman, P., Friesen, W.: Facial action coding system: a technique for the measurement of facial movement. Consulting Psychologists Press, Palo Alto (1978)Google Scholar
  21. 21.
    Kintz, B.L., Delprato, D.J., Mettee, D.R., Persons, C.E., Schappe, R.H.: The experimenter effect. Psychol. Bull. 63(4), 223–232 (1965). doi: 10.1037/h0021718 CrossRefGoogle Scholar
  22. 22.
    Schaefer, K.: The perception and measurement of human-robot trust. University of Central Florida, Orlando (2013)Google Scholar
  23. 23.
    Rau, P.L.P., Li, Y., Li, D.: Effects of communication style and culture on ability to accept recommendations from robots. Comput. Hum. Behav. 25(2), 587–595 (2009). doi: 10.1016/j.chb.2008.12.025 CrossRefGoogle Scholar
  24. 24.
    Araújo, A., Giné, E.: The Central Limit Theorem for Real and Banach Valued Random Variables. Wiley, New York (1980)zbMATHGoogle Scholar
  25. 25.
    Myers, I.B., McCaulley, M.H.: Manual, A Guide to the Development and Use of the Myers-Briggs Type Indicator. Consulting Psychologists Press, Palo Alto (1985)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.INESC-ID & Instituto Superior TécnicoUniversity of LisbonLisbonPortugal

Personalised recommendations