Cover not available
Article published In:
Interaction Studies
Vol. 20:2 (2019) ► pp.234–255

The perception of a robot partner’s effort elicits a sense of commitment to human-robot interaction

| Central European University
| University of Warwick
| Università di Genova | Istituto Italiano di Tecnologia
| Istituto Italiano di Tecnologia
| Istituto Italiano di Tecnologia
| University of Warwick

Previous research has shown that the perception that one’s partner is investing effort in a joint action can generate a sense of commitment, leading participants to persist longer despite increasing boredom. The current research extends this finding to human-robot interaction. We implemented a 2-player version of the classic snake game which became increasingly boring over the course of each round, and operationalized commitment in terms of how long participants persisted before pressing a ‘finish’ button to conclude each round. Participants were informed that they would be linked via internet with their partner, a humanoid robot. Our results reveal that participants persisted longer when they perceived what they believed to be cues of their robot partner’s effortful contribution to the joint action. This provides evidence that the perception of a robot partner’s effort can elicit a sense of commitment to human-robot interaction.

Keywords: commitment, effort, human-robot interaction, joint action
Article outline
  • 1.Introduction
  • 2.Method
    • 2.1Participants
    • 2.2Material
    • 2.3Procedure
  • 3.Results
    • 3.1Main analysis
    • 3.2Secondary analyses
      • 3.2.1ANOVA
      • 3.2.2Linear regression
  • 4.Discussion
  • 5.Conclusion
  • Acknowledgements
  • Note
  • References
Published online: 7 October 2019
DOI logo
https://doi.org/10.1075/is.18001.sze
References
Bainbridge, W. A., Hart, J., Kim, E. S., & Scassellati, B.
(2008, August). The effect of presence on human-robot interaction. In Robot and Human Interactive Communication 2008 RO-MAN 2008 The 17th IEEE International Symposium on (pp. 701–706). IEEE.Google Scholar
Breazeal, C., Brooks, A., Gray, J., Hoffman, G., Kidd, C., Lee, H., … & Mulanda, D.
(2004) Humanoid robots as cooperative partners for people. Int. Journal of Humanoid Robots, 1(2), 1–34.Google Scholar
Brooks, D. J., Begum, M., and Yanco, H. A.
(2016) “Analysis of reactions towards failures and recovery strategies for autonomous robots,” in Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2016) (New York, NY: IEEE), 487–492.Google Scholar
Chennells, M., Michael, J.
(2018) Effort and performance in a cooperative activity are boosted by the perception of a partner’s effort, Nature: Scientific Reports (2018) 81:15692 | DOI logoGoogle Scholar
Clodic, A., Cao, H., Alili, S., Montreuil, V., Alami, R., & Chatila, R.
(2009) Shary: a supervision system adapted to human-robot interaction. In Experimental Robotics (pp. 229–238). Springer Berlin/Heidelberg. DOI logoGoogle Scholar
Clodic, A., Fleury, S., Alami, R., Chatila, R., Bailly, G., Brethes, L.
, …others (2006) Rackham: An interactive robot-guide. In ROMAN 2006-the 15th IEEE International Symposium on Robot and Human Interactive Communication, Hatfield (pp. 502–509). DOI logoGoogle Scholar
Cousineau, D.
(2005) Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson’s method. Tutorials in Quantitative Methods for Psychology, 1(1), 42–45. DOI logoGoogle Scholar
Cully, A., Clune, J., Tarapore, D., & Mouret, J. B.
(2015) Robots that can adapt like animals. Nature, 521(7553), 503–507. DOI logoGoogle Scholar
DeSteno, D., Breazeal, C., Frank, R. H., Pizarro, D., Baumann, J., Dickens, L., & Lee, J. J.
(2012) Detecting the trustworthiness of novel partners in economic exchange. Psychological science, 23(12), 1549–1556. DOI logoGoogle Scholar
Dragan, A. D., Lee, K. C., & Srinivasa, S. S.
(2013, March). Legibility and predictability of robot motion. In Human-Robot Interaction (HRI), 2013 8th ACM/IEEE International Conference on (pp. 301–308). IEEE.Google Scholar
Fasola, J., and Matarić, M. J.
A socially assistive robot exercise coach for the elderly. Journal of Human-Robot Interaction 2.2 (2013): 3–32. DOI logoGoogle Scholar
Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G.
(2009) Statistical power analyses using G∗ Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. DOI logoGoogle Scholar
Feltz, D. L., Forlenza, S. T., Winn, B., & Kerr, N. L.
(2014) Cyber buddy is better than no buddy: A test of the Köhler motivation effect in exergames. GAMES FOR HEALTH: Research, Development, and Clinical Applications, 3(2), 98–105. DOI logoGoogle Scholar
Ferrari, F., Eyssel, F.
2016 Toward a Hybrid Society. In: Springer Handbook of Robotics. Springer International Publishing, p. 909–918.Google Scholar
Fischer, K.
(2011) How people talk with robots: Designing dialog to reduce user uncertainty. AI Magazine, 32(4), 31–38. DOI logoGoogle Scholar
Grigore, E. C., Eder, K., Pipe, A. G., Melhuish, C., & Leonards, U.
(2013, November). Joint action understanding improves robot-to-human object handover. In Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on (pp. 4622–4629). IEEE. DOI logoGoogle Scholar
Hamacher, A., Bianchi-Berthouze, N., Pipe, A. G., & Eder, K.
(2016, August). Believing in BERT: Using expressive communication to enhance trust and counteract operational error in physical Human-Robot Interaction. In Robot and Human Interactive Communication (RO-MAN), 2016 25th IEEE International Symposium on (pp. 493–500). IEEE.Google Scholar
Kahn Jr, P. H., Kanda, T., Ishiguro, H., Freier, N. G., Severson, R. L., Gill, B. T., … & Shen, S.
(2012) “Robovie, you’ll have to go into the closet now”: Children’s social and moral relationships with a humanoid robot. Developmental psychology, 48(2), 303. DOI logoGoogle Scholar
Lee, M. K., Kiesler, S., Forlizzi, J.
Receptionist or information 
kiosk: How do people talk with a robot? Presented at the Conference on Computer-Supported Cooperative Work, New York, 2010. DOI logo
Lee, J. J., Knox, B., Baumann, J., Breazeal, C., & DeSteno, D.
(2013) Computationally modeling interpersonal trust. Frontiers in psychology, 41, 893. DOI logoGoogle Scholar
Lenz, C., Nair, S., Rickert, M., Knoll, A., Rosel, W., Gast, J., … & Wallhoff, F.
(2008, August). Joint-action for humans and industrial robots for assembly tasks. In Robot and Human Interactive Communication, 2008. RO-MAN 2008. The 17th IEEE International Symposium on (pp. 130–135). IEEE. DOI logoGoogle Scholar
Loftus, G. R., & Masson, M. E.
(1994) Using confidence intervals in within-subject designs. Psychonomic bulletin & review, 1(4), 476–490. DOI logoGoogle Scholar
Michael, J., & Salice, A.
(2017) The Sense of Commitment in Human–Robot Interaction. International journal of social robotics, 9(5), 755–763. DOI logoGoogle Scholar
Michael, J., Sebanz, N., & Knoblich, G.
(2015) The sense of commitment: A minimal approach. Frontiers in psychology, 61 1968.Google Scholar
(2016) Observing joint action: Coordination creates commitment. Cognition, 1571, 106–113. DOI logoGoogle Scholar
Mirnig, N., Stollnberger, G., Miksch, M., Stadler, S., Giuliani, M., & Tscheligi, M.
(2017) To err is robot: How humans assess and act toward an erroneous social robot. Frontiers in Robotics and AI, 4, 21.Google Scholar
Palinko, O., Sciutti, A., Wakita, Y., Matsumoto, Y., & Sandini, G.
(2016, November). If looks could kill: Humanoid robots play a gaze-based social game with humans. In Humanoid Robots (Humanoids), 2016 IEEE-RAS 16th International Conference on (pp. 905–910). IEEE. DOI logoGoogle Scholar
Peirce, J. W.
(2007) PsychoPy – Psychophysics software in Python. Journal of Neuroscience Methods, 162(1–2), 8–13. DOI logoGoogle Scholar
Schneider, S. & Kümmert, F.
Exercising with a humanoid companion are more effective than exercising alone, 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), Cancun 2016, pp. 495–501. DOI logoGoogle Scholar
Sciutti, A., Bisio, A., Nori, F., Metta, G., Fadiga, L., & Sandini, G.
(2012) Anticipatory gaze in human-robot interactions. In Gaze in HRI from modeling to communication” workshop at the 7th ACM/IEEE international conference on human-robot interaction, Boston, Massachusetts, USA.Google Scholar
(2013) Robots can be perceived as goal-oriented agents. Interaction Studies, 14(3), 329–350. DOI logoGoogle Scholar
Sciutti, A., Bisio, A., Nori, F., Metta, G., Fadiga, L., Pozzo, T., & Sandini, G.
(2012) Measuring human-robot interaction through motor resonance. International Journal of Social Robotics, 4(3), 223–234. DOI logoGoogle Scholar
Sciutti, A., Schillingmann, L., Palinko, O., Nagai, Y., & Sandini, G.
(2015, March). A gaze-contingent dictating robot to study turn-taking. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (pp. 137–138). ACM.Google Scholar
Sebanz, N., Knoblich, G., & Prinz, W.
(2003) Representing others’ actions: Just like one’s own? Cognition, 881, B11–B21. DOI logoGoogle Scholar
Stenzel, A., Chinellato, E., Bou, M. A. T., del Pobil, Á. P., Lappe, M., & Liepelt, R.
(2012) When humanoid robots become human-like interaction partners: corepresentation of robotic actions. Journal of Experimental Psychology: Human Perception and Performance, 38(5), 1073.Google Scholar
Stulp, F., Grizou, J., Busch, B., & Lopes, M.
(2015, September). Facilitating intention prediction for humans by optimizing robot motions. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on (pp. 1249–1255). IEEE. DOI logoGoogle Scholar
Székely, M., & Michael, J.
(2018) Investing in commitment: Persistence in a joint action is enhanced by the perception of a partner’s effort. Cognition, 1741, 37–42. DOI logoGoogle Scholar
Wainer, J., Feil-Seifer, D. J., Shell, D. A., & Mataric, M. J.
(2006, September). The role of physical embodiment in human-robot interaction. In Robot and Human Interactive Communication 2006 ROMAN 2006. The 15th IEEE International Symposium on (pp. 117–122). IEEE.Google Scholar
Cited by

Cited by 6 other publications

Li, Chen, Dimitrios Chrysostomou & Hongji Yang
2022. 2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C), ► pp. 673 ff. DOI logo
Li, Chen, Dimitris Chrysostomou & Hongji Yang
2023. A speech-enabled virtual assistant for efficient human–robot interaction in industrial environments. Journal of Systems and Software 205 ► pp. 111818 ff. DOI logo
Oravec, Jo Ann
2022. Love, Sex, and Robots: Technological Shaping of Intimate Relationships. In Good Robot, Bad Robot [Social and Cultural Studies of Robots and AI, ], ► pp. 91 ff. DOI logo
Székely, Marcell & John Michael
2024. Perceiving others’ cognitive effort through movement: Path length, speed, and time. Quarterly Journal of Experimental Psychology 77:4 ► pp. 767 ff. DOI logo
Vignolo, Alessia, Henry Powell, Francesco Rea, Alessandra Sciutti, Luke Mcellin & John Michael
2022. A Humanoid Robot’s Effortful Adaptation Boosts Partners’ Commitment to an Interactive Teaching Task. ACM Transactions on Human-Robot Interaction 11:1 ► pp. 1 ff. DOI logo
Vignolo, Alessia, Alessandra Sciutti & John Michael
2020. Using Robot Adaptivity to Support Learning in Child-Robot Interaction. In Social Robotics [Lecture Notes in Computer Science, 12483], ► pp. 428 ff. DOI logo

This list is based on CrossRef data as of 31 march 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.