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: 07 October 2019
https://doi.org/10.1075/is.18001.sze
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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. Crossref logo
Vignolo, Alessia, Alessandra Sciutti & John Michael
2020.  In Social Robotics [Lecture Notes in Computer Science, 12483], ► pp. 428 ff. Crossref logo

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