Exploring Human-Robot Trust Through the Investment Game: An Immersive Space Mission Scenario
Proceedings of the 8th International Conference on Human-Agent Interaction (HAI '20),
pages 121--130,
doi: 10.1145/3406499.3415078
- Nov 2020
Associated documents :
As robots become more advanced and capable, developing trust is an important factor of human-robot interaction and cooperation. However, as multiple environmental and social factors can influence trust, it is important to develop more elaborate scenarios and methods to measure human-robot trust. A widely used measurement of trust in social science is the investment game. In this study, we propose a scaled-up, immersive, science fiction Human-Robot Interaction (HRI) scenario for intrinsic motivation on human-robot collaboration, built upon the investment game and aimed at adapting the investment game for human-robot trust. For this purpose, we utilise two Neuro-Inspired Companion (NICO) - robots and a projected scenery. We investigate the applicability of our space mission experiment design to measure trust and the impact of non-verbal communication. We observe a correlation of 0.43 (p = 0.02) between self-assessed trust and trust measured from the game and a positive impact of non-verbal communication on trust (p = 0.0008) and robot perception for anthropomorphism (p = 0.007) and animacy (p = 0.00002). We conclude that our scenario is an appropriate method to measure trust in human-robot interaction and also to study how non-verbal communication influences a human's trust in robots.
@InProceedings{AZBMSSVAPSW20,
author = {Arts, Emy and Zörner, Sebastian and Bhatia, Kavish and Mir, Glareh and Schmalzl, Florian and Srivastava, Ankit and Vasiljevic, Brenda and Alpay, Tayfun and Peters, Annika and Strahl, Erik and Wermter, Stefan},
title = {Exploring Human-Robot Trust Through the Investment Game: An Immersive Space Mission Scenario},
booktitle = {Proceedings of the 8th International Conference on Human-Agent Interaction (HAI '20)},
editors = {},
number = {},
volume = {},
pages = {121--130},
year = {2020},
month = {Nov},
publisher = {ACM},
doi = {10.1145/3406499.3415078},
}