Theory of Mind Improves Human’s Trust in an Iterative Human-Robot Game

Martina Ruocco; Wenxuan  Mou; Angelo Cangelosi; Caroline Jay; Debora Zanatto

doi:10.1145/3472307.3484176

Theory of Mind Improves Human’s Trust in an Iterative Human-Robot Game

Martina Ruocco^*, Wenxuan Mou, Angelo Cangelosi, Caroline Jay, Debora Zanatto

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

8 Citations (Scopus)

Abstract

Trust is a critical issue in human–robot interactions as it is at the base of the establishment of solid relationships. Theory of Mind (ToM) is the cognitive skill that allows us to understand what others think and believe. Several studies in HRI and psychology suggest that trust and ToM are interdependent concepts since we trust another agent based on our representation of its actions, beliefs, and intentions. However, very few works take ToM of the robot into consideration while studying trust in HRI. In this paper, we aim to examine whether the perception of ToM abilities on a robotic agent influences human-robot trust over time in an iterative game scenario. To this end, participants played an Investment Game with a humanoid robot (Pepper) that was presented as having either low-level ToM or high-level ToM. During the game, the participants were asked to pick a sum of money to invest in the robot. The amount invested was used as the main measurement of human-robot trust. Our experimental results show that robots possessing a high-level of ToM abilities were trusted more than the robots presented with low-level ToM skills.

Original language	English
Title of host publication	HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction
Pages	227–234
Number of pages	8
ISBN (Electronic)	9781450386203
DOIs	https://doi.org/10.1145/3472307.3484176
Publication status	Published - 9 Nov 2021

Publication series

Name	HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction

Bibliographical note

Funding Information:
This material is based upon work supported by the Air Force Office of Scientific Research, USAF under Award No. FA9550-19-1-7002. The work of Debora Zanatto was funded and delivered in partnership between the Thales Group and the University of Bristol, and with the support of the UK Engineering and Physical Sciences Research Council Grant Award EP/R004757/1 entitled ‘Thales-Bristol Partnership in Hybrid Autonomous Systems Engineering (T-B PHASE)’.

Publisher Copyright:
© 2021 ACM.

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10.1145/3472307.3484176

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8 Citations
1 Conference Paper
1 Article (Academic Journal)

When Would You Trust a Robot? A Study on Trust and Theory of Mind in Human-Robot Interactions
Zanatto, D., 14 Oct 2020.
Research output: Contribution to conference › Conference Paper › peer-review

Open Access

22 Citations (Scopus)
Investigating cooperation with robotic peers
Zanatto, D., Patacchiola, M., Goslin, J. & Cangelosi, A., 20 Nov 2019, In: PLoS ONE. 14, 11, 17 p., e0225028.
Research output: Contribution to journal › Article (Academic Journal) › peer-review

Open Access
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14 Citations (Scopus)

123 Downloads (Pure)

Cite this

Ruocco, M., Mou, W., Cangelosi, A., Jay, C., & Zanatto, D. (2021). Theory of Mind Improves Human’s Trust in an Iterative Human-Robot Game. In HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction (pp. 227–234). (HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction). https://doi.org/10.1145/3472307.3484176

Ruocco, Martina ; Mou, Wenxuan ; Cangelosi, Angelo et al. / Theory of Mind Improves Human’s Trust in an Iterative Human-Robot Game. HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction. 2021. pp. 227–234 (HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction).

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abstract = "Trust is a critical issue in human–robot interactions as it is at the base of the establishment of solid relationships. Theory of Mind (ToM) is the cognitive skill that allows us to understand what others think and believe. Several studies in HRI and psychology suggest that trust and ToM are interdependent concepts since we trust another agent based on our representation of its actions, beliefs, and intentions. However, very few works take ToM of the robot into consideration while studying trust in HRI. In this paper, we aim to examine whether the perception of ToM abilities on a robotic agent influences human-robot trust over time in an iterative game scenario. To this end, participants played an Investment Game with a humanoid robot (Pepper) that was presented as having either low-level ToM or high-level ToM. During the game, the participants were asked to pick a sum of money to invest in the robot. The amount invested was used as the main measurement of human-robot trust. Our experimental results show that robots possessing a high-level of ToM abilities were trusted more than the robots presented with low-level ToM skills.",

author = "Martina Ruocco and Wenxuan Mou and Angelo Cangelosi and Caroline Jay and Debora Zanatto",

note = "Funding Information: This material is based upon work supported by the Air Force Office of Scientific Research, USAF under Award No. FA9550-19-1-7002. The work of Debora Zanatto was funded and delivered in partnership between the Thales Group and the University of Bristol, and with the support of the UK Engineering and Physical Sciences Research Council Grant Award EP/R004757/1 entitled {\textquoteleft}Thales-Bristol Partnership in Hybrid Autonomous Systems Engineering (T-B PHASE){\textquoteright}. Publisher Copyright: {\textcopyright} 2021 ACM.",

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Ruocco, M, Mou, W, Cangelosi, A, Jay, C & Zanatto, D 2021, Theory of Mind Improves Human’s Trust in an Iterative Human-Robot Game. in HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction. HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction, pp. 227–234. https://doi.org/10.1145/3472307.3484176

Theory of Mind Improves Human’s Trust in an Iterative Human-Robot Game. / Ruocco, Martina; Mou, Wenxuan ; Cangelosi, Angelo et al.
HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction. 2021. p. 227–234 (HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Theory of Mind Improves Human’s Trust in an Iterative Human-Robot Game

AU - Ruocco, Martina

AU - Mou, Wenxuan

AU - Cangelosi, Angelo

AU - Jay, Caroline

AU - Zanatto, Debora

N1 - Funding Information: This material is based upon work supported by the Air Force Office of Scientific Research, USAF under Award No. FA9550-19-1-7002. The work of Debora Zanatto was funded and delivered in partnership between the Thales Group and the University of Bristol, and with the support of the UK Engineering and Physical Sciences Research Council Grant Award EP/R004757/1 entitled ‘Thales-Bristol Partnership in Hybrid Autonomous Systems Engineering (T-B PHASE)’. Publisher Copyright: © 2021 ACM.

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N2 - Trust is a critical issue in human–robot interactions as it is at the base of the establishment of solid relationships. Theory of Mind (ToM) is the cognitive skill that allows us to understand what others think and believe. Several studies in HRI and psychology suggest that trust and ToM are interdependent concepts since we trust another agent based on our representation of its actions, beliefs, and intentions. However, very few works take ToM of the robot into consideration while studying trust in HRI. In this paper, we aim to examine whether the perception of ToM abilities on a robotic agent influences human-robot trust over time in an iterative game scenario. To this end, participants played an Investment Game with a humanoid robot (Pepper) that was presented as having either low-level ToM or high-level ToM. During the game, the participants were asked to pick a sum of money to invest in the robot. The amount invested was used as the main measurement of human-robot trust. Our experimental results show that robots possessing a high-level of ToM abilities were trusted more than the robots presented with low-level ToM skills.

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M3 - Conference Contribution (Conference Proceeding)

T3 - HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction

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BT - HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction

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Ruocco M, Mou W, Cangelosi A, Jay C, Zanatto D. Theory of Mind Improves Human’s Trust in an Iterative Human-Robot Game. In HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction. 2021. p. 227–234. (HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction). doi: 10.1145/3472307.3484176

Theory of Mind Improves Human’s Trust in an Iterative Human-Robot Game

Abstract

Publication series

Bibliographical note

Access to Document

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Research output

When Would You Trust a Robot? A Study on Trust and Theory of Mind in Human-Robot Interactions

Investigating cooperation with robotic peers

Cite this