The Theory of Mind and Human-Robot Trust Repair
dc.contributor.author | Esterwood, Connor | |
dc.contributor.author | Robert, Lionel | |
dc.date.accessioned | 2023-06-13T23:25:43Z | |
dc.date.available | 2023-06-13T23:25:43Z | |
dc.date.issued | 2023-06-13 | |
dc.identifier.citation | Esterwood, C. and Robert, L. P. (2023). The Theory of Mind and Human-Robot Trust Repair, Scientific Reports, (accepted). | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/177007 | en |
dc.description.abstract | Nothing is perfect and robots can make as many mistakes as any human, which can lead to a decrease in trust in them. However, it is possible, for robots to repair a human’s trust in them after they have made mistakes through various trust repair strategies such as apologies, denials, and promises. Presently, the efficacy of these trust repairs in the human–robot interaction literature has been mixed. One reason for this might be that humans have different perceptions of a robot’s mind. For example, some repairs may be more effective when humans believe that robots are capable of experiencing emotion. Likewise, other repairs might be more effective when humans believe robots possess intentionality. A key element that determines these beliefs is mind perception. Therefore understanding how mind perception impacts trust repair may be vital to understanding trust repair in human–robot interaction. To investigate this, we conducted a study involving 400 participants recruited via Amazon Mechanical Turk to determine whether mind perception influenced the effectiveness of three distinct repair strategies. The study employed an online platform where the robot and participant worked in a warehouse to pick and load 10 boxes. The robot made three mistakes over the course of the task and employed either a promise, denial, or apology after each mistake. Participants then rated their trust in the robot before and after it made the mistake. Results of this study indicated that overall, individual differences in mind perception are vital considerations when seeking to implement effective apologies and denials between humans and robots. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Scientific Reports | en_US |
dc.subject | human–robot interaction | en_US |
dc.subject | trust repair | en_US |
dc.subject | trust repair strategies | en_US |
dc.subject | robot trust | en_US |
dc.subject | human-robot collaboration | en_US |
dc.subject | mind perception | en_US |
dc.subject | promise | en_US |
dc.subject | denial | en_US |
dc.subject | apology | en_US |
dc.subject | robotics | en_US |
dc.subject | Theory of Mind | en_US |
dc.subject | Human-Robot Trust Repair | en_US |
dc.subject | warehouse | en_US |
dc.subject | work collaboration | en_US |
dc.subject | Conscious Experience | en_US |
dc.subject | Intentional Agency | en_US |
dc.subject | human-machine communication | en_US |
dc.subject | expectancy violation theory | en_US |
dc.subject | explainable AI | en_US |
dc.subject | trust violations | en_US |
dc.subject | robot errors | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Artificial intelligence Trust | en_US |
dc.subject | Human-Artificial intelligence Interactions | en_US |
dc.title | The Theory of Mind and Human-Robot Trust Repair | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Information Science | |
dc.subject.hlbtoplevel | Social Sciences | |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Information, School of | en_US |
dc.contributor.affiliationum | Robotics Department | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/177007/1/Esterwood and Robert 2023 Accepted.pdf | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/177007/2/Esterwood and Robert SI 2023 Accepted.pdf | |
dc.identifier.doi | https://dx.doi.org/10.7302/7741 | |
dc.identifier.source | Scientific Reports | en_US |
dc.identifier.orcid | 0000-0002-1410-2601 | en_US |
dc.description.filedescription | Description of Esterwood and Robert 2023 Accepted.pdf : Preprint | |
dc.description.filedescription | Description of Esterwood and Robert SI 2023 Accepted.pdf : Supplementary Materials | |
dc.description.depositor | SELF | en_US |
dc.identifier.name-orcid | Robert, Lionel P.; 0000-0002-1410-2601 | en_US |
dc.working.doi | 10.7302/7741 | en_US |
dc.owningcollname | Information, School of (SI) |
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