Computer Science > Human-Computer Interaction
[Submitted on 11 Apr 2024]
Title:Too good to be true: People reject free gifts from robots because they infer bad intentions
View PDF HTML (experimental)Abstract:A recent psychology study found that people sometimes reject overly generous offers from people because they imagine hidden ''phantom costs'' must be part of the transaction. Phantom costs occur when a person seems overly generous for no apparent reason. This study aims to explore whether people can imagine phantom costs when interacting with a robot. To this end, screen or physically embodied agents (human or robot) offered to people either a cookie or a cookie + \$2. Participants were then asked to make a choice whether they would accept or decline the offer. Results showed that people did perceive phantom costs in the offer + \$2 conditions when interacting with a human, but also with a robot, across both embodiment levels, leading to the characteristic behavioral effect that offering more money made people less likely to accept the offer. While people were more likely to accept offers from a robot than from a human, people more often accepted offers from humans when they were physically compared to screen embodied but were equally likely to accept the offer from a robot whether it was screen or physically embodied. This suggests that people can treat robots (and humans) as social agents with hidden intentions and knowledge, and that this influences their behavior toward them. This provides not only new insights on how people make decisions when interacting with a robot but also how robot embodiment impacts HRI research.
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
Connected Papers (What is Connected Papers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.