Too good to be true: People reject free gifts from robots because they infer bad intentions

TL;DR

This study tests whether phantom costs—the suspicion that overly generous offers have hidden costs—extend from human–human to human–robot interactions. Using a 3-factor between-subjects design (Agent: Human/Robot; Offer: Cookie/Cookie+$2; Embodiment: Screen/Physical) across online and in-person settings, the authors find phantom costs in both HHI and HRI, with the effect more pronounced in screen-embodied conditions. Robots generally elicited higher acceptance in the screen embodiment and, in some conditions, greater perceived intentionality, while physical embodiment amplified distinctions for human agents. The findings support the HOSE framework in HRI, underscore the importance of transparent robot explanations, and illuminate how embodiment shapes trust, decision making, and social behaviors toward robots in everyday contexts.

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.

Figures (6)

• Figure 1: Pictures Used for Each Condition of the Screen Embodiment Part.
• Figure 2: Example of One of the Vignettes Participants Could Meet in the Screen Embodiment Part.
• Figure 3: Plot of the Probability of Accepting the Offer as a Function of the Agent, the Offer, and the Agent Embodiment.
• Figure 4: Graphs Representing Each Criteria as a Function of the Agent, the Offer, and the Embodiment.
• Figure 5: Graphs Representing the Distribution of the Likert Scales.