Improving of Robotic Virtual Agent's errors that are accepted by reaction and human's preference
Takahiro Tsumura, Seiji Yamada
TL;DR
This study investigates how human empathy toward a robotic virtual agent (RVA) is affected when the agent makes mistakes. It uses a three-factor mixed design to test whether agent reaction and human appearance preference modulate empathy and the acceptance of errors during an online scheduling task where mistakes are deliberately introduced. The findings show that overall empathy decreases after the task, while agent reactions and color-based preferences influence tolerance of the agent's mistakes but do not increase empathy. The work provides design guidance for RVA interactions in society, suggesting that perceptual cues can shape forgiveness of errors even when empathy wanes, and points to future research comparing non-mistake scenarios and in-person settings.
Abstract
One way to improve the relationship between humans and anthropomorphic agents is to have humans empathize with the agents. In this study, we focused on a task between an agent and a human in which the agent makes a mistake. To investigate significant factors for designing a robotic agent that can promote humans empathy, we experimentally examined the hypothesis that agent reaction and human's preference affect human empathy and acceptance of the agent's mistakes. The experiment consisted of a four-condition, three-factor mixed design with agent reaction, selected agent's body color for human's preference, and pre- and post-task as factors. The results showed that agent reaction and human's preference did not affect empathy toward the agent but did allow the agent to make mistakes. It was also shown that empathy for the agent decreased when the agent made a mistake on the task. The results of this study provide a way to control impressions of the robotic virtual agent's behaviors, which are increasingly used in society.