This paper describes a between-subjects Amazon Mechanical Turk study (n = 220) that investigated how a robot's affective narrative influences its ability to elicit empathy in human observers. We first conducted a pilot study to develop and validate the robot's affective narratives. Then, in the full study, the robot used one of three different affective narrative strategies (funny, sad, neutral) while becoming less functional at its shopping task over the course of the interaction. As the functionality of the robot degraded, participants were repeatedly asked if they were willing to help the robot. The results showed that conveying a sad narrative significantly influenced the participants' willingness to help the robot throughout the interaction and determined whether participants felt empathetic toward the robot throughout the interaction. Furthermore, a higher amount of past experience with robots also increased the participants' willingness to help the robot. This work suggests that affective narratives can be useful in short-term interactions that benefit from emotional connections between humans and robots.

翻译：本文描述了一项采用被试间设计的Amazon Mechanical Turk研究（n=220），旨在探究机器人的情感叙事如何影响其引发人类观察者共情的能力。我们首先通过预研究开发和验证了机器人的情感叙事内容。随后在正式研究中，机器人在执行购物任务过程中逐步降低功能表现，同时采用三种不同的情感叙事策略（幽默型、悲伤型、中立型）之一。随着机器人功能持续退化，参与者被反复询问是否愿意帮助该机器人。结果表明，悲伤叙事策略显著影响了参与者在整个交互过程中帮助机器人的意愿，并决定了参与者是否在整个交互过程中对机器人产生共情。此外，参与者与机器人互动的过往经验越丰富，其帮助机器人的意愿也越高。本研究提示，情感叙事在需要人机间建立情感联结的短期交互场景中具有潜在应用价值。