Does Trait Loneliness Predict Rejection of Social Robots? The Role of Reduced Attributions of Unique Humanness : Exploring the Effects of Trait Loneliness on Anthropomorphism and Acceptance of Social Robots

摘要

Since chronic loneliness is both a painful individual experience and an increasingly serious social problem, robot companions have emerged as a result of robotization of social work to confront this issue. We foresee that social robots will become pervasive in the near future. Thus, it is crucial to pinpoint the relationship between chronic experiences of loneliness (i.e., trait loneliness) and both anthropomorphism and acceptance of such artificial intelligent agents. Previous research demonstrated that experimentally induced state loneliness increases anthropomorphic inferences about nonhuman agents such as pets. However, in the present research we found that trait (vs. state) loneliness — a permanent personality disposition that is not easily relieved (vs. transitory experiences caused by circumstance, and easily relieved) — reduced participants’ anthropomorphic tendencies and acceptance of a social robot (regardless of the form: a picture of the robot, an on-site robot, or direct interaction with the robot). In particular, believing that the robot lacks good “unique humanness” traits (i.e., Humble, Thorough, Organized, Broadminded, and Polite) is one reason why dispositionally lonely participants are less likely to anthropomorphize a robot, which further prompts reduced acceptance of it. This finding suggests that unique humanness, exemplifying secondary emotions, is vital, not only in interpersonal contexts, but in establishing connections with social robots. ACM Reference format: Sijia Li, Liying Xu, Feng Yu & Kaiping Peng. 2020. Trait Loneliness Predict Rejection of Social Robots? The Role of Reduced Attribution of Unique Humanness: Exploring the Effects of Trait Loneliness on Anthropomorphism and Acceptance of Social Robots. In Proceedings of 2020 ACM Human Robot Interaction (HRI’20), March 23-26, 2020, Cambridge, UK. ACM, NY, NY, USA. 8 pages. https://doi.org/10.1145/3319502.3374777