Blurring Human–Machine Distinctions: Anthropomorphic Appearance in Social Robots as a Threat to Human Distinctiveness

最佳显著性理论 机器人 机器人学 感知 归属 心理学 恐怖谷理论 身份(音乐) 社会心理学 认知心理学 相似性(几何) 社交机器人 人工智能 人机交互 计算机科学 移动机器人 机器人控制 声学 图像(数学) 物理 神经科学
作者
Francesco Ferrari,Maria Paola Paladino,Jolanda Jetten
出处
期刊:International Journal of Social Robotics [Springer Science+Business Media]
卷期号:8 (2): 287-302 被引量:297
标识
DOI:10.1007/s12369-016-0338-y
摘要

The present research aims at gaining a better insight on the psychological barriers to the introduction of social robots in society at large. Based on social psychological research on intergroup distinctiveness, we suggested that concerns toward this technology are related to how we define and defend our human identity. A threat to distinctiveness hypothesis was advanced. We predicted that too much perceived similarity between social robots and humans triggers concerns about the negative impact of this technology on humans, as a group, and their identity more generally because similarity blurs category boundaries, undermining human uniqueness. Focusing on the appearance of robots, in two studies we tested the validity of this hypothesis. In both studies, participants were presented with pictures of three types of robots that differed in their anthropomorphic appearance varying from no resemblance to humans (mechanical robots), to some body shape resemblance (biped humanoids) to a perfect copy of human body (androids). Androids raised the highest concerns for the potential damage to humans, followed by humanoids and then mechanical robots. In Study 1, we further demonstrated that robot anthropomorphic appearance (and not the attribution of mind and human nature) was responsible for the perceived damage that the robot could cause. In Study 2, we gained a clearer insight in the processes underlying this effect by showing that androids were also judged as most threatening to the human–robot distinction and that this perception was responsible for the higher perceived damage to humans. Implications of these findings for social robotics are discussed.
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