Uncanny but not confusing: Multisite study of perceptual category confusion in the Uncanny Valley

Abstract

Android robots that are close, but imperfect, likenesses of humans can provoke negative feelings of dislike and eeriness in humans (“Uncanny Valley” effect). We investigated whether category confusion between the perceptual categories of “robot” and “human” contributes to Uncanny Valley aversion. Using a novel, validated corpus of 182 images of real robot and human faces, we precisely estimated the shape of the Uncanny Valley and the location of the perceived robot/human boundary. To implicitly measure confusion, we tracked 358 subjects’ mouse trajectories as they categorized the faces. We observed a clear Uncanny Valley and a pattern of categorization supporting a perceived categorical boundary. Yet, in contrast to predictions of the category confusion mechanism hypothesis, the Uncanny Valley and category boundary locations did not coincide, and mediation analyses further failed to support a causal role of category confusion. These results suggest category confusion does not explain the Uncanny Valley effect.