It matters to me if you are human: Categorical perception in human and nonhuman agent spectra
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Description: Humanlike but not perfectly human agents frequently evoke feelings of eeriness, a phenomenon termed the Uncanny Valley (UV). The Categorical Perception Hypothesis proposes that effects associated with the UV are due to uncertainty as to whether to categorize agents falling into the valley as “human” or “nonhuman”. However, since UV studies have traditionally looked at agents of varying human-likeness, it remains unclear whether UV-related effects are due to categorical uncertainty in general or are specifically evoked by categorizations that require decisions regarding an agent’s human-likeness. Here, we used mouse tracking to determine whether agent spectra with (i.e., robot-human) and without (i.e., robot-animal and robot-stuffed animal) a human endpoint cause phenomena related to categorical perception to comparable extents. Specifically, we compared human and nonhuman agent spectra with respect to existence and location of a category boundary (H1-1 and H2-1), as well as the magnitude of cognitive conflict around the boundary (H1-2 and H2-2). The results show that human and nonhuman spectra exhibit category boundaries (H1-1) at which cognitive conflict is higher than for less ambiguous parts of the spectra (H1-2). However, in human agent spectra cognitive conflict maxima were more pronounced than for nonhuman agent spectra (H2-1) and category boundaries were shifted towards the human endpoint of the spectrum (H2-2). Overall, these results suggest a quantitatively, though not qualitatively, different categorization process for spectra containing human endpoints. Possible reasons and the impact for virtual and robotic agent design are discussed.