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. 2018 Jun:114:181-185.
doi: 10.1016/j.neuropsychologia.2018.04.027. Epub 2018 Apr 25.

Uncanny valley as a window into predictive processing in the social brain

Affiliations

Uncanny valley as a window into predictive processing in the social brain

Burcu A Urgen et al. Neuropsychologia. 2018 Jun.

Abstract

Uncanny valley refers to humans' negative reaction to almost-but-not-quite-human agents. Theoretical work proposes prediction violation as an explanation for uncanny valley but no empirical work has directly tested it. Here, we provide evidence that supports this theory using event-related brain potential recordings from the human scalp. Human subjects were presented images and videos of three agents as EEG was recorded: a real human, a mechanical robot, and a realistic robot in between. The real human and the mechanical robot had congruent appearance and motion whereas the realistic robot had incongruent appearance and motion. We hypothesize that the appearance of the agent would provide a context to predict her movement, and accordingly the perception of the realistic robot would elicit an N400 effect indicating the violation of predictions, whereas the human and the mechanical robot would not. Our data confirmed this hypothesis suggesting that uncanny valley could be explained by violation of one's predictions about human norms when encountered with realistic but artificial human forms. Importantly, our results implicate that the mechanisms underlying perception of other individuals in our environment are predictive in nature.

Keywords: Action perception; N400; Predictive processing; Social neuroscience; Uncanny valley.

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Figures

Fig. 1.
Fig. 1.
Hypothetical curves that depict the uncanny valley effect for static and moving agents in varying levels of humanlikeness.
Fig. 2.
Fig. 2.
Stimuli used in the ERP experiment, ERP plots for the N400 effect, and bar plot for the N400 effect. (A) Sample static frames from the movies used in the EEG experiment depicting the three agents: Robot, Android, Human. (B) ERP plots of a representative frontal site (Fz) for static and dynamic forms for each agent (Robot, Android, Human). N400 is greater for dynamic Android compared to static, whereas no such difference was found for Human or Robot.(C) Bar graphs representing the area under curve for N400 (370–600 ms) for each of the conditions. N400 is significantly greater for dynamic than static form for Android, whereas they did not differ for Robot or Human.
Fig. 3.
Fig. 3.
ERP scalp maps representing the difference between static and dynamic forms for each agent (Human, Android, Human) in the time interval of the N400 (370–600 ms).
Fig. 4.
Fig. 4.
Source reconstruction analysis, all conditions (dynamic-static) collapsed. LORETA analysis in the N400 (370–600 ms) interval identified a distributed brain activity including middle and superior temporal areas (MTG and STG), tempora-parietal junction (IPL), and frontal areas (IFG, MFG, Medial FG), primarily in the left hemisphere (Difference waves of all conditions (Robot, Android, Human) are collapsed). Colorbar shows the source density. The maximal source density of this network is inferior parietal lobule (x = −59, y = −32, z = 29, MNI coordinates). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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