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Measuring the Uncanny Valley Effect

Refinements to Indices for Perceived Humanness, Attractiveness, and Eeriness

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Abstract

Using a hypothetical graph, Masahiro Mori proposed in 1970 the relation between the human likeness of robots and other anthropomorphic characters and an observer’s affective or emotional appraisal of them. The relation is positive apart from a U-shaped region known as the uncanny valley. To measure the relation, we previously developed and validated indices for the perceptual-cognitive dimension humanness and three affective dimensions: interpersonal warmth, attractiveness, and eeriness. Nevertheless, the design of these indices was not informed by how the untrained observer perceives anthropomorphic characters categorically. As a result, scatter plots of humanness vs. eeriness show the stimuli cluster tightly into categories widely separated from each other. The present study applies a card sorting task, laddering interview, and adjective evaluation (\(N=30\)) to revise the humanness, attractiveness, and eeriness indices and validate them via a representative survey (\(N = 1311\)). The revised eeriness index maintains its orthogonality to humanness (\(r=.04\), \(p=.285\)), but the stimuli show much greater spread, reflecting the breadth of their range in human likeness and eeriness. The revised indices enable empirical relations among characters to be plotted similarly to Mori’s graph of the uncanny valley. Accurate measurement with these indices can be used to enhance the design of androids and 3D computer animated characters.

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Notes

  1. The cinematic production of narrative computer animation by means of a videogame or other real-time graphics engine.

  2. The value is the mean of 12 Cronbach’s \(\alpha \)s, one for each character.

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Acknowledgments

The authors would like to thank Debaleena Chattopadhyay, Alexander Fedorikhin, Edgar Huang, Wade J. Mitchell, Himalaya Patel, and Mark Pfaff for their insightful comments on an earlier draft of the manuscript and Ryan Sukale for technical help. This research was supported by the US National Institutes of Health (P20 GM066402) and an IUPUI Signature Center.

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  1. Indiana University School of Informatics and Computing, 535 West Michigan Street, Indianapolis, IN, 46202, USA

    Chin-Chang Ho & Karl F. MacDorman

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Correspondence to Karl F. MacDorman.

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Ho, CC., MacDorman, K.F. Measuring the Uncanny Valley Effect. Int J of Soc Robotics 9, 129–139 (2017). https://doi.org/10.1007/s12369-016-0380-9

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