The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
The Uncanny Valley: Effect of Realism on the Impression of Artificial Human Faces
Abstract
Roboticists believe that people will have an unpleasant impression of a humanoid robot that has an almost, but not perfectly, realistic human appearance. This is called the uncanny valley, and is not limited to robots, but is also applicable to any type of human-like object, such as dolls, masks, facial caricatures, avatars in virtual reality, and characters in computer graphics movies. The present study investigated the uncanny valley by measuring observers' impressions of facial images whose degree of realism was manipulated by morphing between artificial and real human faces. Facial images yielded the most unpleasant impressions when they were highly realistic, supporting the hypothesis of the uncanny valley. However, the uncanny valley was confirmed only when morphed faces had abnormal features such as bizarre eyes. These results suggest that to have an almost perfectly realistic human appearance is a necessary but not a sufficient condition for the uncanny valley. The uncanny valley emerges only when there is also an abnormal feature.
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Cites
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3D Virtual worlds and the metaverse: Current status and future possibilities
Dionisio, J. D. N., III, W. G. B., & Gilbert, R. (2013) · ACM Computing Surveys · doi:10.1145/2480741.2480751 · Cited by 1,151
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Causes and consequences of mind perception
Waytz, A., Gray, K., Epley, N., & Wegner, D. M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.05.006 · Cited by 759
Too real for comfort? Uncanny responses to computer generated faces
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Kätsyri, J., Förger, K., Mäkäräinen, M., & Takala, T. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00390 · Cited by 500
Metadata
- Authors
- Seyama, J., & Nagayama, R. S.
- Venue
- Presence: Teleoperators and Virtual Environments
- Publisher
- MIT Press - Journals
- Published
- 2007-08-01
- Volume
- 16
- Issue
- 4
- Pages
- 337-351
- ISSN
- 1054-7460 (ISSN-L)
- OpenAlex
- W2101870300
- Cited by
- 580
- References
- 35
- FWCI
- 11.75
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
