The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not
Open accessAbstract
Human replicas may elicit unintended cold, eerie feelings in viewers, an effect known as the uncanny valley. Masahiro Mori, who proposed the effect in 1970, attributed it to inconsistencies in the replica's realism with some of its features perceived as human and others as nonhuman. This study aims to determine whether reducing realism consistency in visual features increases the uncanny valley effect. In three rounds of experiments, 548 participants categorized and rated humans, animals, and objects that varied from computer animated to real. Two sets of features were manipulated to reduce realism consistency. (For humans, the sets were eyes-eyelashes-mouth and skin-nose-eyebrows.) Reducing realism consistency caused humans and animals, but not objects, to appear eerier and colder. However, the predictions of a competing theory, proposed by Ernst Jentsch in 1906, were not supported: The most ambiguous representations-those eliciting the greatest category uncertainty-were neither the eeriest nor the coldest.
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Cohen, J. (1992) · Psychological Bulletin · doi:10.1037//0033-2909.112.1.155 · Cited by 8,771
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483
The Uncanny Valley [From the Field]
Mori, M., MacDorman, K., & Kageki, N. (2012) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2012.2192811 · Cited by 3,542
Self-citationThe eyes have it: the neuroethology, function and evolution of social gaze
Emery, N. J. (2000) · Neuroscience & Biobehavioral Reviews · doi:10.1016/s0149-7634(00)00025-7 · Cited by 2,037
Categorical Perception: The Groundwork of Cognition
Harnad, S. R. (1990) · Medical Entomology and Zoology · Cited by 1,434
Cues of being watched enhance cooperation in a real-world setting
Bateson, M., Nettle, D., & Roberts, G. (2006) · Biology Letters · doi:10.1098/rsbl.2006.0509 · Cited by 1,304
On the motivational nature of cognitive dissonance: Dissonance as psychological discomfort.
Elliot, A. J., & Devine, P. G. (1994) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.67.3.382 · Cited by 1,289
Temporal Cortex Activation in Humans Viewing Eye and Mouth Movements
Puce, A., Allison, T., Bentin, S., Gore, J. C., & McCarthy, G. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-06-02188.1998 · Cited by 1,077
Wide-band negative permeability of nonlinear metamaterials
Lapine, M., Shadrivov, I., & Kivshar, Y. (2012) · Scientific Reports · doi:10.1038/srep00412 · Cited by 1,017
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Tropea, P., Schlieter, H., Sterpi, I., Judica, E., Gand, K., Caprino, M., Gabilondo, I., Gomez-Esteban, J. C., Busnatu, S., Sinescu, C., et al. (2019) · Journal of Medical Internet Research · doi:10.2196/12805 · Cited by 73
Metadata
- Authors
- MacDorman, K. F., & Chattopadhyay, D.
- Venue
- Cognition
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 146
- Pages
- 190-205
- ISSN
- 0010-0277 (ISSN-L)
- OpenAlex
- W1867827413
- Cited by
- 257
- References
- 67
- FWCI
- 18.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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