Abstract
The Uncanny Valley Hypothesis (UVH, proposed in the 1970s) suggests that looking at or interacting with almost human-like artificial characters would trigger eeriness or discomfort. We studied how well subjects can assess degrees of human likeness for computer-generated characters. We conducted two studies, where subjects were asked to assess human likeness of given computer-generated models (Study 1) and to point the most typical model for a given category (Study 2). The results suggest that evaluation of the way human likeness is assessed should be an internal part of UVH research.
Author contributions: The authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
1. Mori M, MacDorman KF, Kageki N. The uncanny valley [from the field]. IEEE Robot Autom Mag 2012;19:98–100. (Original work published in 1970 in Japaneese).10.1109/MRA.2012.2192811Search in Google Scholar
2. Kätsyri J, Förger K, Mäkäräinen M, Takala T. A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness. Front Psychol 2015;6:390.10.3389/fpsyg.2015.00390Search in Google Scholar PubMed PubMed Central
3. Ueyama Y. A bayesian model of the uncanny valley effect for explaining the effects of therapeutic robots in autism spectrum disorder. PLoS One 2015;10:e0138642.10.1371/journal.pone.0138642Search in Google Scholar PubMed PubMed Central
4. MacDorman KF, Green RD, Ho C-C, Koch CT. Too real for comfort? Uncanny responses to computer generated faces. Comput Hum Behav 2009;25:695–710.10.1016/j.chb.2008.12.026Search in Google Scholar PubMed PubMed Central
5. Geller T. Overcoming the uncanny valley. IEEE Comput Graph Appl 2008;28:11–7.10.1109/MCG.2008.79Search in Google Scholar PubMed
6. Gee F, Browne WN, Kawamura K. Uncanny valley revisited. In: Mitch Wilkes, editor. Robot and Human Interactive Communication, ROMAN 2005. IEEE International Workshop on IEEE, Nashville, TN, USA, 2005:151–7. Doi: 10.1109/ROMAN.2005.1513772.Search in Google Scholar
7. Piwek L, McKay LS, Pollick FE. Empirical evaluation of the uncanny valley hypothesis fails to confirm the predicted effect of motion. Cognition 2014;130:271–7.10.1016/j.cognition.2013.11.001Search in Google Scholar PubMed
8. Dill V, Flach LM, Hocevar R, Lykawka C, Musse SR, Pinho MS. Evaluation of the Uncanny Valley in CG characters. In: Yukiko N, Neff M, Paiva A, Walker M, editors. International Conference on Intelligent Virtual Agents. Berlin: Springer, 2012:511–3.10.1007/978-3-642-33197-8_62Search in Google Scholar
9. R Core Team. 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Accessed: 20 Mar 2017.Search in Google Scholar
10. Gamer M, Lemon J, Singh IF. irr: Various coefficients of interrater reliability and agreement. Accessed: 20 Mar 2017, R package version 0.84, 2012.Search in Google Scholar
11. Viera AJ, Garrett JM. Understanding interobserver agreement: The kappa statistic. Fam Med 2005;37:360–3.Search in Google Scholar PubMed
12. Hallgren KA. Computing inter-rater reliability for observational data: an overview and tutorial. Tutor Quant Methods Psychol 2012;8:23–34.10.20982/tqmp.08.1.p023Search in Google Scholar PubMed PubMed Central
©2017 Walter de Gruyter GmbH, Berlin/Boston
