Skip to main content

Measuring the Uncanny Valley Effect

Refinements to Indices for Perceived Humanness, Attractiveness, and Eeriness

International Journal of Social Robotics volume 9, pages 129–139 (2017)Cite this article

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.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

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.

References

  1. Arkes HR (1991) Costs and benefits of judgment errors: implications for debiasing. Psychol Bull 110(3):486–498. doi:10.1037/0033-2909.110.3.486

    Article  Google Scholar 

  2. Bartneck C, Kulić D, Croft E, Zoghbi S (2009) Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int J Soc Robot 1(1):71–81. doi:10.1007/s12369-008-0001-3

    Article  Google Scholar 

  3. Becker-Asano C, Ogawa K, Nishio S, Ishiguro H (2010) Exploring the uncanny valley with Geminoid HI-1 in a real-world application. In: Blashki K (ed) Proceedings of IADIS International Conference Interfaces and Human Computer Interaction. IADIS Press, Lisbon, Portugal, pp 121–128

  4. Bentler PM (1969) Semantic space is (approximately) bipolar. J Psychol 71(1):33–40. doi:10.1080/00223980.1969.10543067

    Article  Google Scholar 

  5. Bentler PM (1990) Comparative fit indexes in structural models. Psychol Bull 170(2):238–246. doi:10.1037/0033-2909.107.2.238

    Article  Google Scholar 

  6. Burleigh TJ, Schoenherr JR (2015) A reappraisal of the uncanny valley: categorical perception or frequency-based sensitization? Front Psychol 5(1488):1–19. doi:10.3389/fpsyg.2014.01488

    Google Scholar 

  7. Chattopadhyay D, MacDorman KF (2016) Familiar faces rendered strange: Why inconsistent realism drives characters into the uncanny valley. J Vis, 16(11):7, 1–25

  8. Cheetham M, Pavlovic I, Jordan N, Suter P, Jäncke L (2013) Category processing and the human likeness dimension of the uncanny valley hypothesis: eye-tracking data. Front Psychol 4(108):1–12. doi:10.3389/fpsyg.2013.00108

    Google Scholar 

  9. Cheetham M, Suter P, Jäncke L (2011) The human likeness dimension of the “uncanny valley hypothesis”: behavioral and functional MRI findings. Front Hum Neurosci 5(125):1–14. doi:10.3389/fnhum.2011.00126

    Google Scholar 

  10. Chin WW, Todd PA (1995) On the use, usefulness, and ease of use of structural equation modeling in MIS research: a note of caution. MIS Q 19(2):237–246. doi:10.2307/249690

    Article  Google Scholar 

  11. Dunning D, Johnson K, Ehrlinger J, Kruger J (2003) Why people fail to recognize their own incompetence. Curr Dir Psychol Sci 12(3):83–87. doi:10.1111/1467-8721.01235

    Article  Google Scholar 

  12. Feldman NH, Griffiths TL, Morgan JL (2009) The influence of categories on perception: explaining the perceptual magnet effect as optimal statistical inference. Psychol Rev 116(4):752–782. doi:10.1037/a0017196

    Article  Google Scholar 

  13. Fox CR, Clemen RT (2005) Subjective probability assessment in decision analysis: partition dependence and bias toward the ignorance prior. Manag Sci 51(9):1417–1432. doi:10.1287/mnsc.1050.0409

    Article  Google Scholar 

  14. Gärling T (1976) A multidimensional scaling and semantic differential technique study of the perception of environmental settings. Scand J Psychol 17(1):323–332. doi:10.1111/j.1467-9450.1976.tb00248.x

    Article  Google Scholar 

  15. Gefen D, Straub D, Boudreau M (2000) Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inf Syst 4(7):1–79

    Google Scholar 

  16. Gerbing DW, Hamilton JG (1996) Viability of exploratory factor analysis as a precursor to confirmatory factor analysis. Struct Equ Model 3(1):62–72. doi:10.1080/10705519609540030

    Article  Google Scholar 

  17. Goetz J, Kiesler S, Powers A (2003) Matching robot appearance and behavior to tasks to improve human-robot cooperation. In: Proceedings of the 12th IEEE International Workshop on Robot and Human Interactive Communication. IEEE Press, Piscataway, pp 55–60. doi:10.1109/ROMAN.2003.1251796

  18. Hanson D (2005) Expanding the aesthetic possibilities for humanoid robots. In: Proceedings of the views of the uncanny valley workshop. IEEE-RAS International Conference on Humanoid Robots. December 5, Tsukuba, Japan

  19. Harnad S (1987) Category induction and representation. In: Harnad S (ed) Categorical perception: the groundwork of cognition. Cambridge University Press, New York, pp 535–565

    Google Scholar 

  20. Hashimoto T, Nakane H, Kobayashi H (2013) Android patient robot simulating depressed patients for diagnosis training of psychiatric trainees. In: Proceedings of the Second IEEE International Conference on Robot, Vision and Signal Processing. IEEE Press, Piscataway, pp 247–252. doi:10.1109/RVSP.2013.63

  21. Ho C-C, MacDorman KF (2010) Revisiting the uncanny valley theory: developing and validating an alternative to the Godspeed indices. Comput Hum Behav 26(6):1508–1518. doi:10.1016/j.chb.2010.05.015

    Article  Google Scholar 

  22. Ho C-C, MacDorman KF, Pramono ZAD (2008) Human emotion and the uncanny valley: A GLM, MDS, and isomap analysis of robot video ratings. In: Proceedings of the Third ACM/IEEE International Conference on Human-Robot Interaction (pp. 169–176). ACM, New York. doi:10.1145/1349822.1349845

  23. Kätsyri J, Förger K, Mäkäräinen M, Takala T (2015) A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness. Front Psychol 6(390):1–16. doi:10.3389/fpsyg.2015.00390

    Google Scholar 

  24. Kikutani M, Roberson D, Hanley JR (2010) Categorical perception for unfamiliar faces: the effect of covert and overt face learning. Psychol Sci 21(6):865–872. doi:10.1177/0956797610371964

    Article  Google Scholar 

  25. Kruger J, Dunning D (1999) Unskilled and unaware of it: how difficulties in recognizing one’s own incompetence lead to inflated self-assessments. J Personal Soc Psychol 77(6):1121–1134. doi:10.1037/0022-3514.77.6.1121

    Article  Google Scholar 

  26. Looser CE, Wheatley T (2010) The tipping point of animacy: how, when, and where we perceive life in a face. Psychol Sci 21(12):1854–1862. doi:10.1177/0956797610388044

    Article  Google Scholar 

  27. Lorr M, Wunderlich RA (1988) A semantic differential mood scale. J Clin Psychol 44(1):33–36

    Article  Google Scholar 

  28. MacDorman KF, Chattopadhyay D (2016) Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not. Cognition 146:190–205. doi:10.1016/j.cognition.2015.09.019

    Article  Google Scholar 

  29. MacDorman KF, Entezari SO (2015) Individual differences predict sensitivity to the uncanny valley. Interact Stud 16(2):141–172. doi:10.1075/is.16.2.01mac

    Article  Google Scholar 

  30. MacDorman KF, Green RD, Ho C-C, Koch C (2009) Too real for comfort: Uncanny responses to computer generated faces. Comput Hum Behav 25(3):695–710. doi:10.1016/j.chb.2008.12.026

    Article  Google Scholar 

  31. MacDorman KF, Ishiguro H (2006a) The uncanny advantage of using androids in social and cognitive science research. Interact Stud 7(3):297–337. doi:10.1075/is.7.3.03mac

    Article  Google Scholar 

  32. MacDorman KF, Ishiguro H (2006b) Opening Pandora’s uncanny box: reply to commentaries on the uncanny advantage of using androids in social and cognitive science research. Interact Stud 7(3):361–368. doi:10.1075/is.7.3.10mac

    Article  Google Scholar 

  33. MacDorman KF, Vasudevan SK, Ho C-C (2009) Does Japan really have robot mania? Comparing attitudes by implicit and explicit measures. AI Soc 23(4):485–510. doi:10.1007/s00146-008-0181-2

    Article  Google Scholar 

  34. Macrae CN, Bodenhausen GV (2000) Social cognition: thinking categorically about others. Annu Rev Psychol 51(1):93–120. doi:10.1146/annurev.psych.51.1.93

    Article  Google Scholar 

  35. Mangan BB (2015) The uncanny valley as fringe experience. Interact Stud 16(2):193–199. doi:10.1075/is.16.2.05man

    Article  Google Scholar 

  36. Mathur MB, Reichling DB (2016) Navigating a social world with robot partners: a quantitative cartography of the uncanny valley. Cognition 146:22–32. doi:10.1016/j.cognition.2015.09.008

    Article  Google Scholar 

  37. Meah LFS, Moore RK (2014) The uncanny valley: a focus on misaligned cues. In: Beetz M, Johnston B, Williams M-A (eds) Social robotics, LNAI, vol 8755. Springer, Cham, pp 256–265. doi:10.1007/978-3-319-11973-1_26

  38. Michalowski MP, Sabanovic S, Simmons R (2006) A spatial model of engagement for a social robot. In: Proceedings of the Ninth IEEE International Workshop on Advanced Motion Control. IEEE Press, Piscataway, pp 762–767. doi:10.1109/AMC.2006.1631755

  39. Misselhorn C (2009) Empathy with inanimate objects and the uncanny valley. Minds Mach 19(3):345–459. doi:10.1007/s11023-009-9158-2

    Article  Google Scholar 

  40. Mitchell WJ, Szerszen Sr, KA, Lu AS, Schermerhorn PW, Scheutz M, MacDorman KF (2011). A mismatch in the human realism of face and voice produces an uncanny valley. i-Perception, 2(1), 10–12. doi:10.1068/i0415

  41. Moore RK (2012) A Bayesian explanation of the ‘uncanny valley’ effect and related psychological phenomena. Sci Rep 2(864):1–5. doi:10.1038/srep00864

    Google Scholar 

  42. Mori M (2012) The uncanny valley. IEEE Robotics and Automation (trans: MacDorman KF, Kageki N) 19(2), 98–100 doi:10.1109/MRA.2012.2192811 (Original work published in 1970)

  43. Nomura T, Kanda T, Suzuki T, Kato K (2004) Psychology in human-robot communication: an attempt through investigation of negative attitudes and anxiety toward robots. In: Proceedings of the 13th IEEE International Workshop on Robot and Human Interactive Communication. IEEE Press, Piscataway, pp 35–40

  44. Nomura T, Kanda T (2016) Rapport–expectation with a robot scale. Int J Soc Robot (8)1: 21–30. doi:10.1007/s12369-015-0293-z

  45. Prakash A, Rogers WA (2015) Why some humanoid faces are perceived more positively than others: effects of humanlikeness and task. Int J Soc Robot 7(2):309–0331. doi:10.1007/s12369-014-0269-4

    Article  Google Scholar 

  46. Pronin E (2007) Perception and misperception of bias in human judgment. Trends Cognit Sci 11(1):37–43. doi:10.1016/j.tics.2006.11.001

    Article  Google Scholar 

  47. Pronin E, Lin DY, Ross L (2002) The bias blind spot: perceptions of bias in self versus others. Personal Soc Psychol Bull 28(3):369–381. doi:10.1177/0146167202286008

    Article  Google Scholar 

  48. Ramey CH (2005) The uncanny valley of similarities concerning abortion, baldness, heaps of sand, and humanlike robots. In: Proceedings of the Views of the Uncanny Valley Workshop. IEEE-RAS International Conference on Humanoid Robots. December 5, Tsukuba, pp 8–13

  49. Riek LD, Rabinowitch TC, Chakrabarti B, Robinson P (2009) Empathizing with robots: fellow feeling along the anthropomorphic spectrum. In: Proceedings of the Third International Conference on Affective Computing and Intelligent Interaction and Workshops. Amsterdam, pp 1–6, September 10–12. doi:10.1109/ACII.2009.5349423

  50. Rosenberg S, Nelson C, Vivekananthan P (1968) A multidimensional approach to the structure of personality impressions. J Personal Soc Psychol 97(4):283–294. doi:10.1037/h0026086

    Article  Google Scholar 

  51. Rugg G, McGeorge P (1997) The sorting techniques: a tutorial paper on card sorts, picture sorts and item sorts. Expert Syst 12(4):80–93. doi:10.1111/1468-0394.00045

    Article  Google Scholar 

  52. Seyama J, Nagayama RS (2007) The uncanny valley: the effect of realism on the impression of artificial human faces. Presence 16(4):337–351. doi:10.1162/pres.16.4.337

    Article  Google Scholar 

  53. ter Hofstede F, Audenaert A, Steenkamp J-BEM, Wedel M (1998) An investigation into the association pattern techniques as a quantitative approach to measuring means-end chains. Int J Res Mark 15(1):37–50. doi:10.1016/S0167-8116(97)00029-3

    Article  Google Scholar 

  54. Tondu B, Bardou N (2011) A new interpretation of Mori’s uncanny valley for future humanoid robots. Int J Robot Autom 26(3):337–348. doi:10.2316/Journal.206.2011.3.206-3348

    Google Scholar 

  55. Turkle S (2007) Authenticity in the age of digital companions. Interact Stud 8(3):501–517. doi:10.1075/is.8.3.11tur

    Article  Google Scholar 

  56. Uekermann F, Herrmann A, Wentzel D, Landwehr JR (2008) The influence of stimulus ambiguity on category and attitude formation. Rev Manag Sci 4(1):33–52. doi:10.1007/s11846-009-0034-5

    Article  Google Scholar 

  57. van Schuur WH, Kiers HAL (1994) Why factor analysis often is the incorrect model for analyzing bipolar concepts and what model to use instead. Appl Psychol Meas 18(2):97–110. doi:10.1177/014662169401800201

    Article  Google Scholar 

  58. Vanden Abeele P (1992) A means-end study of dairy consumption motivation. Report for the European Commission, EC Regulation 1000/90–43 ST

  59. Vlachos E, Scharfe H (2015) An open-ended approach to evaluating android faces. In: Proceedings of the 24th IEEE International Symposium on Robot and Human Interactive Communication. IEEE Press, Piscataway, pp 746–751

  60. Walters M, Marcos S, Syrdal DS, Dautenhahn K (2013) An interactive game with a robot: People’s perceptions of robot faces and a gesture-based user interface. In: Proceedings of the Sixth International Conference on Advanced Computer-human Interactions. IARIA Press, Lisbon, Portugal, pp 123–128

  61. Yamada Y, Kawabe T, Ihaya K (2013) Categorization difficulty is associated with negative evaluation in the “uncanny valley” phenomenon. Jpn Psychol Res 55(1):20–32. doi:10.1111/j.1468-5884.2012.00538.x

    Article  Google Scholar 

  62. Yamauchi T (2005) Labeling bias and categorical induction: generative aspects of category information. J Exp Psychol 31(3):538–553. doi:10.1037/0278-7393.31.3.538

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

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

    Chin-Chang Ho & Karl F. MacDorman

Authors
  1. Chin-Chang Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karl F. MacDorman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karl F. MacDorman.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12369-016-0380-9

Keywords

  • Anthropomorphism
  • Categorical perception
  • Cognitive bias
  • Psychometric scales
  • Social perception