, Volume 33, Issue 4, pp 527–536 | Cite as

The quest for appropriate models of human-likeness: anthropomorphism in media equation research

  • Nils KlowaitEmail author
Original Article


Nass’ and Reeves’ media equation paradigm within human–computer interaction (HCI) challenges long-held assumptions about how users approach computers. Given a rudimentary set of cues present in the system’s design, users are said to unconsciously treat computers as genuine interactants—extending rules of politeness, biases and human interactive conventions to machines. Since the results have wide-ranging implications for HCI research methods, interface design and user experiences, researchers are hard-pressed to experimentally verify the paradigm. This paper focuses on the methodology of attributing the necessary social cues to the agent, a core aspect of the experimental design of studies dealing with the media equation. A typology of experimental anthropomorphisms is developed, allowing an assessment of how the differing axiomatic assumptions affect the relevance of the results for an evaluation of the paradigm. The paper concludes with a series of arguments in favour of one particular anthropomorphism type for researching the media equation.


Human–computer interaction Avatars Intelligent agents Natural language interfaces Media equation Computers are social actors 


  1. Barber D, Sims VK, Chin MG et al (2006) Anthropomorphism of textured “faces”. Proc Hum Factors Ergon Soc Annu Meet 50(17):1932–1935. doi: 10.1177/154193120605001746 CrossRefGoogle Scholar
  2. Blackstone MM, Wiebe DJ, Mollen CJ, Kalra A, Fein JA (2009) Feasibility of an interactive voice response tool for adolescent assault victims. Acad Emerg Med 16(10):956–962. doi: 10.1111/j.1553-2712.2009.00519.x CrossRefGoogle Scholar
  3. Brahnam S, de Angeli A (2012) Gender affordances of conversational agents. Interact Comput 24(3):139–153. doi: 10.1016/j.intcom.2012.05.001 CrossRefGoogle Scholar
  4. Button G, Dourish P (1996) Technomethodology: paradoxes and possibilities. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 19–26Google Scholar
  5. Cowell AJ, Stanney KM (2005) Manipulation of non-verbal interaction style and demographic embodiment to increase anthropomorphic computer character credibility. Int J Hum Comput Stud 62(2):281–306. doi: 10.1016/j.ijhcs.2004.11.008 CrossRefGoogle Scholar
  6. De Meuse KP (1987) A review of the effects of non-verbal cues on the performance appraisal process. J Occup Psychol 60(3):207–226. doi: 10.1111/j.2044-8325.1987.tb00254.x CrossRefGoogle Scholar
  7. Dennett DC (1988) Précis of the intentional stance. Behav Brain Sci 11(03):495–505. doi: 10.1017/S0140525X00058611 CrossRefGoogle Scholar
  8. Dillman DA, Phelps G, Tortora R, Swift K, Kohrell J, Berck J, Messer BL (2009) Response rate and measurement differences in mixed-mode surveys using mail, telephone, interactive voice response (IVR) and the Internet. Soc Sci Res 38(1):1–18. doi: 10.1016/j.ssresearch.2008.03.007 CrossRefGoogle Scholar
  9. Eyssel F, Hegel F (2012) (S)he’s got the look: gender stereotyping of robots. J Appl Soc Psychol 42(9):2213–2230. doi: 10.1111/j.1559-1816.2012.00937.x CrossRefGoogle Scholar
  10. Kim Y, Sundar SS (2012) Anthropomorphism of computers: is it mindful or mindless? Comput Hum Behav 28(1):241–250. doi: 10.1016/j.chb.2011.09.006 CrossRefGoogle Scholar
  11. Klowait NO (2017) A conceptual framework for researching emergent social orderings in encounters with automated computer-telephone interviewing agents. Int J Commun Linguist Stud 15(1):19–37Google Scholar
  12. Langer EJ (1992) Matters of mind: mindfulness/mindlessness in perspective. Conscious Cognit 1(3):289–305CrossRefGoogle Scholar
  13. Laurel B (1991) Computers as theatre. Addison-Wesley Longman Publishing Co., Inc, BostonGoogle Scholar
  14. Laurel B, Mountford SJ (eds) (1990) The art of human–computer interface design. Addison-Wesley Longman, BostonzbMATHGoogle Scholar
  15. Liang Y, Lee SA, Jang J-W (2013) Mindlessness and gaining compliance in computer–human interaction. Comput Hum Behav 29(4):1572–1579. doi: 10.1016/j.chb.2013.01.009 CrossRefGoogle Scholar
  16. Linek SB, Gerjets P, Scheiter K (2010) The speaker/gender effect: does the speaker’s gender matter when presenting auditory text in multimedia messages? Instr Sci 38(5):503–521CrossRefGoogle Scholar
  17. McEneaney JE (2009) Agency attribution in human–computer interaction: engineering psychology and cognitive ergonomics. In: Harris D (ed) 8th International conference, EPCE 2009, held as part of HCI international 2009, San Diego, CA, USA, 19–24 July 2009. Proceedings. Springer, Berlin, pp 81–90. doi: 10.1007/978-3-642-02728-4_9 CrossRefGoogle Scholar
  18. Moon Y (1998) Impression management in computer-based interviews: the effects of input modality, and distance. Public Opin Q 62(4):610–622. doi: 10.1086/297862 CrossRefGoogle Scholar
  19. Nass C, Brave S (2005) Wired for speech: how voice activates and advances the human-computer relationship. MIT Press, CambridgeGoogle Scholar
  20. Nass C, Moon Y (2000) Machines and mindlessness: social responses to computers. J Soc Issues 56(1):81–103. doi: 10.1111/0022-4537.00153 CrossRefGoogle Scholar
  21. Nass C, Steuer J, Tauber ER (1994) Computers are social actors. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 72–78. doi: 10.1145/191666.191703
  22. Nass C, Moon Y, Green N (1997) Are machines gender neutral? Gender-stereotypic responses to computers with voices. J Appl Soc Psychol 27(10):864–876. doi: 10.1111/j.1559-1816.1997.tb00275.x CrossRefGoogle Scholar
  23. Reeves B, Nass C (1996) The media equation: how people treat computers, television, and new media like real people and places. Cambridge University Press, CambridgeGoogle Scholar
  24. Sundar SS, Nass C (2000) Source orientation in human–computer interaction: programmer, networker, or independent social actor. Commun Res 27(6):683–703. doi: 10.1177/009365000027006001 CrossRefGoogle Scholar
  25. Sundar SS, Bellur S, Oh J, Jia H, Kim H-S (2014) Theoretical importance of contingency in human–computer interaction: effects of message interactivity on user engagement. Commun Res. doi: 10.1177/0093650214534962 CrossRefGoogle Scholar
  26. Tourangeau R, Couper MP, Steiger DM (2003) Humanizing self-administered surveys: experiments on social presence in web and IVR surveys. Comput Hum Behav 19(1):1–24. doi: 10.1016/S0747-5632(02)00032-8 CrossRefGoogle Scholar
  27. von der Pütten Astrid M, Krämer NC, Gratch J, Kang S-H (2010) “It doesn’t matter what you are!” Explaining social effects of agents and avatars. Comput Hum Behav 26(6):1641–1650. doi: 10.1016/j.chb.2010.06.012 CrossRefGoogle Scholar
  28. Wiebe DJ, Carr BG, Datner EM, Elliott MR, Richmond TS (2008) Feasibility of an automated telephone survey to enable prospective monitoring of subjects whose confidentiality is paramount: a four-week cohort study of partner violence recurrence after Emergency Department discharge. Epidemiol Perspect Innov. doi: 10.1186/1742-5573-5-1 CrossRefGoogle Scholar
  29. Zhu B, Kaber D (2012) Effects of etiquette strategy on human–robot interaction in a simulated medicine delivery task. Intell Serv Robot 5(3):199–210. doi: 10.1007/s11370-012-0113-3 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.International Center for Contemporary Social TheoryMoscow School of Social and Economic SciencesMoscowRussian Federation
  2. 2.Faculty of Philosophy and Social SciencesRussian Presidential Academy of National Economy and Public AdministrationMoscowRussian Federation
  3. 3.MoscowRussian Federation

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