skip to main content
10.1145/1349822.1349842acmconferencesArticle/Chapter ViewAbstractPublication PageshriConference Proceedingsconference-collections
research-article

How people anthropomorphize robots

Published:12 March 2008Publication History

ABSTRACT

We explored anthropomorphism in people's reactions to a robot in social context vs. their more considered judgments of robots in the abstract. Participants saw a photo and read transcripts from a health interview by a robot or human interviewer. For half of the participants, the interviewer was polite and for the other half, the interviewer was impolite. Participants then summarized the interactions in their own words and responded true or false to adjectives describing the interviewer. They later completed a post-task survey about whether a robot interviewer would possess moods, attitudes, and feelings. The results showed substantial anthropomorphism in participants' interview summaries and true-false responses, but minimal anthropomorphism in the abstract robot survey. Those who interacted with the robot interviewer tended to anthropomorphize more in the post-task survey, suggesting that as people interact more with robots, their abstract conceptions of them will become more anthropomorphic.

References

  1. Barrett, J. L. & Keil, F. C. (1996). Conceptualizing a nonnatural entity: Anthropomorphism in God concepts. Cognitive Psychology, 31, 219--247.Google ScholarGoogle ScholarCross RefCross Ref
  2. Cacciari, C., & Glucksberg, S. (1994). Understanding figurative language. In M. A. Gernsbacher (Ed.), Handbook of psycholinguistics (pp. 447--477). New York: Academic Press.Google ScholarGoogle Scholar
  3. Coenen, L. H. M., HIdebouw, L., & Semin, G. (2006). The Linguistic Category Model (LCM) Manual (parts 1 and 2). June 2006 Version.Google ScholarGoogle Scholar
  4. Dovidio, J. F., Kawakami, K., & Beach, K. R. (2001). Implicit and explicit attitudes: Examination of the relationship between measures of intergroup bias. In R. Brown, & S. L. Gaertner (Eds.), Blackwell handbook on social psychology (Vol. 4, pp. 175--197). Intergroup relations. Oxford: Blackwell.Google ScholarGoogle Scholar
  5. Greenwald, A. G., Banaji, M. R., Rudman, L. A., Farnham, S. D., Nosek, B. A., & Mellott, D. S. (2002). A unified theory of implicit attitudes, stereotypes, self--esteem, and self-concept. Psychological Review, 109, 3---25.Google ScholarGoogle ScholarCross RefCross Ref
  6. Higgins, E. T., & Rholes, (1978). "Saying is believing": Effects of message modification on memory and liking of the person described. Journal of Experimental Social Psychology, 14, 363--378.Google ScholarGoogle ScholarCross RefCross Ref
  7. Kiesler, S., & Goetz, J. (2002). Machine trait scales for evaluating mechanistic mental models of robots and computer-based machines. Unpublished manuscript, Carnegie Mellon University. Downloadable at http://anthropomorphism.org/pdf/Machine_scale.pdfGoogle ScholarGoogle Scholar
  8. Kiesler, S., Lee, S-L, & Kramer, A. D. I. (2006). Relationship effects in psychological explanations of nonhuman behavior. Anthrozoöös, 19, 335--352.Google ScholarGoogle ScholarCross RefCross Ref
  9. Kramer, A. D. I., Fussell, S. R., & Setlock, L. D. (2004). Text analysis as a tool for analyzing conversation in online support groups. CHI 2004 Late Breaking Results (pp. 1485--1488). NY: ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Lee, S., Kiesler, S., Lau, I.Y. & Chiu, C-Y. (2005). Human mental models of humanoid robots. Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA '05). Barcelona, April 18-22., 2767--2772.Google ScholarGoogle Scholar
  11. Leyens, J., Paladino, P. M., Rodriguez-Torres, R., Vaes, J., Demoulin, S., Rodriguez--Perez, A., & Gaunt, R. (2000). The emotional side of prejudice: The attribution of secondary emotions to ingroups and outgroups. Personality and Social Psychology Review, 4, 186--197.Google ScholarGoogle ScholarCross RefCross Ref
  12. Loughnan, S., & Haslan, N. (2007). Animals and androids: Implicit associations between social categories and nonhumans. Psychological Science, 18, 116--121.Google ScholarGoogle ScholarCross RefCross Ref
  13. Morkes, J., Kernal, H. K., & Nass, C. (1999). Effect of humor in task-oriented human-computer interaction and computer-mediated communication: A direct test of SRCT theory. Human-Computer interaction, 14, 395--435. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Nass, C. & Brave, S. (2005). Wired for speech: How voice activates and advances the human-computer relationship. Cambridge, MA: MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Nass, C. & Lee, K. M. (2001). Does computer-synthesized speech manifest personality? Experimental tests of recognition, similarity-attraction, and consistency-attraction. Journal of Experimental Psychology: Applied, 7, 171--181.Google ScholarGoogle ScholarCross RefCross Ref
  16. Olejnik, S. & ALgina, J. (2003). Generalized eta and omega squared statistics: Measures of effect size for some common research designs. Psychological Methods, 8, 434--447.Google ScholarGoogle ScholarCross RefCross Ref
  17. Pennebaker, J.W., Francis, M.E., & Booth, R. J. (2001). Linguistic Inquiry and Word Count: LIWC (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google ScholarGoogle Scholar
  18. Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. (2003). Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54, 547--577.Google ScholarGoogle ScholarCross RefCross Ref
  19. Posner, M. I. (1978). Chronometric explorations of mind. Hillsdale, NJ: Erlbaum.Google ScholarGoogle Scholar
  20. Powers, A., Kramer, A. D. I., Lim, S., Kuo, J., Lee, S-L., & Kiesler, S. (2005). Eliciting information from people with a gendered humanoid robot. Proceedings of the 14th IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2005).Google ScholarGoogle ScholarCross RefCross Ref
  21. Powers, A., & Kiesler, S. (2006). The advisor robot: Tracing people's mental model from a robot's physical attributes. Conference on Human-Robot Interaction 2006. Salt Lake City, March 1-3, 218--225. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Powers, A., Kiesler, S., Fussell, S., & Torrey, C. (2007). Comparing a computer agent with a humanoid robot. Proceedings of HRI07, pp. 145--152. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Quinn, K. A., Macrae, C. N., & Bodenhausen, G. V. (2003). Stereotyping and impression formation: How categorical thinking shapes person perception. In M. A. Hogg & J. Cooper (Eds.), Sage handbook of social psychology (pp. 87--109). Thousand Oaks, CA: Sage Publications.Google ScholarGoogle Scholar
  24. Schooler, J. W., & Engsler-Schooler, T. Y. (1990). Verbal overshadowing of visual memories: Some things are better left unsaid. Cognitive Psychology, 22, 36--71.Google ScholarGoogle ScholarCross RefCross Ref
  25. Semin, G. R., & Fiedler, K. (1988). The cognitive functions of linguistic categories in describing persons: Social cognition and language. Journal of Personality and Social Psychology, 54, 558--568.Google ScholarGoogle ScholarCross RefCross Ref
  26. Semin, G. R., & Fiedler, K. (1991). The linguistic category model, its bases, applications and range. In W. Stroebe & M. Hewstone (Eds.), European review of social psychology (Vol. 2, pp.1--30). Chichester, England: Wiley.Google ScholarGoogle Scholar
  27. Smith, E. R.,, Miller , D. A., Maitner, A. T., Crump, S. A., Garcia-Marques, T., Mackie, D. M. (2006). Familiarity can increase stereotyping. Journal Of Experimental Social Psychology, 42, 471--478.Google ScholarGoogle ScholarCross RefCross Ref
  28. Torrey, C. Powers, A., Marge, M., Fussell, S. R., & Kiesler, S. (2006). Effects of adaptive robot dialogue on information exchange and social relation. Proceedings of the Conference on Human--Robot Interaction 2006, pp. 126--133. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Wigboldus, D. H. J., Dijksterhuis, A., & Van Knippenberg, A. (2003). When stereotypes get in the way: Stereotypes obstruct stereotype-inconsistent trait inferences. Journal of Personality and Social Psychology, 84, 470--484.Google ScholarGoogle ScholarCross RefCross Ref
  30. Yee, N., Bailenson, J.N., & Rickertsen, K. (2007). A meta-analysis of the impact of the inclusion and realism of human-like faces on user experiences in interfaces. In Proceedings of the Conference on Human Computer Systems CHI'07. pp. 1--10, NY: ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. How people anthropomorphize robots

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Conferences
              HRI '08: Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
              March 2008
              402 pages
              ISBN:9781605580173
              DOI:10.1145/1349822

              Copyright © 2008 ACM

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 12 March 2008

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article

              Acceptance Rates

              Overall Acceptance Rate242of1,000submissions,24%

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader