While artificial agents (AA) such as Artificial Intelligence are being extensively developed, a popular belief that AA will someday surpass human intelligence is growing. The present research examined whether this common belief translates into negative psychological and behavioral consequences when individuals assess that an AA performs better than them on cognitive and intellectual tasks. In two studies, participants were led to believe that an AA performed better or less well than them on a cognitive inhibition task (Study 1) and on an intelligence task (Study 2). Results indicated that being outperformed by an AA increased subsequent participants’ performance as long as they did not experience psychological discomfort towards the AA and self-threat. Psychological implications in terms of motivation and potential threat as well as the prerequisite for the future interactions of humans with AAs are further discussed.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
For second session analyses similar to Normand & Croizet (2013), see Supplementary Material.
However we controlled that participants’ performance on the Raven matrices did not differ in Session 1, F(2, 73) = 429., p = .653, ηp2 = .012.
Anderson, M. L. (2005). Why is AI so scary? Artificial Intelligence, 169(2), 201–208. https://doi.org/10.1016/j.artint.2005.10.008.
Augustinova, M., & Ferrand, L. (2012). The influence of mere social presence on Stroop interference: New evidence from the semantically-based Stroop task. Journal of Experimental Social Psychology. https://doi.org/10.1016/j.jesp.2012.04.014.
Augustinova, M., & Ferrand, L. (2014). Automaticity of word reading: Evidence from the semantic stroop paradigm. Current Directions in Psychological Science, 23(5), 343–348. https://doi.org/10.1177/0963721414540169.
Ayoub, K., & Payne, K. (2016). Strategy in the Age of Artificial Intelligence. Journal of Strategic Studies, 39(5–6), 793–819. https://doi.org/10.1080/01402390.2015.1088838.
Baron, R. S. (1986). Distraction-conflict theory: Progress and problems. In Advances in experimental social psychology (Vol. 19, pp. 1–40). Academic Press.
Blascovich, J., Mendes, W. B., Hunter, S. B., & Salomon, K. (1999). Social “facilitation” as challenge and threat. Journal of Personality and Social Psychology, 77(1), 68–77. https://doi.org/10.1037/0022-3522.214.171.124.
Brewka, G. (1996). Artificial intelligence—a modern approach by Stuart Russell and Peter Norvig, Prentice Hall. Series in Artificial Intelligence, Englewood Cliffs, NJ. In The Knowledge Engineering Review (Vol. 11). https://doi.org/10.1017/s0269888900007724
Brown, J. D. (2002). The Cronbach alpha reliability estimate. JALT Testing & Evaluation SIG Newsletter, 6(1), 17–18.
Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: A theoretical account of the processing in the Raven progressive matrices test. Psychological Review, 97(3), 404–431. https://doi.org/10.1037/0033-295X.97.3.404.
Carpinella, C. M., Wyman, A. B., Perez, M. A., & Stroessner, S. J. (2017). The Robotic Social Attributes Scale (RoSAS): Development and Validation. ACM/IEEE International Conference on Human-Robot Interaction, Part F1271, 254–262. https://doi.org/10.1145/2909824.3020208
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555.
Dalrymple, K. L., & Herbert, J. D. (2007). Acceptance and commitment therapy for generalized social anxiety disorder a pilot study. Behavior Modification, 31(5), 543–568. https://doi.org/10.1177/0145445507302037.
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146.
Fumi, F. G., & Parr, R. G. (1953). Electronic states of diatomic molecules: The oxygen molecule. The Journal of Chemical Physics, 21(10), 1864–1868. https://doi.org/10.1063/1.1698680.
Gerber, J. P., Wheeler, L., & Suls, J. (2018). A social comparison theory meta-analysis 60+ years on. Psychological Bulletin, 144(2), 177–197. https://doi.org/10.1037/bul0000127.
Harrison, T. L., Shipstead, Z., & Engle, R. W. (2015). Why is working memory capacity related to matrix reasoning tasks? Memory and Cognition, 43(3), 389–396. https://doi.org/10.3758/s13421-014-0473-3.
Heerink, M. (2011). Exploring the influence of age, gender, education and computer experience on robot acceptance by older adults. HRI 2011 - Proceedings of the 6th ACM/IEEE International Conference on Human-Robot Interaction. https://doi.org/10.1145/1957656.1957704
Huguet, P., Galvaing, M. P., Monteil, J. M., & Dumas, F. (1999). Social presence effects in the Stroop task: Further evidence for an attentional view of social facilitation. Journal of Personality and Social Psychology, 77(5), 1011–1024. https://doi.org/10.1037/0022-35126.96.36.1991.
Kuo, I. H., Rabindran, J. M., Broadbent, E., Lee, Y. I., Kerse, N., Stafford, R. M. Q., et al. (2009). Age and gender factors in user acceptance of healthcare robots. Proceedings IEEE International Workshop on Robot and Human Interactive Communication. https://doi.org/10.1109/ROMAN.2009.5326292.
Lachaud, C. M., & Renaud, O. (2011). A tutorial for analyzing human reaction times: How to filter data, manage missing values, and choose a statistical model. Applied Psycholinguistics. https://doi.org/10.1017/s0142716410000457.
Lawless, W. F., Mittu, R., Russell, S., & Sofge, D. (2017). Autonomy and artificial intelligence: A Threat or Savior? In: Autonomy and Artificial Intelligence: A Threat or Savior?https://doi.org/10.1007/978-3-319-59719-5
Lockwood, P., & Kunda, Z. (1997). Superstars and me: Predicting the impact of role models on the self. Journal of Personality and Social Psychology, 73(1), 91–103. https://doi.org/10.1037/0022-35188.8.131.52.
McArthur, D., Lewis, M., & Bishary, M. (2005). The Roles Of Artificial Intelligence In Education: Current Progress And Future Prospects. I-Manager’s Journal of Educational Technology, 1(4), 42–80. https://doi.org/10.26634/jet.1.4.972.
Muller, D., Atzeni, T., & Butera, F. (2004). Coaction and upward social comparison reduce the illusory conjunction effect: Support for distraction-conflict theory. Journal of Experimental Social Psychology, 40(5), 659–665. https://doi.org/10.1016/j.jesp.2003.12.003.
Muller, D., & Butera, F. (2007). The focusing effect of self-evaluation threat in coaction and social comparison. Journal of Personality and Social Psychology, 93(2), 194–211. https://doi.org/10.1037/0022-35184.108.40.206.
Mushtaq, F., Bland, A. R., & Schaefer, A. (2011). Uncertainty and cognitive control. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2011.00249.
Nomura, T. (2017). Robots and Gender. In: Principles of gender-specific medicine: Gender in the genomic era: Third Edition. 10.1016/B978-0-12-803506-1.00042-5
Normand, A., & Croizet, J. C. (2013). Upward social comparison generates att entional focusing when the dimension of comparison is self-threatening. Social Cognition, 31(3), 336–348. https://doi.org/10.1521/soco.2013.31.3.336.
Normand, A., Bouquet, C. A., & Croizet, J. C. (2014). Does evaluative pressure make you less or more distractible? Role of top-down attentional control over response selection. Journal of Experimental Psychology: General, 143(3), 1097–1111. https://doi.org/10.1037/a0034985.
Pan, Y., & Steed, A. (2016). A comparison of avatar-, video-, and robot-mediated interaction on users’ trust in expertise. Frontiers Robotics AI. https://doi.org/10.3389/frobt.2016.00012.
Paulhus, D. L. (2013). Measurement and control of response bias. Measures of Personality and Social Psychological Attitudes. https://doi.org/10.1016/b978-0-12-590241-0.50006-x.
Perri, (2001). Ethics, regulation and the new artificial intelligence, part I: Accountability and Power. Information Communication and Society, 4(2), 199–229. https://doi.org/10.1080/13691180110044461.
Przybylski, A. K., Rigby, C. S., & Ryan, R. M. (2010). A motivational model of video game engagement. Review of General Psychology, 14(2), 154–166. https://doi.org/10.1037/a0019440.
Raven, J. C. (1941). Standardization of progressive matrices, 1938. British Journal of Medical Psychology, 19(1), 137–150. https://doi.org/10.1111/j.2044-8341.1941.tb00316.x.
Raven, J. (2000). The Raven’s progressive matrices: Change and stability over culture and time. Cognitive Psychology, 41(1), 1–48. https://doi.org/10.1006/cogp.1999.0735.
Riether, N., Hegel, F., Wrede, B., & Horstmann, G. (2012). Social facilitation with social robots? HRI’12 - Proceedings of the 7th Annual ACM/IEEE International Conference on Human-Robot Interaction, 41–47. https://doi.org/10.1145/2157689.2157697
Rigas, H., Booth, T., Briggs, F., Murata, T., & Stone, H. S. (1985). Artificial intelligence research in Japan. Computer, 18(9), 83–90. https://doi.org/10.1109/MC.1985.1663007.
Rubio, V., & Deng, X. W. (2007). PLANT SCIENCE: Standing on the Shoulders of GIGANTEA. Science, 318(5848), 206–207. https://doi.org/10.1126/science.1150213.
Sanders, G. S., Baron, R. S., & Moore, D. L. (1978). Distraction and social comparison as mediators of social facilitation effects. Journal of Experimental Social Psychology, 14(3), 291–303. https://doi.org/10.1016/0022-1031(78)90017-3.
Schermerhorn, P., Scheutz, M., & Crowell, C. R. (2008). Robot social presence and gender: Do females view robots differently than males? HRI 2008 Proceedings of the 3rd ACM/IEEE International Conference on Human-Robot Interaction: Living with Robots. https://doi.org/10.1145/1349822.1349857
Serrano-Cinca, C., Fuertes-Callén, Y., & Mar-Molinero, C. (2005). Measuring DEA efficiency in Internet companies. Decision Support Systems, 38(4), 557–573. https://doi.org/10.1016/j.dss.2003.08.004.
Spatola, N., Belletier, C., Chausse, P., Augustinova, M., Normand, A., Barra, V., et al. (2019a). Improved Cognitive Control in Presence of Anthropomorphized Robots. International Journal of Social Robotics, 11(3), 463–476. https://doi.org/10.1007/s12369-018-00511-w.
Spatola, N., Belletier, C., Normand, A., Chausse, P., Monceau, S., Augustinova, M., et al. (2018). Not as bad as it seems: When the presence of a threatening humanoid robot improves human performance. Science Robotics, 3(21), aat5843. https://doi.org/10.1126/scirobotics.aat5843.
Spatola, N., Monceau, S., & Ferrand, L. (2019b). Cognitive impact of Social Robots: How anthropomorphism boosts performance. IEEE Robotics and Automation Magazine. https://doi.org/10.1109/MRA.2019.2928823.
Stankov, L., & Schweizer, K. (2007). Raven’s progressive matrices, manipulations of complexity and measures of accuracy, speed and confidence. Psychology Science, 49(4), 326–342.
Suls, J., Martin, R., & Wheeler, L. (2002). Social comparison: Why, with whom, and with what effect? Current Directions in Psychological Science, 11(5), 159–163. https://doi.org/10.1111/1467-8721.00191.
Tanaka, K., Nakanishi, H., & Ishiguro, H. (2014). Comparing video, avatar, and robot mediated communication: pros and cons of embodiment. Collaboration Technologies and Social Computing, 460, 96–110. https://doi.org/10.1007/978-3-662-44651-5_9.
Tesser, A. (1988). Toward a self-evaluation maintenance model of social behavior. Advances in Experimental Social Psychology, 21(C), 181–227. https://doi.org/10.1016/S0065-2601(08)60227-0.
Testa, M., & Major, B. (1990). The impact of social comparisons after failure: The moderating effects of perceived control. Basic and Applied Social Psychology, 11(2), 205–218. https://doi.org/10.1207/s15324834basp1102_7.
Vandierendonck, A. (2017). A comparison of methods to combine speed and accuracy measures of performance: A rejoinder on the binning procedure. Behavior Research Methods. https://doi.org/10.3758/s13428-016-0721-5.
Vandierendonck, A. (2018). Further tests of the utility of integrated speed-accuracy measures in task switching. Journal of Cognition. https://doi.org/10.5334/joc.6.
Wood, J. V. (1989). Theory and research concerning social comparisons of personal attributes. Psychological Bulletin, 106, 231–248. https://doi.org/10.1037/0033-2909.106.2.231.
Conflict of interest
The authors declare that they have no conflict of interest.
This study was approved by the Clermont-Ferrand IRM UCA Ethics Committee (Ref.: IRB00011540-2018-23) and was carried out in accordance with the provisions of the World Medical Association Declaration of Helsinki.
All data are publicly available via the Open Science Framework and can be accessed at https://osf.io/7yd9v/.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
About this article
Cite this article
Spatola, N., Normand, A. Human vs. machine: the psychological and behavioral consequences of being compared to an outperforming artificial agent. Psychological Research 85, 915–925 (2021). https://doi.org/10.1007/s00426-020-01317-0
- Human–machine interaction
- Social comparison
- Logical reasoning
- Cognitive control