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What can the implicit social cognition literature teach us about implicit social cognition?

Published online by Cambridge University Press:  13 May 2022

Benedek Kurdi  and
Yarrow Dunham Open the ORCID record for Yarrow Dunham [Opens in a new window]
Benedek Kurdi
Affiliation:
Department of Psychology, Yale University New Haven, CT 06511. [email protected]; http://www.benedekkurdi.com [email protected]; http://www.socialcogdev.com
Yarrow Dunham
Affiliation:
Department of Psychology, Yale University New Haven, CT 06511. [email protected]; http://www.benedekkurdi.com [email protected]; http://www.socialcogdev.com
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Abstract

We highlight several sets of findings from the past decade elucidating the relationship between implicit social cognition and real-world inequality: Studies focusing on practical ramifications of implicit social cognition in applied contexts, the relationship between implicit social cognition and consequential real-world outcomes at the level of individuals and geographic units, and convergence between individual-level and corpus-based measures of implicit bias.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 45 , 2022 , e80
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Agerström, J., & Rooth, D.-O. (2011). The role of automatic obesity stereotypes in real hiring discrimination. Journal of Applied Psychology, 96(4), 790–805. http://doi.org/10.1037/a0021594.CrossRefGoogle ScholarPubMed
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186. http://doi.org/10.1126/science.aal4230.CrossRefGoogle ScholarPubMed
Caliskan, A., & Lewis, M. (2020). Social biases in word embeddings and their relation to human cognition. PsyArXiv. http://doi.org/10.31234/osf.io/d84kg.Google Scholar
Charlesworth, T. E. S., & Banaji, M. R. (2019). Patterns of implicit and explicit attitudes: I. Long-term change and stability from 2007 to 2016. Psychological Science, 30(2), 174–192. http://doi.org/10.1177/0956797618813087.CrossRefGoogle ScholarPubMed
Charlesworth, T. E. S., Yang, V., Mann, T. C., Kurdi, B., & Banaji, M. R. (2021). Gender stereotypes in natural language: Word embeddings show robust consistency across child and adult language corpora of more than 65 million words. Psychological Science, Psychological Science, 32(2), 218–240. http://doi.org/10.1177/0956797620963619.CrossRefGoogle ScholarPubMed
Chetty, R., Hendren, N., Jones, M. R., & Porter, S. R. (2020). Race and economic opportunity in the United States: An intergenerational perspective. The Quarterly Journal of Economics, 135(2), 711–783. http://doi.org/10.1093/qje/qjz042.CrossRefGoogle Scholar
Dasgupta, N., & Asgari, S. (2004). Seeing is believing: Exposure to counterstereotypic women leaders and its effect on the malleability of automatic gender stereotyping. Journal of Experimental Social Psychology, 40(5), 642–658. http://doi.org/10.1016/j.jesp.2004.02.003.CrossRefGoogle Scholar
Dunham, Y., Baron, A. S., & Banaji, M. R. (2008). The development of implicit intergroup cognition. Trends in Cognitive Sciences, 12(7), 248–253. http://doi.org/10.1016/j.tics.2008.04.006.CrossRefGoogle ScholarPubMed
Glover, D., Pallais, A., & Pariente, W. (2017). Discrimination as a self-fulfilling prophecy: Evidence from French grocery stores. The Quarterly Journal of Economics, 132(3), 1219–1260. http://doi.org/10.1093/qje/qjx006.CrossRefGoogle Scholar
Hagiwara, N., Penner, L. A., Gonzalez, R., Eggly, S., Dovidio, J. F., Gaertner, S. L. (2013). Racial attitudes, physician-patient talk time ratio, and adherence in racially discordant medical interactions. Social Science & Medicine, 87(C), 123–131. http://doi.org/10.1016/j.socscimed.2013.03.016.CrossRefGoogle ScholarPubMed
Hehman, E., Calanchini, J., Flake, J. K., & Leitner, J. B. (2019). Establishing construct validity evidence for regional measures of explicit and implicit racial bias. Journal of Experimental Psychology: General, 148(6), 1022–1040. http://doi.org/10.1037/xge0000623.CrossRefGoogle ScholarPubMed
Hehman, E., Flake, J. K., & Calanchini, J. (2018). Disproportionate use of lethal force in policing is associated with regional racial biases of residents. Social Psychological and Personality Science, 9(4), 393–401. http://doi.org/10.1177/1948550617711229.CrossRefGoogle Scholar
Kurdi, B., & Dunham, Y. (2020). Propositional accounts of implicit evaluation: Taking stock and looking ahead. Social Cognition, 38(Supplement), s42–s67. http://doi.org/10.1521/soco.2020.38.supp.s42.CrossRefGoogle Scholar
Kurdi, B., Mann, T. C., Charlesworth, T. E. S., & Banaji, M. R. (2019a). The relationship between implicit intergroup attitudes and beliefs. Proceedings of the National Academy of Sciences, 21, 201820240–10. http://doi.org/10.1073/pnas.1820240116.Google Scholar
Kurdi, B., Seitchik, A. E., Axt, J. R., Carroll, T. J., Karapetyan, A., Kaushik, N. (2019b). Relationship between the implicit association test and intergroup behavior: A meta-analysis. American Psychologist, 74(5), 569–586. http://doi.org/10.1037/amp0000364.CrossRefGoogle Scholar
Lovakov, A., & Agadullina, E. R. (2021). Empirically derived guidelines for effect size interpretation in social psychology. European Journal of Social Psychology, 51(3), 485–504. http://doi.org/10.1002/ejsp.2752.CrossRefGoogle Scholar
Olson, K. R., Key, A. C., & Eaton, N. R. (2015). Gender cognition in transgender children. Psychological Science, 26(4), 467–474. http://doi.org/10.1177/0956797614568156.CrossRefGoogle ScholarPubMed
Payne, B. K., Vuletich, H. A., & Lundberg, K. B. (2017). The bias of crowds: How implicit bias bridges personal and systemic prejudice. Psychological Inquiry, 28(4), 233–248. http://doi.org/10.1080/1047840X.2017.1335568.CrossRefGoogle Scholar
Penner, L. A., Dovidio, J. F., Gonzalez, R., Albrecht, T. L., Chapman, R., Foster, T. … Eggly, S. (2016). The effects of oncologist implicit racial bias in racially discordant oncology interactions. Journal of Clinical Oncology, 34(24), 2874–2880. http://doi.org/10.1200/JCO.2015.66.3658.CrossRefGoogle ScholarPubMed
Penner, L. A., Dovidio, J. F., West, T. V., Gaertner, S. L., Albrecht, T. L., Dailey, R. K., & Markova, T. (2010). Aversive racism and medical interactions with black patients: A field study. Journal of Experimental Social Psychology, 46(2), 436–440. http://doi.org/10.1016/j.jesp.2009.11.004.CrossRefGoogle ScholarPubMed
Riddle, T., & Sinclair, S. (2019). Racial disparities in school-based disciplinary actions are associated with county-level rates of racial bias. Proceedings of the National Academy of Sciences, 116(17), 8255–8260. http://doi.org/10.1073/pnas.1808307116.CrossRefGoogle ScholarPubMed
Steffens, M. C., Jelenec, P., & Noack, P. (2010). On the leaky math pipeline: Comparing implicit math–gender stereotypes and math withdrawal in female and male children and adolescents. Journal of Educational Psychology, 102(4), 947–963. http://doi.org/10.1037/a0019920.CrossRefGoogle Scholar