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Extreme opponents of genetically modified foods know the least but think they know the most

Nature Human Behaviour (2019) | Download Citation



There is widespread agreement among scientists that genetically modified foods are safe to consume1,2 and have the potential to provide substantial benefits to humankind3. However, many people still harbour concerns about them or oppose their use4,5. In a nationally representative sample of US adults, we find that as extremity of opposition to and concern about genetically modified foods increases, objective knowledge about science and genetics decreases, but perceived understanding of genetically modified foods increases. Extreme opponents know the least, but think they know the most. Moreover, the relationship between self-assessed and objective knowledge shifts from positive to negative at high levels of opposition. Similar results were obtained in a parallel study with representative samples from the United States, France and Germany, and in a study testing attitudes about a medical application of genetic engineering technology (gene therapy). This pattern did not emerge, however, for attitudes and beliefs about climate change.

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  1. 1.

    AAAS. Statement by the AAAS Board of Directors on Labeling of Genetically Modified Foods AAAS.com https://www.aaas.org/news/statement-aaas-board-directors-labeling-genetically-modified-foods (2012).

  2. 2.

    Economidis, I., Cichocka, D. & Hoegel, J. A Decade of EU-funded GMO Research (2001–2010). https://doi.org/10.2777/97784 (Publications Office of the European Union, 2010).

  3. 3.

    Sharma, S., Kaur, R. & Singh, A. Recent advances in CRISPR/Cas mediated genome editing for crop improvement. Plant Biotechnol. Rep. 11, 193–207 (2017).

  4. 4.

    Gaskell, G., Bauer, M. W., Durant, J. & Allum, N. C. Worlds apart? The reception of genetically modified foods in Europe and the U.S. Science 285, 384–387 (1999).

  5. 5.

    Scott, S. E., Inbar, Y. & Rozin, P. Evidence for absolute moral opposition to genetically modified food in the United States. Perspect. Psychol. Sci. 11, 315–324 (2016).

  6. 6.

    Funk, C. & Rainie, L. Public and Scientists’ Views on Science and Society (Pew Research Center, 2015)

  7. 7.

    Bodmer, W. F. The public understanding of science. R. Soc. https://royalsociety.org/~/media/Royal_Society_Content/policy/publications/1985/10700.pdf (1985).

  8. 8.

    Gross, A. G. The roles of rhetoric in the public understanding of science. Public Underst. Sci. 3, 3–23 (1994).

  9. 9.

    Ranney, M. A. & Clark, D. Climate change conceptual change: scientific information can transform attitudes. Top. Cogn. Sci. 8, 49–75 (2016).

  10. 10.

    Costa-Font, M., Gil, J. M. & Traill, W. B. Consumer acceptance, valuation of and attitudes towards genetically modified food: review and implications for food policy. Food Policy 33, 99–111 (2008).

  11. 11.

    Allum, N., Sturgis, P., Tabourazi, D. & Brunton-Smith, I. Science knowledge and attitudes across cultures: a meta-analysis. Public Underst. Sci. 17, 35–54 (2008).

  12. 12.

    Frewer, L. J., Howard, C., Hedderley, D. & Shepherd, R. Reactions to information about genetic engineering: impact of source characteristics, perceived personal relevance, and persuasiveness. Public Underst. Sci. 8, 35–50 (1999).

  13. 13.

    Scholderer, J. & Frewer, L. J. The biotechnology communication paradox: experimental evidence and the need for a new strategy. J. Consum. Policy 26, 125–157 (2003).

  14. 14.

    House, L. et al. Objective and subjective knowledge: impacts on consumer demand for genetically modified foods in the United States and the European Union. AgBioForum 7, 113–123 (2004).

  15. 15.

    Knight, A. J. Differential effects of perceived and objective knowledge measures on perceptions of biotechnology. AgBioForum 8, 221–227 (2006).

  16. 16.

    Alba, J. W. & Hutchinson, J. W. Knowledge calibration: what consumers know and what they think they know. J. Consum. Res. 27, 123–156 (2000).

  17. 17.

    Sloman, S. & Fernbach, P. The Knowledge Illusion: Why We Never Think Alone (Riverhead Books, New York, 2017).

  18. 18.

    Rozenblit, L. & Keil, F. The misunderstood limits of folk science: an illusion of explanatory depth. Cogn. Sci. 26, 521–562 (2002).

  19. 19.

    Kruger, J. & Dunning, D. Unskilled and unaware of it: how difficulties in recognizing one’s own incompetence lead to inflated self-assessments. J. Pers. Soc. Psychol. 77, 1121–1134 (1999).

  20. 20.

    Fernbach, P. M., Rogers, T., Fox, C. R. & Sloman, S. A. Political extremism is supported by an illusion of understanding. Psychol. Sci. 24, 939–946 (2013).

  21. 21.

    Linville, P. W. The complexity−extremity effect and age-based stereotyping. J. Pers. Soc. Psychol. 42, 193–211 (1982).

  22. 22.

    van Prooijen, J.-W. Overclaiming knowledge predicts anti-establishment voting. SPSP 2018 https://osf.io/v73ap/ (2018).

  23. 23.

    Motta, M., Callaghan, T. & Sylvester, S. Knowing less but presuming more: Dunning-Kruger effects and the endorsement of anti-vaccine policy attitudes. Soc. Sci. Med. 211, 274–281 (2018).

  24. 24.

    Lewandowsky, S., Gignac, G. E. & Oberauer, K. The role of conspiracist ideation and worldviews in predicting rejection of science. PLoS One 8, e75637 (2013).

  25. 25.

    Science and Engineering Indicators 2016 https://www.nsf.gov/statistics/2016/nsb20161/uploads/1/10/tt07-03.pdf (NSF, 2016).

  26. 26.

    AAAS Benchmarks for Science Literacy: A Project 2061 Report (Oxford Univ. Press, 1993).

  27. 27.

    Durant, J. R., Evans, G. A. & Thomas, G. P. The public understanding of science. Nature 340, 11–14 (1989).

  28. 28.

    Mielby, H., Sandøe, P. & Lassen, J. The role of scientific knowledge in shaping public attitudes to GM technologies. Public Underst. Sci. 22, 155–168 (2013).

  29. 29.

    Miller, J. D., Scott, E. C. & Okamoto, S. Public acceptance of evolution. Science 313, 765–766 (2006).

  30. 30.

    Hornsey, M. J., Harris, E. A., Bain, P. G. & Fielding, K. S. Meta-analyses of the determinants and outcomes of belief in climate change. Nat. Clim. Change 6, 622–626 (2016).

  31. 31.

    Drummond, C. & Fischhoff, B. Individuals with greater science literacy and education have more polarized beliefs on controversial science topics. Proc. Natl Acad. Sci. USA 114, 9587–9592 (2017).

  32. 32.

    Kahan, D. M., Jenkins-Smith, H. & Braman, D. Cultural cognition of scientific consensus. J. Risk Res. 14, 147–174 (2011).

  33. 33.

    Gaskell, G. et al. Europeans and Biotechnology in 2005: Patterns and Trends. Eurobarometer 64.3 (Eurobarometer, 2006).

  34. 34.

    Tests & Procedures: Gene Therapy. Mayo Clinic https://www.mayoclinic.org/tests-procedures/gene-therapy/about/pac-20384619 (2018).

  35. 35.

    Kahan, D. et al. The polarizing impact of science literacy and numeracy on perceived climate change risks. Nat. Clim. Change 2, 732–735 (2012).

  36. 36.

    van der Linden, S. et al. Culture versus cognition is a false dilemma. Nat. Clim. Change 7, 457 (2017).

  37. 37.

    Sturgis, P. & Allum, N. Science in society: re-evaluating the deficit model of public attitudes. Public Underst. Sci. 13, 55–74 (2004).

  38. 38.

    Simis, M. J., Madden, H., Cacciatore, M. A. & Yeo, S. K. The lure of rationality: why does the deficit model persist in science communication? Public Underst. Sci. 25, 400–414 (2016).

  39. 39.

    Wood, S. L. & Lynch, J. G. Prior knowledge and complacency in new product learning. J. Consum. Res. 29, 416–426 (2002).

  40. 40.

    Bredahl, L., Grunert, K. G. & Frewer, L. J. Consumer attitudes and decision-making with regard to genetically engineered food products—a review of the literature and a presentation of models for future research. J. Consum. Policy 21, 251–277 (1998).

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This research was funded by a grant from Humility & Conviction in Public Life, a project of the University of Connecticut sponsored by the John Templeton Foundation to P.M.F., by the Center for Ethics and Social Responsibility at the University of Colorado, by a National Science Foundation DRMS grant (Award Number: 1559371) to S.E.S. and by an SSHRC grant (Award Number: 435-2017-0304) to Y.I. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank B. de Langhe, D. Lichtenstein, J. Lynch, G. McClelland, L. Min, J. Pomerance, D. Rothschild, S. Shaw and S. Sloman.

Author information


  1. Leeds School of Business, University of Colorado, Boulder, CO, USA

    • Philip M. Fernbach
    •  & Nicholas Light
  2. Olin Business School, Washington University in St. Louis, St. Louis, MO, USA

    • Sydney E. Scott
  3. Department of Psychology, University of Toronto, Toronto, Ontario, Canada

    • Yoel Inbar
  4. Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA

    • Paul Rozin


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P.M.F. devised the paper’s central idea, and independently, as part of a larger study, S.E.S., Y.I. and P.R. considered the same idea. P.M.F. and N.L. developed the predictions and designed Studies 1, 3 and 4. N.L. performed the analyses and P.M.F. supervised the findings. For Study 2, S.E.S., Y.I. and P.R. developed the predictions and design and S.E.S. performed the analysis. P.M.F. and N.L. wrote the original manuscript and all authors contributed to the final manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Philip M. Fernbach.

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