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Motivated numeracy and enlightened self-government

Published online by Cambridge University Press:  31 May 2017

DAN M. KAHAN ,
ELLEN PETERS ,
ERICA CANTRELL DAWSON  and
PAUL SLOVIC
DAN M. KAHAN*
Affiliation:
Yale University, USA
ELLEN PETERS
Affiliation:
The Ohio State University, USA
ERICA CANTRELL DAWSON
Affiliation:
Cornell University, USA
PAUL SLOVIC
Affiliation:
Decision Research & University of Oregon, USA
*
*Correspondence to: Yale Law School, PO Box 208215, New Haven, CT 06520, USA. Email: dan.kahan@yale.edu
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Abstract

Why does public conflict over societal risks persist in the face of compelling and widely accessible scientific evidence? We conducted an experiment to probe two alternative answers: the ‘science comprehension thesis’ (SCT), which identifies defects in the public's knowledge and reasoning capacities as the source of such controversies; and the ‘identity-protective cognition thesis’ (ICT), which treats cultural conflict as disabling the faculties that members of the public use to make sense of decision-relevant science. In our experiment, we presented subjects with a difficult problem that turned on their ability to draw valid causal inferences from empirical data. As expected, subjects highest in numeracy – a measure of the ability and disposition to make use of quantitative information – did substantially better than less numerate ones when the data were presented as results from a study of a new skin rash treatment. Also as expected, subjects’ responses became politically polarized – and even less accurate – when the same data were presented as results from the study of a gun control ban. But contrary to the prediction of SCT, such polarization did not abate among subjects highest in numeracy; instead, it increased. This outcome supported ICT, which predicted that more numerate subjects would use their quantitative-reasoning capacity selectively to conform their interpretation of the data to the result most consistent with their political outlooks. We discuss the theoretical and practical significance of these findings.

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Articles
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Behavioural Public Policy , Volume 1 , Issue 1 , May 2017 , pp. 54 - 86
Copyright
Copyright © Cambridge University Press 2017 

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References

Ai, C. and Norton, E. C. (2003), ‘Interaction terms in logit and probit models’, Economics Letters, 80(1): 123–129.CrossRefGoogle Scholar
Akerlof, G. A. and Kranton, R. E. (2000), ‘Economics and identity’, The Quarterly Journal of Economics, 115: 715–753.CrossRefGoogle Scholar
Anderson, E. (1993), Value in ethics and economics, Cambridge, Mass: Harvard University Press.Google Scholar
Baron, J. (1995), ‘Myside bias in thinking about abortion’, Thinking & Reasoning, 1: 221–235.CrossRefGoogle Scholar
Baron, J. (2008), Thinking and deciding, New York: Cambridge University Press.Google Scholar
Breyer, S. G. (1993), Breaking the Vicious Circle: Toward Effective Risk Regulation, Cambridge, MA: Harvard University Press.Google Scholar
Chaiken, S. and Trope, Y. (1999), Dual-process theories in social psychology, New York, NY: Guilford Press.Google Scholar
Chen, S., Duckworth, K. and Chaiken, S. (1999), ‘Motivated Heuristic and Systematic Processing’, Psychological Inquiry, 10: 44–49.CrossRefGoogle Scholar
Cohen, G. L. (2003), ‘Party over Policy: The Dominating Impact of Group Influence on Political Beliefs’, Journal of Personality and Social Psychology, 85: 808–822.CrossRefGoogle ScholarPubMed
Cohen, J. (1988), Statistical Power Analysis for the Behavioral Sciences, Hillsdale, NJ: Lawrence Earlbaum Assocs.Google Scholar
Cohen, J., Cohen, P., West, S. G. and Aiken, L. S. (2003), Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd Edn., Mahwah, N.J.: L. Erlbaum Associates.Google Scholar
Dawson, E., Gilovich, T. and Regan, D. T. (2002), ‘Motivated Reasoning and Performance on the was on Selection Task’, Personality and Social Psychology Bulletin, 28: 1379–1387.CrossRefGoogle Scholar
Dawson, E., Gilovich, T. and Regan, D. T. (2000), Motivated Reasoning and Susceptibility to the “Cell A” Bias. Unpublished manuscript.Google Scholar
Dewey, J. (1910), ‘Science as Subject-Matter and as Method’, Science, 31: 121–127.CrossRefGoogle ScholarPubMed
Gastil, J., Braman, D., Kahan, D. and Slovic, P. (2011), ‘The Cultural Orientation of Mass Political Opinion’, PS: Political Science & Politics, 44: 711–714.Google Scholar
Giner-Sorolla, R. and Chaiken, S. (1997), ‘Selective Use of Heuristic and Systematic Processing Under Defense Motivation’, Personality and Social Psychology Bulletin, 23: 84–97.CrossRefGoogle Scholar
Greene, W. (2010), ‘Testing hypotheses about interaction terms in nonlinear models’, Economics Letters, 107(2): 291–296.CrossRefGoogle Scholar
Hamilton, L. C. (2011), ‘Education, politics and opinions about climate change evidence for interaction effects’, Climatic Change, 104: 231–242.CrossRefGoogle Scholar
Hamilton, L. C., Matthew, J. and Schaefer, A. (2012), ‘Public knowledge and concern about polar-region warming’, Polar Geography, 35: 155–168.CrossRefGoogle Scholar
Hillman, A. L. (2010), ‘Expressive behavior in economics and politics’, European Journal of Political Economy, 26: 403–418.CrossRefGoogle Scholar
Jolls, C., Sunstein, C. R. and Thaler, R. (1998), ‘A Behavioral Approach to Law & Economics’, Stanford Law Review, 50: 1471.CrossRefGoogle Scholar
Jost, J. T., Hennes, E. P., and Lavine, H. (2014), ‘“Hot” political cognition: Its self-, group-, and system-serving purposes’, in Carlson, D. E. (ed.), Oxford handbook of social cognition, New York: Oxford University Press.Google Scholar
Kahan, D. (2010), ‘Fixing the Communications Failure’, Nature, 463: 296–297.CrossRefGoogle ScholarPubMed
Kahan, D. M. (2015), ‘Climate-Science Communication and the Measurement Problem’, Advances in Political Psychology, 36: 1–43.CrossRefGoogle Scholar
Kahan, D. (2012), ‘Why we are poles apart on climate change’, Nature, 488: 255.CrossRefGoogle ScholarPubMed
Kahan, D. M., Jamieson, K. H., Landrum, A. and Winneg, K. (2017), ‘Culturally antagonistic memes and the Zika virus: an experimental test’, Journal of Risk Research, 20(1): 1–40.CrossRefGoogle Scholar
Kahan, D., Braman, D., Cohen, G., Gastil, J. and Slovic, P. (2010), ‘Who Fears the HPV Vaccine, Who Doesn't, and Why? An Experimental Study of the Mechanisms of Cultural Cognition’, Law and Human Behavior, 34: 501–516.CrossRefGoogle ScholarPubMed
Kahan, D. M. (2013), ‘Ideology, Motivated Reasoning, and Cognitive Reflection’, Judgment and Decision Making, 8: 407–424.CrossRefGoogle Scholar
Kahan, D. M. (2012), ‘Cultural Cognition as a Conception of the Cultural Theory of Risk’, in Hillerbrand, R., Sandin, P., Roeser, S. & Peterson, M. (eds.) Handbook of Risk Theory: Epistemology, Decision Theory, Ethics and Social Implications of Risk, London: Springer Ltd, 725–760.CrossRefGoogle Scholar
Kahan, D. M. (2011), ‘The Supreme Court 2010 Term—Foreword: Neutral Principles, Motivated Cognition, and Some Problems for Constitutional Law’, Harvard Law Review, 126: 1.Google Scholar
Kahan, D. M., Braman, D., Gastil, J., Slovic, P. and Mertz, C. K. (2007), ‘Culture and Identity-Protective Cognition: Explaining the White-Male Effect in Risk Perception’, Journal of Empirical Legal Studies, 4: 465–505.CrossRefGoogle Scholar
Kahan, D. M., Braman, D., Monahan, J., Callahan, L. and Peters, E. (2010), ‘Cultural Cognition and Public Policy: The Case of Outpatient Commitment Laws’, Law and Human Behavior, 34: 118–140.CrossRefGoogle ScholarPubMed
Kahan, D. M., Jenkins-Smith, H. and Braman, D. (2011), ‘Cultural Cognition of Scientific Consensus’, Journal of Risk Research, 14: 147–174.CrossRefGoogle Scholar
Kahan, D. M., Peters, E., Wittlin, M., Slovic, P., Ouellette, L. L., Braman, D. and Mandel, G. (2012), ‘The polarizing impact of science literacy and numeracy on perceived climate change risks’, Nature Climate Change, 2: 732–735.CrossRefGoogle Scholar
Kahan, D. M., Slovic, P., Braman, D. and Gastil, J. (2006), ‘Fear of Democracy: A Cultural Evaluation of Sunstein on Risk’, Harvard Law Review, 119: 1071–1109.Google Scholar
Kahneman, D. (2003), ‘Maps of Bounded Rationality: Psychology for Behavioral Economics’, American Economic Review, 93: 1449–1475.CrossRefGoogle Scholar
Keil, F. C. (2010), ‘The Feasibility of Folk Science’, Cognitive Science, 34: 826–862.CrossRefGoogle ScholarPubMed
King, G., Honaker, J., Joseph, A. and Scheve, K. (2001), ‘Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation’, American Political Science Review, 95: 49–69.CrossRefGoogle Scholar
King, G., Tom, M. and Wittenberg, J. (2000), ‘Making the Most of Statistical Analyses: Improving Interpretation and Presentation’, American Journal of Political Science, 44: 347–361.CrossRefGoogle Scholar
Kunda, Z. (1990), ‘The Case for Motivated Reasoning’, Psychological Bulletin, 108: 480–498.CrossRefGoogle ScholarPubMed
Lavine, H., Johnston, C. D. and Steenbergen, M. R. (2012), The ambivalent partisan: how critical loyalty promotes democracy, New York, NY: Oxford University Press.CrossRefGoogle Scholar
Lessig, L. (1995), ‘The Regulation of Social Meaning’, University of Chicago Law Review, 62: 943–1045.CrossRefGoogle Scholar
Liberali, J. M., Reyna, V. F., Furlan, S., Stein, L. M. and Pardo, S. T. (2012), ‘Individual Differences in Numeracy and Cognitive Reflection, with Implications for Biases and Fallacies in Probability Judgment’, Journal of Behavioral Decision Making, 25: 361–381.CrossRefGoogle ScholarPubMed
Lodge, M. and Taber, C. S. (2013), The rationalizing voter, Cambridge, New York: Cambridge University Press.CrossRefGoogle Scholar
Loewenstein, G. F., Weber, E. U., Hsee, C. K. and Welch, N. (2001), ‘Risk as Feelings’, Psychological Bulletin, 127, 267–287.CrossRefGoogle ScholarPubMed
Marx, S. M., Weber, E. U., Orlove, B. S., Leiserowitz, A., Krantz, D. H., Roncoli, C. and Phillips, J. (2007), ‘Communication and mental processes: Experiential and analytic processing of uncertain climate information’, Global Environmental Change – Human and Policy Dimensions, 17(1): 47–58.CrossRefGoogle Scholar
McCaffrey, M. S. and Buhr, S. M. (2008), ‘Clarifying climate confusion: addressing systemic holes, cognitive gaps, and misconceptions through climate literacy’, Physical Geography, 29(6): 512–528.CrossRefGoogle Scholar
McCright, A. M. and Dunlap, R. E. (2013), ‘Bringing ideology in: the conservative white male effect on worry about environmental problems in the USA’, Journal of Risk Research, 16: 211–226.CrossRefGoogle Scholar
Miller, J. D. and Pardo, R. (2000), in Dierkes, M. & Grote, C.v. (eds.) Between understanding and trust: The public, science and technology, Australia: Harwood Academic, 131–156.Google Scholar
Olson, M. (1965), The logic of collective action; public goods and the theory of groups, Cambridge, MA: Harvard University Press.Google Scholar
Pampel, F. C. (2000), Logistic regression: a primer, Thousand Oaks, CA: Sage Publications.CrossRefGoogle Scholar
Peters, E., Västfjäll, D., Slovic, P., Mert, C. K., Maocco, K. and Dickert, S. (2006), ‘Numeracy and Decision Making’, Psychological Science, 17: 407–413.CrossRefGoogle ScholarPubMed
Powers, E. A. (2005), ‘Interpreting logit regressions with interaction terms: an application to the management turnover literature’, Journal of Corporate Finance, 11(3): 504–522.CrossRefGoogle Scholar
Rosenau, J. (2012), ‘Science denial: a guide for scientists’, Trends in microbiology, 20: 567–569.CrossRefGoogle Scholar
Rubin, D. (2004), Multiple imputation for nonresponse in surveys, Hoboken, NJ: Wiley-Interscience.Google Scholar
Rubin, D. B. (1987), Multiple imputation for nonresponse in surveys, New York, NY: Wiley.CrossRefGoogle Scholar
Sherman, D. K. and Cohen, G. L. (2006), Advances in Experimental Social Psychology, Vol. 38, Academic Press, 183–242.Google Scholar
Smith, E. R. (2000), in Reis, H. T. and Judd, C. M. (eds.) Handbook of Research Methods in Social and Personality Psychology, Cambridge: Cambridge University Press, 17–39.Google Scholar
Stanovich, K. E. and West, R. F. (1998), ‘Who uses base rates and P (D|H)? An analysis of individual differences’, Memory & Cognition, 26: 161–179.CrossRefGoogle ScholarPubMed
Stanovich, K. E. and West, R. F. (2000), ‘Individual differences in reasoning: Implications for the rationality debate?’ Behavioral and Brain Sciences, 23(5): 645–665.CrossRefGoogle ScholarPubMed
Stanovich, K. E. (2009), What Intelligence Tests Miss: The Psychology of Rational Thought, New Haven, CT: Yale University Press.Google Scholar
Stanovich, K. E. (2013), ‘Why humans are (sometimes) less rational than other animals: Cognitive complexity and the axioms of rational choice’, Thinking & Reasoning, 19: 1–26.CrossRefGoogle Scholar
Stanovich, K. E., West, R. F. and Toplak, M. E. (2011), in Evans, J. S. B. T., Manktelow, K. I., Over, D. E. and Elqayam, S. eds.) The science of reason: a Festschrift for Jonathan St. B.T Evans, New York, NY: Psychology Press, 355–396.Google Scholar
Streiner, D. L. (2003), ‘Unicorns Do Exist: A Tutorial on “Proving” the Null Hypothesis’, Canadian Journal of Psychiatry, 48: 756–761.CrossRefGoogle ScholarPubMed
Sunstein, C. R. (2005), Laws of Fear: Beyond the Precautionary Principle, Cambridge, UK ; New York, NY: Cambridge University Press.CrossRefGoogle Scholar
Sunstein, C. R. (2006), ‘Misfearing: A Reply’, Harvard Law Review, 119: 1110–1125.Google Scholar
Sunstein, C. R. (2007), ‘On the Divergent American Reactions to Terrorism and Climate Change’, Columbia Law Review, 107: 503–557.Google Scholar
Sunstein, C. R. (2003), ‘What's Available? Social Influences and Behavioral Economics’, Northwestern University Law Review, 97.Google Scholar
Toplak, M., West, R. and Stanovich, K. (2011), ‘The Cognitive Reflection Test as a predictor of performance on heuristics-and-biases tasks’, Memory & Cognition, 39(7): 1275–1289.CrossRefGoogle ScholarPubMed
Wasserman, E. A., Dorner, W. W. and Kao, S. F. (1990), ‘Contributions of Specific Cell Information to Judgments of Interevent Contingency’, Journal of Experimental Psychology, Learning, Memory, and Cognition, 16(3): 509–521.CrossRefGoogle ScholarPubMed
Watts, D. J. (2011), Everything is Obvious: Once You Know the Answer: How Common Sense Fails, Atlantic Books.Google Scholar
Weber, E. (2006), ‘Experience-Based and Description-Based Perceptions of Long-Term Risk: Why Global Warming Does Not Scare Us (yet)’, Climatic Change, 77: 103–120.CrossRefGoogle Scholar
Weber, E. U. and Stern, P. C. (2011), ‘Public Understanding of Climate Change in the United States’, American Psychologist, 66: 315–328.CrossRefGoogle ScholarPubMed
Weller, J. A., Dieckmann, N. F., Tusler, M., Mertz, C., Burns, W. J. and Peters, E. (2012), ‘Development and testing of an abbreviated numeracy scale: A Rasch analysis approach’, Journal of Behavioral Decision Making, 26: 198–212.CrossRefGoogle Scholar
Westen, D., Blagov, P., Harenski, K., Kilts, C. and Hamann, S. (2006), ‘Neural Bases of Motivated Reasoning: An fMRI Study of Emotional Constraints on Partisan Political Judgment in the 2004 U.S. Presidential Election’, Journal of Cognitive Neuroscience, 18: 1947–1958.CrossRefGoogle ScholarPubMed