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Individual Differences in Beliefs

from Part III - Variation in Beliefs

Published online by Cambridge University Press:  03 November 2022

Julien Musolino
Rutgers University, New Jersey
Joseph Sommer
Rutgers University, New Jersey
Pernille Hemmer
Rutgers University, New Jersey
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The Cognitive Science of Belief
A Multidisciplinary Approach
, pp. 463 - 512
Publisher: Cambridge University Press
Print publication year: 2022

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Abelson, R. P. (1986) Beliefs are like possessions. Journal of the Theory of Social Behaviour, 16, 223250.CrossRefGoogle Scholar
Abelson, R. P. (1988) Conviction. American Psychologist, 43, 267275.CrossRefGoogle Scholar
Aczel, B., Bago, B., Szollosi, A., Foldes, A., & Lukacs, B. (2015) Measuring individual differences in decision biases: methodological considerations. Frontiers in Psychology, 6(1770). ScholarPubMed
Ahn, W.-Y., Kishida, K., Gu, X. et al. (2014) Nonpolitical images evoke neural predictors of political ideology. Current Biology, 24(22), 26932699.Google Scholar
Alloy, L. B. & Tabachnik, N. (1984) Assessment of covariation by humans and animals: the joint influence of prior expectations and current situational information. Psychological Review, 91, 112149.CrossRefGoogle ScholarPubMed
Babcock, L., Loewenstein, G., Issacharoff, S., & Camerer, C. (1995) Biased judgments of fairness in bargaining. The American Economic Review, 85, 13371343.Google Scholar
Blackmore, S. (1999) The meme machine. Oxford University Press.Google Scholar
Block, J. & Block, J. H. (2006) Nursery school personality and political orientation two decades later. Journal of Research in Personality, 40, 734749.CrossRefGoogle Scholar
Bolsen, T. & Palm, R. (2020) Motivated reasoning and political decision making. In Thompson, W. (Ed.). Oxford Research Encyclopedia, Politics. Scholar
Bouchard, T. J. & McGue, M. (2003) Genetic and environmental influences on human psychological differences. Journal of Neurobiology, 54, 445.Google Scholar
Bovens, L. & Hartmann, P. (2003) Bayesian epistemology. Oxford University Press.Google Scholar
Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2007) Individual differences in adult decision-making competence. Journal of Personality and Social Psychology, 92, 938956.CrossRefGoogle ScholarPubMed
Clark, C. J., Liu, B. S., Winegard, B. M., & Ditto, P. H. (2019) Tribalism is human nature. Current Directions in Psychological Science, 28, 587592.CrossRefGoogle Scholar
Clark, C. J. & Winegard, B. M. (2020) Tribalism in war and peace: the nature and evolution of ideological epistemology and its significance for modern social science. Psychological Inquiry, 31, 122.Google Scholar
Clements, Z. A. & Munro, G. D. (2021) Biases and their impact on opinions of transgender bathroom usage. Journal of Applied Social Psychology, 51 370383. Scholar
Dawes, R. M. (1989) Statistical criteria for establishing a truly false consensus effect. Journal of Experimental Social Psychology, 25, 117.CrossRefGoogle Scholar
Dawes, R. M. (1990) The potential nonfalsity of the false consensus effect. In Hogarth, R. M. (Ed.). Insights into decision making (pp. 179199). University of Chicago Press.Google Scholar
De Neve, J.-E. (2015) Personality, childhood experience, and political ideology. Political Psychology, 36, 5573.CrossRefGoogle ScholarPubMed
Dennett, D. C. (1995) Darwin’s dangerous idea: evolution and the meanings of life. Simon & Schuster.CrossRefGoogle Scholar
Dennett, D. C. (2017) From bacteria to Bach and back. Norton.Google Scholar
Dentakos, S., Saoud, W., Ackerman, R., & Toplak, M. E. (2019) Does domain matter? Monitoring accuracy across domains. Metacognition and Learning, 14, 413436.–019-09198-4Google Scholar
Ditto, P., Liu, B., Clark, C. et al. (2019a) At least bias Is bipartisan: a meta-analytic comparison of partisan bias in liberals and conservatives. Perspectives on Psychological Science, 14, 273291.Google Scholar
Ditto, P., Liu, B., Clark, C. et al. (2019b) Partisan bias and its discontents. Perspectives on Psychological Science, 14, 304316.Google Scholar
Druckman, J. N. (2012) The politics of motivation. Critical Review, 24(2), 199216.Google Scholar
Drummond, C. & Fischhoff, B. (2017) Individuals with greater science literacy and education have more polarized beliefs on controversial science topics. Proceedings of the National Academy of Sciences, 114(36), 9587.Google Scholar
Drummond, C. & Fischhoff, B. (2019) Does “putting on your thinking cap” reduce myside bias in evaluation of scientific evidence? Thinking & Reasoning, 25, 477505.Google Scholar
Edwards, K. & Smith, E. E. (1996) A disconfirmation bias in the evaluation of arguments. Journal of Personality and Social Psychology, 71, 524.Google Scholar
Ehret, P. J., Sparks, A. C., & Sherman, D. K. (2017) Support for environmental protection: an integration of ideological-consistency and information-deficit models. Environmental Politics, 26, 253277.CrossRefGoogle Scholar
Eichmeier, A. & Stenhouse, N. (2019) Differences that don’t make much difference: Party asymmetry in open-minded cognitive styles has little relationship to information processing behavior. Research & Politics, 6(3), 2053168019872045. Scholar
Epley, N. & Gilovich, T. (2016) The mechanics of motivated reasoning. Journal of Economic Perspectives, 30(3), 133140.Google Scholar
Evans, J. St. B. T. (2019) Reflections on reflection: the nature and function of Type 2 processes in dual-process theories of reasoning. Thinking and Reasoning, 25, 383415.CrossRefGoogle Scholar
Evans, J. St. B. T., Over, D. E., & Manktelow, K. (1993) Reasoning, decision making and rationality. Cognition, 49, 165187.CrossRefGoogle ScholarPubMed
Finucane, M. L. & Gullion, C. M. (2010) Developing a tool for measuring the decision-making competence of older adults. Psychology and Aging, 25, 271288.Google Scholar
Fraley, R. C., Griffin, B. N., Belsky, J., & Roisman, G. I. (2012) Developmental antecedents of political ideology: A longitudinal investigation from birth to age 18 years. Psychological Science, 23, 14251431.Google Scholar
Frederick, S. (2005) Cognitive reflection and decision making. Journal of Economic Perspectives, 19, 2542.Google Scholar
Funk, C. L., Smith, K. B., Alford, J. R. et al. (2013) Genetic and environmental transmission of political orientations. Political Psychology, 34, 805819.Google Scholar
Gentzkow, M. & Shapiro, J. (2006) Media bias and reputation. Journal of Political Economy, 114, 280316.Google Scholar
Greene, J. D. (2013) Moral tribes. Penguin Press.Google Scholar
Guay, B. & Johnston, C. (2021) Ideological asymmetries and the determinants of politically motivated reasoning. American Journal of Political Science, 65.Google Scholar
Hahn, U. & Harris, A. J. L. (2014) What does it mean to be biased: motivated reasoning and rationality. In Ross, B. H. (Ed.). Psychology of Learning and Motivation, Vol. 61(pp. 41102). Academic Press.Google Scholar
Haidt, J. (2012) The righteous mind. Pantheon Books.Google Scholar
Hamilton, L. C. (2011) Education, politics and opinions about climate change evidence for interaction effects. Climatic Change, 104, 231242.CrossRefGoogle Scholar
Harris, E. A. & Van Bavel, J. J. (2021) Preregistered replication of “feeling superior is a bipartisan issue: extremity (not direction) of political views predicts perceived belief superiority”. Psychological Science, 32, 451458.Google Scholar
Hart, W., Albarracin, D., Eagly, A. H., Brechan, I., Lindberg, M. J., & Merrill, L. (2009) Feeling validated versus being correct: a meta-analysis of selective exposure to information. Psychological Bulletin, 135, 555588.Google Scholar
Hatemi, P. K. & McDermott, R. (2016) Give me attitudes. Annual Review of Political Science, 19, 331350.Google Scholar
Henry, P. J. & Napier, J. L. (2017) Education is related to greater ideological prejudice. Public Opinion Quarterly, 81, 930942.Google Scholar
Hoch, S. J. (1987) Perceived consensus and predictive accuracy: the pros and cons of projection. Journal of Personality and Social Psychology, 53, 221234.CrossRefGoogle Scholar
Houston, D. A. & Fazio, R. H. (1989) Biased processing as a function of attitude accessibility: making objective judgments subjectively. Social Cognition, 7, 5166.CrossRefGoogle Scholar
Hufer, A., Kornadt, A. E., Kandler, C., & Riemann, R. (2020) Genetic and environmental variation in political orientation in adolescence and early adulthood: a nuclear twin family analysis. Journal of Personality and Social Psychology, 118, 762776.CrossRefGoogle ScholarPubMed
Jones, P. E. (2019) Partisanship, political awareness, and retrospective evaluations, 1956–2016. Political Behavior, 42, 12951317. doi:10.1007/s11109-019-09543-yGoogle Scholar
Joslyn, M. R. & Haider-Markel, D. P. (2014) Who knows best? Education, partisanship, and contested facts. Politics & Policy, 42, 919947.CrossRefGoogle Scholar
Kahan, D. M. (2013) Ideology, motivated reasoning, and cognitive reflection. Judgment and Decision Making, 8, 407424.Google Scholar
Kahan, D. M. (2015) Climate-science communication and the measurement problem. Political Psychology, 36, 143.CrossRefGoogle Scholar
Kahan, D. M. & Corbin, J. C. (2016) A note on the perverse effects of actively open-minded thinking on climate-change polarization. Research & Politics, 3(4), 15. Scholar
Kahan, D. M., Hoffman, D. A., Braman, D., Evans, D., & Rachlinski, J. J. (2012) “They saw a protest”: cognitive illiberalism and the speech-conduct distinction. Stanford Law Review, 64(4), 851906.Google Scholar
Kahan, D. M., Peters, E., Dawson, E., & Slovic, P. (2017) Motivated numeracy and enlightened self-government. Behavioural Public Policy, 1, 5486.Google Scholar
Kahan, D. M., Peters, E., Wittlin, M. et al. (2012) The polarizing impact of science literacy and numeracy on perceived climate change risks. Nature Climate Change, 2, 732735.CrossRefGoogle Scholar
Kahan, D. M. & Stanovich, K. E. (2016) Rationality and belief in human evolution. Annenberg Public Policy Center Working Paper No. 5. Scholar
Kahneman, D. (2011) Thinking, fast and slow. Farrar, Straus & Giroux.Google Scholar
Klaczynski, P. A. (1997) Bias in adolescents’ everyday reasoning and its relationship with intellectual ability, personal theories, and self-serving motivation. Developmental Psychology, 33, 273283.CrossRefGoogle ScholarPubMed
Klaczynski, P. A. (2014) Heuristics and biases: interactions among numeracy, ability, and reflectiveness predict normative responding. Frontiers in Psychology, 5, 113.CrossRefGoogle ScholarPubMed
Klaczynski, P. A., & Lavallee, K. L. (2005) Domain-specific identity, epistemic regulation, and intellectual ability as predictors of belief-based reasoning: A dual-process perspective. Journal of Experimental Child Psychology, 92, 124.Google Scholar
Klaczynski, P. A., & Robinson, B. (2000) Personal theories, intellectual ability, and epistemological beliefs: adult age differences in everyday reasoning tasks. Psychology and Aging, 15, 400416.Google Scholar
Koehler, J. J. (1993) The influence of prior beliefs on scientific judgments of evidence quality. Organizational Behavior and Human Decision Processes, 56, 2855.Google Scholar
Kokis, J., Macpherson, R., Toplak, M., West, R. F., & Stanovich, K. E. (2002) Heuristic and analytic processing: age trends and associations with cognitive ability and cognitive styles. Journal of Experimental Child Psychology, 83, 2652.Google Scholar
Kornblith, H. (1993) Inductive inference and its natural ground. MIT University Press.Google Scholar
Kraft, P. W., Lodge, M., & Taber, C. S. (2015) Why people “Don’t trust the evidence”: motivated reasoning and scientific beliefs. The Annals of the American Academy of Political and Social Science, 658(1), 121133.Google Scholar
Kuhn, D. & Modrek, A. (2018) Do reasoning limitations undermine discourse? Thinking & Reasoning, 24, 97116.Google Scholar
Liberali, J. M., Reyna, V. F., Furlan, S., Stein, L. M., & 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, 361381.CrossRefGoogle ScholarPubMed
Ludeke, S., Johnson, W., & Bouchard, T. J. (2013) “Obedience to traditional authority:” a heritable factor underlying authoritarianism, conservatism and religiousness. Personality and Individual Differences, 55, 375380.CrossRefGoogle Scholar
Lupia, A., Levine, A. S., Menning, J. O., & Sin, G. (2007) Were Bush tax cut supporters “simply ignorant?” A second look at conservatives and liberals in “Homer Gets a Tax Cut.” Perspectives on Politics, 5, 773784.Google Scholar
Macpherson, R. & Stanovich, K. E. (2007) Cognitive ability, thinking dispositions, and instructional set as predictors of critical thinking. Learning and Individual Differences, 17, 115127.Google Scholar
Mercier, H. & Sperber, D. (2017) The enigma of reason. Harvard University Press.Google Scholar
O’Connor, C. & Weatherall, J. O. (2018) Scientific polarization. European Journal for Philosophy of Science, 8, 855875.Google Scholar
Olsson, E. J. (2013) A Bayesian simulation model of group deliberation and polarization. In Zenker, F. (Ed.). Bayesian argumentation (pp. 113133). Springer.Google Scholar
Oskarsson, S., Cesarini, D., Dawes, C. et al. (2015) Linking genes and political orientations: testing the cognitive ability as mediator hypothesis. Political Psychology, 36, 649665.Google Scholar
Parker, A. M., Bruine de Bruin, W., Fischhoff, B., & Weller, J. (2018) Robustness of decision-making competence: evidence from two measures and an 11-year longitudinal study. Journal of Behavioral Decision Making, 31, 380391.CrossRefGoogle Scholar
Parker, A. M. & Fischhoff, B. (2005) Decision-making competence: external validation through an individual differences approach. Journal of Behavioral Decision Making, 18, 127.Google Scholar
Patel, N., Baker, S. G., & Scherer, L. D. (2019) Evaluating the cognitive reflection test as a measure of intuition/reflection, numeracy, and insight problem solving, and the implications for understanding real-world judgments and beliefs. Journal of Experimental Psychology: General, 148, 21292153.Google Scholar
Perkins, D. N. (1985). Postprimary education has little impact on informal reasoning. Journal of Educational Psychology, 77, 562571.Google Scholar
Perkins, D. N., Farady, M., & Bushey, B. (1991) Everyday reasoning and the roots of intelligence. In Voss, J., Perkins, D., & Segal, J. (Eds.). Informal reasoning and education (pp. 83105). Erlbaum.Google Scholar
Ross, L., Greene, D., & House, P. (1977) The “false consensus effect”: an egocentric bias in social perception and attribution processes. Journal of Experimental Social Psychology, 13, 279301.Google Scholar
, W., West, R. F., & Stanovich, K. E. (1999) The domain specificity and generality of belief bias: searching for a generalizable critical thinking skill. Journal of Educational Psychology, 91, 497510.Google Scholar
Sarathchandra, D., Navin, M. C., Largent, M. A., & McCright, A. M. (2018) A survey instrument for measuring vaccine acceptance. Preventive Medicine, 109, 17.Google Scholar
Simas, E. N., Clifford, S., & Kirkland, J. H. (2020). How empathic concern fuels political polarization. American Political Science Review, 114, 258269.Google Scholar
Sinayev, A. & Peters, E. (2015) Cognitive reflection vs. calculation in decision making. Frontiers in Psychology, 6(532). doi:10.3389/fpsyg.2015.00532CrossRefGoogle ScholarPubMed
Sloman, S. & Fernbach, P. M. (2017) The knowledge illusion. Riverhead Books.Google Scholar
Spearman, C. (1904) General intelligence, objectively determined and measured. American Journal of Psychology, 15, 201293.Google Scholar
Spearman, C. (1927) The abilities of man. Macmillan.Google Scholar
Stanovich, K. E. (1999) Who is rational? Studies of individual differences in reasoning. Erlbaum.CrossRefGoogle Scholar
Stanovich, K. E. (2004) The robot’s rebellion: finding meaning in the age of Darwin. University of Chicago Press.Google Scholar
Stanovich, K. E. (2021) The bias that divides us: The science and politics of myside thinking. MIT Press.CrossRefGoogle Scholar
Stanovich, K. E. & Toplak, M. E. (2019) The need for intellectual diversity in psychological science: our own studies of actively open-minded thinking as a case study. Cognition, 187, 156166.Google Scholar
Stanovich, K. E. & West, R. F. (1997) Reasoning independently of prior belief and individual differences in actively open-minded thinking. Journal of Educational Psychology, 89, 342357.Google Scholar
Stanovich, K. E. & West, R. F. (1998a) Individual differences in rational thought. Journal of Experimental Psychology: General, 127, 161188.Google Scholar
Stanovich, K. E. & West, R. F. (1998b) Who uses base rates and P(D/~H)? An analysis of individual differences. Memory & Cognition, 26, 161179.CrossRefGoogle Scholar
Stanovich, K. E. & West, R. F. (2000) Individual differences in reasoning: implications for the rationality debate? Behavioral and Brain Sciences, 23, 645726.CrossRefGoogle ScholarPubMed
Stanovich, K. E. & West, R. F. (2007) Natural myside bias is independent of cognitive ability. Thinking & Reasoning, 13, 225247.Google Scholar
Stanovich, K. E. & West, R. F. (2008) On the failure of intelligence to predict myside bias and one-sided bias. Thinking & Reasoning, 14, 129167.CrossRefGoogle Scholar
Stanovich, K. E., West, R. F., & Toplak, M. E. (2016) The rationality quotient: toward a test of rational thinking. MIT Press.CrossRefGoogle Scholar
Stenhouse, N., Myers, T. A., Vraga, E. K., Kotcher, J. E., Beall, L., & Maibach, E. W. (2018) The potential role of actively open-minded thinking in preventing motivated reasoning about controversial science. Journal of Environmental Psychology, 57, 1724.Google Scholar
Taber, C. S. & Lodge, M. (2006) Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science, 50, 755769.CrossRefGoogle Scholar
Tappin, B. M., Pennycook, G., & Rand, D. G. (2020) Thinking clearly about causal inferences of politically motivated reasoning. Current Opinion in Behavioral Sciences, 34, 8187.Google Scholar
Tetlock, P. E. (1986) A value pluralism model of ideological reasoning. Journal of Personality and Social Psychology, 50, 819827.Google Scholar
Tetlock, P. E. (2002) Social functionalist frameworks for judgment and choice: intuitive politicians, theologians, and prosecutors. Psychological Review, 109, 451471.Google Scholar
Toner, K., Leary, M. R., Asher, M. W., & Jongman-Sereno, K. P. (2013) Feeling superior is a bipartisan issue: extremity (not direction) of political views predicts perceived belief superiority. Psychological Science, 24, 24542462.Google Scholar
Toplak, M. E., Liu, E., Macpherson, R., Toneatto, T., & Stanovich, K. E. (2007) The reasoning skills and thinking dispositions of problem gamblers: a dual-process taxonomy. Journal of Behavioral Decision Making, 20, 103124.Google Scholar
Toplak, M. E. & Stanovich, K. E. (2002) The domain specificity and generality of disjunctive reasoning: searching for a generalizable critical thinking skill. Journal of Educational Psychology, 94, 197209.Google Scholar
Toplak, M. E. & Stanovich, K. E. (2003) Associations between myside bias on an informal reasoning task and amount of post-secondary education. Applied Cognitive Psychology, 17, 851860.Google Scholar
Toplak, M. E., West, R. F., & Stanovich, K. E. (2011) The Cognitive Reflection Test as a predictor of performance on heuristics and biases tasks. Memory & Cognition, 39, 12751289.Google Scholar
Toplak, M. E., West, R. F., & Stanovich, K. E. (2014a) Assessing miserly processing: an expansion of the Cognitive Reflection Test. Thinking & Reasoning, 20, 147168.Google Scholar
Toplak, M. E., West, R. F., & Stanovich, K. E. (2014b) Rational thinking and cognitive sophistication: development, cognitive abilities, and thinking dispositions. Developmental Psychology, 50, 10371048.Google Scholar
Tversky, A. & Kahneman, D. (1974) Judgment under uncertainty: heuristics and biases. Science, 185, 11241131.CrossRefGoogle ScholarPubMed
Twito, L. & Knafo-Noam, A. (2020) Beyond culture and the family: evidence from twin studies on the genetic and environmental contribution to values. Neuroscience & Biobehavioral Reviews, 112, 135143.CrossRefGoogle ScholarPubMed
Van Bavel, J. J. & Pereira, A. (2018) The partisan brain: an identity-based model of political belief. Trends in Cognitive Sciences, 22(3), 213224.CrossRefGoogle Scholar
Van Boven, L., Ramos, J., Montal-Rosenberg, R., Kogut, T., Sherman, D. K., & Slovic, P. (2019) It depends: partisan evaluation of conditional probability importance. Cognition, 188, 5163.Google Scholar
Viator, R. E., Harp, N. L., Rinaldo, S. B., & Marquardt, B. B. (2020) The mediating effect of reflective-analytic cognitive style on rational thought. Thinking & Reasoning, 26, 133.Google Scholar
Weaver, E. A. & Stewart, T. R. (2012) Dimensions of judgment: factor analysis of individual differences. Journal of Behavioral Decision Making, 25, 402413.Google Scholar
Weller, J., Ceschi, A., Hirsch, L., Sartori, R., & Costantini, A. (2018) Accounting for individual differences in decision-making competence: personality and gender differences. Frontiers in Psychology, 9, 22582258.Google Scholar
Westen, D., Blagov, P., Kilts, C., & 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, 19471958.Google Scholar
Westwood, S. J., Iyengar, S., Walgrave, S., Leonisio, R., Miller, L., & Strijbis, O. (2018) The tie that divides: cross-national evidence of the primacy of partyism. European Journal of Political Research, 57, 333354.Google Scholar
Wynn, K. (2016) Origins of value conflict: babies do not agree to disagree. Trends in Cognitive Sciences, 20(1), 35.CrossRefGoogle Scholar
Yudkin, D., Hawkins, S., & Dixon, T. (2019) The perception gap: how false impressions are pulling Americans apart. More in Common. Scholar


Adomavicius, G., Bockstedt, J., & Curley, S. P. (2015) Bundling effects on variety seeking for digital information goods. Journal of Management Information Systems, 31 (4), 182212.Google Scholar
Bartlema, A., Lee, M. D., Wetzels, R., & Vanpaemel, W. (2014) A Bayesian hierarchical mixture approach to individual differences: case studies in selective attention and representation in category learning. Journal of Mathematical Psychology, 59, 132150.Google Scholar
Batchelder, W. H. & Anders, R. (2012) Cultural consensus theory: comparing different concepts of cultural truth. Journal of Mathematical Psychology, 56(5), 316332.Google Scholar
Behrend, T. S., Sharek, D. J., Meade, A. W., & Wiebe, E. N. (2011) The viability of crowdsourcing for survey research. Behavior Research Methods, 43(3), 800813.Google Scholar
Böckenholt, U. (1992) Thurstonian representation for partial ranking data. British Journal of Mathematical and Statistical Psychology, 45(1), 3149.CrossRefGoogle Scholar
Böckenholt, U. (1993) Applications of Thurstonian models to ranking data. In Probability Models and Statistical Analyses for Ranking Data (pp. 157172). Springer.Google Scholar
Böckenholt, U. (2006) Thurstonian-based analyses: past, present, and future utilities. Psychometrika, 71(4), 615629.Google Scholar
Brady, H. E. (1990) Dimensional analysis of ranking data. American Journal of Political Science, 34(4), 10171048.Google Scholar
Brooks, S. P. & Gelman, A. (1997) General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7(4), 434455.Google Scholar
Carey, S. (2004) Bootstrapping & the origin of concepts. Daedalus, 133(1), 5968.Google Scholar
Coaley, K. (2014) An introduction to psychological assessment and psychometrics. Sage.Google Scholar
Cronbach, L. J. (1957) The two disciplines of scientific psychology. American Psychologist, 12(11), 671684.Google Scholar
Duncan, O. D. (1984) Notes on social measurement: historical and critical. Russell Sage Foundation.Google Scholar
Farrell, S. & Lewandowsky, S. (2018) Computational modeling of cognition and behavior. Cambridge University Press.Google Scholar
Florig, H. K., Morgan, M., Morgan, K. M., et al. (2001) A deliberative method for ranking risks (i): overview and test bed development. Risk Analysis, 21(5), 913913.Google Scholar
Geiger, D., Seedorf, S., Schulze, T., Nickerson, R. C., & Schader, M. (2011) Managing the crowd: towards a taxonomy of crowdsourcing Processes. In AMCIS Proceedings, 430.Google Scholar
Giles, O., Richards, R., & Markkula, G. (2018) Bayesian analysis of subjective ranking data using Thurstonian models: tutorial, novel methods, and an open-source library. PsyArXiv, Scholar
Healey, M. K. & Kahana, M. J. (2014) Is memory search governed by universal principles or idiosyncratic strategies? Journal of Experimental Psychology: General, 143(2), 575596.Google Scholar
Hemmer, P., Steyvers, M., & Miller, B. (2010) The wisdom of crowds with informative priors. In Ohlsson, S., & Catrambone, R. (Eds.). Proceedings of the 32nd annual conference of the Cognitive Science Society, (pp. 11301135). Cognitive Science Society.Google Scholar
Hult, G. T. M., Neese, W. T., & Bashaw, R. E. (1997) Faculty perceptions of marketing journals. Journal of Marketing Education, 19(1), 3752.Google Scholar
Johnson, T. R. & Kuhn, K. M. (2013) Bayesian Thurstonian models for ranking data using JAGS. Behavior Research Methods, 45(3), 857872.Google Scholar
Kass, R. E. & Raftery, A. E. (1995) Bayes factors. Journal of the American Statistical Association, 90(430), 377395.Google Scholar
Katsikatsou, M. & Yang-Wallentin, F. (2011) On the dentification of the unrestricted Thurstonian model for ranking data. In Working Paper, Department of Statistics (p. 32). Uppsala University.Google Scholar
Kemp, C. & Tenenbaum, J. B. (2008) The discovery of structural form. Proceedings of the National Academy of Sciences, 105(31), 1068710692.Google Scholar
Krabbe, P. F. (2008) Thurstone scaling as a measurement method to quantify subjective health outcomes. Medical Are, 46(4), 357–365.Google Scholar
Lee, M. D. (2011) How cognitive modeling can benefit from hierarchical Bayesian models. Journal of Mathematical Psychology, 55(1), 17.Google Scholar
Lee, M. D. (2006) A hierarchical Bayesian model of human decision making on an optimal stopping problem. Cognitive Science, 30(3), 555580.Google Scholar
Lee, M. D., Bock, J. R., Cushman, I., & Shankle, W. R. (2020) An application of multinomial processing tree models and Bayesian methods to understanding memory impairment. Journal of Mathematical Psychology, 95, 102328.Google Scholar
Lee, M. D., Liu, E. C., & Steyvers, M. (2015) The roles of knowledge and memory in generating top-10 lists. In Noelle, D. C., & Dale, R. (Eds.). Proceedings of the 37th Annual Conference of the Cognitive Science Society (pp. 12671272). Cognitive Science Society.Google Scholar
Lee, M. D., Steyvers, M., de Young, M., & Miller, B. J. (2012) Inferring expertise in knowledge and prediction ranking tasks. Topics in Cognitive Science, 4(1), 151163.Google Scholar
Lee, M. D., Steyvers, M., & Miller, B. J. (2014) A cognitive model for aggregating people’s rankings. PLoS ONE, 9(5), 19.Google Scholar
Lee, M. D. & Vanpaemel, W. (2008). Exemplars, prototypes, similarities and rules in category representation: an example of hierarchical Bayesian analysis. Cognitive Science, 32(8), 14031424.Google Scholar
Lichtenstein, S., Fischoff, B., & Phillips, L. D. (1982) Calibration of probabilities: the state of the art to 1980. In Kahneman, D., Slovic, P., & Tversky, A. (Eds.). Judgment Under Uncertainty: Heuristics and Biases (pp. 306334). Cambridge University Press.Google Scholar
Maydeu-Olivares, A. (1999) Thurstonian modeling of ranking data via mean and covariance structure analysis. Psychometrika, 64(3), 325340.Google Scholar
Miller, G. A. (1956) The magical number seven, plus or minus two. Psychological Review, 63(2), 8197.Google Scholar
Milosavljevic, M., Navalpakkam, V., Koch, C., & Rangel, A. (2012) Relative visual saliency differences induce sizable bias in consumer choice. Journal of Consumer Psychology, 22(1), 6774.Google Scholar
Morgan, K. M., DeKay, M. L., Fischbeck, P. S., Morgan, M. G., Fischhoff, B., & Florig, H. K. (2001) A deliberative method for ranking risks (ii): evaluation of validity and agreement among risk managers. Risk Analysis, 21(5), 923–923.Google Scholar
Oakes, M. E. & Slotterback, C. S. (2002) The good, the bad, and the ugly: characteristics used by young, middle-aged, and older men and women, dieters and non-dieters to judge healthfulness of foods. Appetite, 38(2), 9197.Google Scholar
Orne, M. T. (1962) On the social psychology of the psychological experiment: with particular reference to demand characteristics and their implications. American Psychologist, 17(11), 776783.CrossRefGoogle Scholar
Pearson, R. G. & Byars, G. E. (1956) The development and validation of a checklist for measuring subjective fatigue. Technical report, School of Aviation Medicine, Randolph AFB TX.Google Scholar
Pew, R. W. (1969) The speed-accuracy operating characteristic. Acta Psychologica, 30, 1626.CrossRefGoogle Scholar
Piaget, J. (2003). Part I: cognitive development in children–Piaget development and learning. Journal of Research in Science Teaching, 40(1), 8–18.Google Scholar
Plummer, M. (2003) JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In K. Hornik, F. Leisch, & A. Zeileis (Eds.). Proceedings of the 3rdInternational Workshop on Distributed Statistical Computing (DSC 2003).Google Scholar
Romney, A. K., Batchelder, W. H., & Weller, S. C. (1987) Recent applications of cultural consensus theory. American Behavioral Scientist, 31(2), 163177.Google Scholar
Selker, R., Lee, M. D., & Iyer, R. (2017) Thurstonian cognitive models for aggregating top-n lists. Decision, 4(2), 87101.Google Scholar
Shafir, E. (1993) Choosing versus rejecting: why some options are both better and worse than others. Memory & Cognition, 21(4), 546556.Google Scholar
Shiffrin, R. M., Lee, M. D., Kim, W.-J., & Wagenmakers, E.-J. (2008) A survey of model evaluation approaches with a tutorial on hierarchical Bayesian methods. Cognitive Science, 32(8), 12481284.Google Scholar
Siena College Research Institute (2018) US presidents study historical rankings. Scholar
Surowiecki, J. (2004) The wisdom of crowds. Random House.Google Scholar
Taylor, J. (2006) The rivalry: Bill Russell, Wilt Chamberlain, and the golden age of basketball. Ballantine Books.Google Scholar
Thurstone, L. L. (1927a) A law of comparative judgement. Psychological Review, 34(4), 273286.Google Scholar
Thurstone, L. L. (1927b) The method of paired comparisons for social values. The Journal of Abnormal and Social Psychology, 21(4), 384400.Google Scholar
Van Vaerenbergh, Y., & Thomas, T. D. (2013) Response styles in survey research: a literature review of antecedents, consequences, and remedies. International Journal of Public Opinion Research, 25(2), 195217.Google Scholar
Villarreal, M., Velázquez, C., Baroja, J. L., Segura, A., Bouzas, A., & Lee, M. D. (2019) Bayesian methods applied to the generalized matching law. Journal of the Experimental Analysis of Behavior, 111(2), 252273.Google Scholar
Weber, S. J. & Cook, T. D. (1972) Subject effects in laboratory research: an examination of subject roles, demand characteristics, and valid inference. Psychological Bulletin, 77(4), 273295.Google Scholar
Wetzels, R., Grasman, R. P. P. P., & Wagenmakers, E. (2010) An encompassing prior generalization of the Savage-Dickey density ratio test. Computational Statistics and Data Analysis, 54(9), 20942102.Google Scholar
Wine, B., Gilroy, S., & Hantula, D. A. (2012) Temporal (in) stability of employee preferences for rewards. Journal of Organizational Behavior Management, 32(1), 5864.Google Scholar
Yao, G. & Böckenholt, U. (1999) Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler. British Journal of Mathematical and Statistical Psychology, 52(1), 7992.Google Scholar
Zhang, H. & Maloney, L. T. (2012) Ubiquitous log odds: a common representation of probability and frequency distortion in perception, action and cognition. Frontiers in Neuroscience, 6(1), 114.Google Scholar
Zizzo, D. J. (2010) Experimenter demand effects in economic experiments. Experimental Economics, 13(1), 7598.Google Scholar

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