Abstract
A computational model of political attitudes and beliefs is developed that incorporates contemporary psychological theory with well-documented findings from electoral behavior. We compare this model, John Q. Public (JQP), to a Bayesian learning model via computer simulations of observed changes in candidate evaluations over the 2000 presidential campaign. In these simulations, JQP reproduces responsiveness, persistence, and polarization of political attitudes, while the Bayesian learning model has difficulty accounting for persistence and polarization. We conclude that “motivated reasoning”—the discounting of information that challenges priors along with the uncritical acceptance of attitude-consistent information—is the reason our model can better account for persistence and polarization in candidate evaluations.
Similar content being viewed by others
Notes
Our model is developed within the ACT-R cognitive architecture (The A daptive C haracter of T hought – R ational; Anderson et al. 2004), which is a leading theoretical and modeling framework used in cognitive science for a wide range of learned behaviors, among them language comprehension, the recognition and recall of information, inferencing, the formation of beliefs, and the learning of complex skills. However, while ACT-R provides comprehensive, integrated sets of cognitive mechanisms for learning, it lacks affective mechanisms, which are essential to current theories of political information-processing, and it lacks specific mechanisms for preference updating. Consequently, much of our work developing JQP was devoted to building affective and updating mechanisms and integrating them with the cognitive processes in ACT-R.
This notation for random noise is conventional in computational modeling because it makes transparent the manipulation of the normal density function.
S ji represents the strength of association from node j to node i. It is an increasing function of the number of times node j has sent activation to node i, and it is not symmetrical (Anderson 1993). C ji = 1 when nodes j and i share the same valence (positive or negative), C ji = −1 when they have different valences, and C ji = 0 when either of them is neutral.
The number of buffers could be manipulated to represent greater or lesser cognitive limitations, but we do not explore this implication here.
To test the internal validity of the model (i.e., whether the model behaves as expected in our theory), we conducted a series of purely formal computational experiments (Kim 2005) in which the model successfully reproduced practice, recency, and spreading of activation effects on recall; cognitive and attitude priming effects; question order and wording effects in the survey response; both on-line and memory-based processing; and the ability to learn by adjusting beliefs and attitudes in response to campaign events. These tests were essential to establish that the model is in fact consistent with a wide range of “well-known phenomena” from the empirical cognitive literatures. These tests are available from the first author.
For the trait perceptions to be useful, we need to assign a value to each trait. That is, when a respondent says “Bush is trustworthy,” we need to have some idea how positive this attribution is. Fortunately, most human traits and other general concepts have been normed by large samples of respondents, so we can scale the evaluative implications of a given trait (positivity, negativity, intensity, etc.) by consulting the Affective Norms for English Words (ANEW; Bradley and Lang 1999) and N. Anderson (1968), which provide means and standard deviations for a large number of trait concepts.
Recall also that transient random noise is added to the activation levels of all objects in memory each time an object is retrieved (Eq. 1), which also approximates individual differences in processing.
The average of correlation between the actual and simulated evaluations over time and that across groups was used as a fit measure. However, the same qualitative results were obtained with wide ranges of parameter values.
The parameter search space was extensive; the values within the interval [0, 10] with stride 0.1 for P 0 , h, and q, and those within [0, 1] with stride 0.01 for γ were first examined. Then, the values within [0, 1] with stride 0.01 for γ, h, and q were examined in a refined search. For very small values of h and q (0, 0.01, and 0.02), simulation results and model fits were almost identical. With the optimized parameter values, the weight, K t , did not converge during the simulation.
The values within [0, 1] with stride 0.01 were examined.
Formally, JQP will be a motivated reasoner as long as γ > 0, 0 < δ < 1, 0 < ρ < 1 and its belief structure is reasonably consistent.
References
Abelson, R. (1963). Computer simulation of personality. New York: Wiley.
Achen, C. (1992). Social psychology, demographic variables, and linear regression: Breaking the iron triangle in voting research. Political Behavior, 14, 195–211.
Anderson, N. (1968). Likableness ratings of 555 personality-trait words. Journal of Personality and Social Psychology, 9, 272–279.
Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University.
Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Erlbaum.
Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of mind. Psychological Review, 111, 1036–1060.
Bartels, L. M. (2002). Beyond the running tally: Partisan bias in political perceptions. Political Behavior, 24, 117–150.
Bradley, M., & Lang, P. (1999). Affective norms for English words (ANEW). Gainesville, FL: The NIMH Center for the Study of Emotion and Attention, University of Florida.
Bullock, J. G. (2009). Partisan bias and the Bayesian ideal in the study of public opinion. Journal of Politics, 71, 1109–1124.
Campbell, A., Converse, P. E., Miller, W. E., & Stokes, D. E. (1960). The American voter. New York: Wiley.
Downs, A. (1957). An economic theory of democracy. New York: Harper and Row.
Fazio, R. (2001). On the automatic activation of associated evaluations: An overview. Cognition and Emotion, 15, 115–141.
Gerber, A., & Green, D. P. (1998). Rational learning and partisan attitudes. American Journal of Political Science, 42, 794–818.
Hamill, R., & Lodge, M. (1985). Cognitive consequences of political sophistication. In R. R. Lau & D. O. Sears (Eds.), Political cognition (pp. 69–93). Hillsdale, NJ: Lawrence Erlbaum.
Hastie, R., & Pennington, N. (1989). Notes on the distinction between memory-based and on-line judgment. In J. Bassili (Ed.), On-line cognition in person perception (pp. 1–19). Hillsdale, NJ: Lawrence Erlbaum.
Kim, S. (2005). A model of political information processing: The dynamics of candidate evaluation and strategic behaviors in social dilemma games. Unpublished Dissertation, Stony Brook University.
Kinder, D. R. (1998). Opinion and action in the realm of politics. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (4th ed., pp. 778–867). Boston: McGraw-Hill.
LeDoux, J. (1996). The emotional brain: The mysterious underpinnings of emotional life. New York: Simon and Schuster.
LeDoux, J. (2003). Synaptic self: How our brains become who we are. London: Penguin Books.
Lodge, M., Steenbergen, M., & Brau, S. (1995). The responsive voter: Campaign information and the dynamics of candidate evaluation. American Political Science Review, 89, 309–326.
Lodge, M., & Taber, C. S. (2000). Three steps toward a theory of motivated political reasoning. In A. Lupia, M. McCubbins, & S. Popkin (Eds.), Elements of reason (pp. 183–213). New York: Cambridge University Press.
Lodge, M., & Taber, C. S. (2005). The primacy of affect for political candidates, groups, and issues: An experimental test of the hot cognition hypothesis. Political Psychology, 26, 333–487.
Miller, G. A. (1957). The magic number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–93.
Niemi, R. G., & Jennings, M. K. (1991). Issues and inheritance in the formation of party identification. American Journal of Political Science, 35, 970–988.
Page, B. I., & Shapiro, R. Y. (1992). The rational public. Chicago: University of Chicago Press.
Romer, D., Kenski, K., Waldman, P., Adasiewicz, C., & Jamieson, K. H. (2003). Capturing campaign dynamics: The National Annenberg Election Survey. Oxford: Oxford University Press.
Taber, C. S. (2003). Information processing and public opinion. In D. O. Sears, L. Huddy, & R. Jervis (Eds.), Oxford handbook of political psychology (pp. 433–476). Oxford: Oxford University Press.
Taber, C. S., & Lodge, M. (2006). Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science, 50, 755–769.
Taber, C. S., & Steenbergen, M. R. (1995). Computational experiments in electoral behavior. In M. Lodge & K. McGraw (Eds.), Political judgment: Structure and process (pp. 141–206). Ann Arbor, MI: University of Michigan Press.
Tourangeau, R., Rips, L. J., & Rasinski, K. (2000). The psychology of the survey response. Cambridge: Cambridge University Press.
Zajonc, R. B. (1984). Feeling and thinking: Preferences need no inferences. American Psychologist, 35, 151–175.
Zajonc, R. B. (2000). Feeling and thinking: Closing the debate over the independence of affect. In J. Forgas (Ed.), Feeling and thinking: The role of affect in social cognition (pp. 31–58). Cambridge: Cambridge University Press.
Zaller, J. R. (1992). The nature and origins of mass opinion. Cambridge: University of Cambridge Press.
Zaller, J., & Feldman, S. (1992). A simple theory of the survey response. American Journal of Political Science, 36, 579–616.
Acknowledgments
This research was supported by an NSF dissertation grant to Sung-youn Kim, research grants for Teragrid supercomputing resources (TG-SES050006 N, TG-SES050007 N), and an NSF research grant to Milton Lodge and Charles Taber (SES0241282).
Author information
Authors and Affiliations
Corresponding author
Additional information
Detailed simulation results are available from the first author upon request.
Rights and permissions
About this article
Cite this article
Kim, Sy., Taber, C.S. & Lodge, M. A Computational Model of the Citizen as Motivated Reasoner: Modeling the Dynamics of the 2000 Presidential Election. Polit Behav 32, 1–28 (2010). https://doi.org/10.1007/s11109-009-9099-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11109-009-9099-8