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Indiana University Bloomington

Jerome Busemeyer

Dr. Jerome Busemeyer

Provost Professor


jbusemey [at] | personal website

office: PY 328 | (812)855-4882

lab: Decision Research Laboratory
   PY A300G | (812)856-4678


Dynamic, emotional, and cognitive models of judgment and decision making; neural network models of function learning, interpolation, extrapolation; methodology for comparing and testing complex models of behavior; measurement theory with error contaminated data

Educational Background

  • 1980 - Post Doctoral Fellow, Quantitative Methods, University of Illinois
  • 1979 - Ph.D. University of South Carolina
  • 1976 - M.A. University of South Carolina
  • 1973 - B.A. University of Cincinnati, cum laude

Areas of Study

  • Cognitive Science
  • Dynamic Models
  • Quantitative Methods

Research Topics

  • Dynamic, emotional, and cognitive models of judgment and decision making
  • Neural network models of function learning, interpolation, extrapolation
  • Methodology for comparing and testing complex models of behavior
  • Measurement theory with error contaminated data.

Representative Publications

Johnson, J. G. & Busemeyer, J. R. (2005) A dynamic, computational model of preference reversal phenomena. Psychological Review, 112(4), 841-861.

Yechiam, E. & Busemeyer, J. R. (2005) Comparisons of basic assumptions embedded in learning models for experienced based decision making. Psychonomic Bulletin and Review, 12 (3), 387-402.

McDaniel, M. A. & Busemeyer, J. R. (2005) The conceptual basis of function learning and extrapolation: Comparison of rule and associative based models. Psychonomic Bulletin and Review, 12 (1), 24-42.

Busemeyer, J. R., Wang, Z., & Townsend, J. T. (2006) Quantum dynamics of human decision making. Journal of Mathematical Psychology, 50, 220-241.

Rieskamp, J., Busemeyer, J. R., & Mellers, B. A. (2006) Extending the bounds of rationality: A review of research on preferential choice. Journal of Economic Literature, 44, 631-636.

Diederich, A. & Busemeyer, J. R. (2006) Modeling the effects of payoffs on response bias in a perceptual discrimination task: Threshold bound, drift rate change, or two stage processing hypothesis. Perception and Psychophysics, 97 (1), 51-72.

Yechiam, E., Busemeyer, J. R., Stout, J. C., & Bechara, A. (2005) Using cognitive models to map relations between neuropsychological disorders and human decision making deficits. Psychological Science, 16 (12), 841-861.

Busemeyer, J. R. & Johnson, J. G. (2006) Micro-process models of decision-making. In R. Sun (Ed.) Cambridge Handbook of Computational Cognitive Modeling. Cambridge University Press.

Busemeyer, J.R., Jessup, R. K., Johnson, J.G., & Townsend, J. T. (2006) Building bridges between neural models and complex human decision making behavior. Neural Networks, 19, 1047-1058.

Busemeyer, J. R., Barkan, R., Mehta, S.; & Chatervedi, A. (2007) Context models and models of preferential choice: Implications for Consumer Behavior. Marketing Theory, 7 (1), 39-58.

Yechiam, E. & Busemeyer, J. R. (2008) Evaluating generalizability and parameter consistency in learning models. Games and Economic Behavior, 63, 370-394.

Busemeyer, J. R. & Pleskac, T. (2009) Theoretical tools for understanding and aiding dynamic decision making. Journal of Mathematical Psychology, 53, 126-138.

Johnson, J.G. & Busemeyer, J. R. (2007) A computational model of the attention processes used to generate decision weights in risky decision making. Under revision for Cognition.

Jessup, R. K., Bishara, A. J., & Busemeyer, J. R. (2008) Feedback produces divergence from prospect theory in predictive choice. Psychological Science, 19 (10), 1015-1022.

Ahn, W. Y., Busemeyer, J. R., Wagenmakers, E. J., Stout, J. C. (2009) Comparison of decision learning models using the generalization criterion method. Cognitive Science, 32, 1376-1402.

Pothos, E. M. & Busemeyer, J. R. (2009) A Quantum Probability Explanation for Violations of "Rational" Decision Theory. Proceedings of the Royal Society B, 276 (1165), 2171-2178.

Busemeyer, J. R. & Diederich, A. Cognitive Modeling. Sage.

Pleskac, T. J. & Busemeyer, J. R. (submitted). Two Stage Dynamic Signal Detection Theory: A Dynamic and Stochastic Theory of Confidence, Choice, and Response Time.