and Brain Sciences
Dr. Jerome Busemeyer
jbusemey [at] indiana.edu | personal website
office: PY 328 | (812)855-4882
lab: Decision Research Laboratory
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
- 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
- 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.
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.
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