Department of
Psychological and Brain Sciences

• Cognitive Science
• Dynamic Models
• Quantitative Methods

• Quantum cognition
• 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.

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.

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. (2010). Two Stage Dynamic Signal Detection: A Theory of Choice, Decision Time, and Confidence. Psychological Review, 117 (3), 864-901.

Busemeyer, J. R., Pothos, E. & Franco, R., Trueblood, J. S. (2011) A quantum theoretical explanation for probability judgment ‘errors’. Psychological Review, 108, 193-218.

Trueblood, J. S. & Busemeyer, J. R. (2011) A quantum probability explanation for order effects on inference. Cognitive Science, 35, 1518-1552.

Hotaling, J. M., Busemeyer, J. R., & Li, J. (2010). Theoretical developments in Decision Field Theory: A Comment on K. Tsetsos, N. Chater, & M. Usher. Psychological Review, 117, 1294-1298.

Ahn, W.Y., Krawitz, A., Kim, W., Busemeyer, J. R., & Brown, J. W. (2011). A model based f-MRI analysis with hierarchical Bayesian parameter estimation. Journal of Neuroscience, Psychology, and Economics, 4(2), 95-110

Busemeyer, J. R., & Bruza, P. D. (2012). Quantum models of cognition and decision. Cambridge, UK: Cambridge University Press.

Pothos, E. M., & Busemeyer, J. R. (2013). Can quantum probability provide a new direction for cognitive modeling? Behavioral and Brain Sciences, 36, 255-274. (Target Article).

Pothos, E. M., Busemeyer, J. R., & Trueblood, J. S. (2013). A quantum geometric model of similarity. Psychological Review, 120 (3), 679-696.

Dai, J. & Busemeyer, J. R. (2014). Towards a probabilistic, dynamic, and attribute-wise model of intertemporal Choice. Journal of Experimental Psychology: General, 143 (4), 1489-1514.

Busemeyer, J. R., & Rieskamp, J. (2014). Psychological research and theories on preferential choice. In S. Hess & A. Daly (Eds.), Handbook of choice modeling. Edward Elgar Publishers

Kvam, P. D., Pleskac, T. J., Yu, S., & Busemeyer, J. R. (2015) Interference Effects of Choice on Confidence. Proceedings of the National Academy of Science. 112 (34) 10645-10650

Hotaling, J. M. Cohen, A. L., Shiffrin, R. M., & Busemeyer, J. R. (2015) The dilution effect and information integration in perceptual decision making. PLoS One 10(9): e0138481. Doi:10.1371/journal.pone.0138481

Johnson, J. J. & Busemeyer, J. R. (2016) A computational model of the attention process in risky choice. Decision, 3 (4), 254-280.

Khododadi, A., Fakhari, P., & Busemeyer, J. R. (2017) Learning to Allocate Limited Time to Decisions with Different Expected Outcomes. Cognitive Psychology, 95, 17-49

Fakhari, P., Khodadadi, A. & Busemey, J. R. (2018). The detour problem in a stochastic environment: Tolman Revisited. Cognitive Psychology, 101, 29-49.

Busemeyer, J. R., Gluth, S., Rieskamp, J., Turner, B. M. (2019) Cognitive and Neural Bases of Multi-Attribute, Multi-Alternative, Value-based Decisions. Trends in Cognitive Sciences, 23, 3, 251-263.

Kvam, P. D., & Busemeyer, J. R. (2020) A distributional and dynamic theory of pricing. Psychological Review, 127(6), 1053–1078

Pothos, E. M., & Busemeyer, J. R. (2013). Can quantum probability provide a new direction for cognitive modeling? Behavioral and Brain Sciences, 36, 255-274. (Target Article).

Trueblood, J. S., & Busemeyer, J. R. (2012). Quantum information theory. In D. Quinones (Ed.) Encyclopedia of the sciences of learning. Springer. (pp. 2748-2751).

Busemeyer, J. R. (2012). Introduction to quantum probability for social and behavioral scientists. In Rudolph, L. (Ed.), Qualitative mathematics for the social sciences: Mathematical models for research on cultural dynamics. New York, NY: Routledge. (pp. 41-69)

Fakhari, P., Khodadadi, A., & Busemeyer, J. R. (2018). The detour problem in a stochastic environment: Tolman revisited. Cognitive psychology, 101, 29-49.

Busemeyer, J. R., Kvam, P. D., & Pleskac, T. J. (2020) Comparison of Markov versus quantum dynamical models of human decision making. Wiley Interdisciplinary Reviews: Cognitive Science.

Kvam, P. D., & Busemeyer, J. R. (2020) A distributional and dynamic theory of pricing. Psychological Review, 127(6), 1053–1078.

Pothos, E.M., Busemeyer, J. R. (2022) Quantum Cognition. Annual Review of Psychology, 73, 749-778

Yi, S., Lu, M., & Busemeyer, J. (2022). Application of Quantum Cognition to Judgments for Medical Decisions. Quantum Reports, 4(2).

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