Jerome Busemeyer

Home Website
Distinguished Professor and Provost Professor
Psychological And Brain Sciences
Adjunct Professor
Statistics

Field of study

Mathematical and computational models of judgment and decision making

Education

Ph.D., University of South Carolina, 1979
University of Illinois, Post-Doctoral Fellow, Quantitative Methods 1980

Research interests

Dynamic, emotional, and cognitive models of judgment and decision making, quantum models of cognition and decision

Professional Experience

Past President of the Society of Mathematical Psychology
Member of NSF Panel
Member of NIMH panel
Editor Journal Mathematical Psychology 2005-2010
Editor Decision 2012-2019
Associate Editor Psychological Review 2012-2015

Representative publications

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

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

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

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

Markov versus quantum dynamic models of belief change during evidence monitoring (2019)
Busemeyer, J. R., Kvam, P. D., & Pleskac, T. J.
Scientific Reports, 9 18025

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

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

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

Multi-alternative decision field theory: A dynamic artificial neural network model of decision-making (2001)
Roe, R. M., Busemeyer, J. R., & Townsend, J. T.
Psychological Review, 108 370-392

Decision Field Theory: A Dynamic Cognitive Approach to Decision Making (1993)
Busemeyer, J., & Townsend, J. T.
Psychological Review, 100 (3), 432-459

Decision field theory provides for a mathematical foundation leading to a dynamic, stochastic theory of decision behavior in an uncertain environment. This theory is used to explain (1) violations of stochastic dominance,(2) violations of strong stochastic transitivity,(3) violations of independence between alternatives,(4) serial position effects on preference,(5) speed–accuracy trade-off effects in decision making,(6) the inverse relation between choice probability and decision time,(7) changes in the direction of preference under time pressure,(8) slower decision times for avoidance as compared with approach conflicts, and (9) preference reversals between choice and selling price measures of preference. The proposed theory is compared with 4 other theories of decision making under uncertainty.(PsycINFO Database Record (c) 2016 APA, all rights reserved)

Dissertation Committee Service

Dissertation Committee Service
Author	Dissertation Title	Committee
Denton, Stephen	Exploring Active Learning in a Bayesian Framework (September 2009)	Kruschke, J. (Co-Chair), Busemeyer, J. (Co-Chair), Jones, M., Todd, P.
Dimperio, Eric	A Dynamic Model of Planning Behaviors in Multi-Stage Risky Decision Tasks (August 2009)	Busemeyer, J., Goldstone, R., Kruschke, J., Scheutz, M.
Fific, Mario	Emerging holistic properties at face value: assessing Characteristics of face perception (January 2006)	Townsend, J. (Chair), Busemeyer, J., Maki, D., Shiffrin, R.
Frey, Seth	Complex Collective Dynamics in Human Higher-Level Reasoning. A Study Over Multiple Methods (August 2013)	Goldstone, R. (Chair), Todd, P., Beer, R., Busemeyer, J.
Harris, Jack	Automated Cognitive Model Evaluation: Methodologies And Uses (December 2011)	Schuetz, M. (Chair), Bertenthal, B., Busemeyer, J., Leake, D.
Hotaling, Jared	Decision field theory-planning: A cognitive model of planning and dynamic (November 2013)	Busemeyer, J. (Chair), Shiffrin, R., Todd, P., Nosofsky, R.
Houpt, Joseph	Statistical Measures for the Double Factorial Paradigm (April 2012)	Townsend, J. (Co-chair), Busemeyer, J., Kruschke, J. (Co-chair), Huang, C.
Jessup, Ryan	Neural Correlates of the Behavioral Differences Between Descriptive and Experiential Choice: An Examination Combining Computational Modeling with FMRI (September 2008)	Busemeyer, J. (Chair), Brown, J., Sporns, O., Todd, P.
Johnson, J. G.	A Computational Modeling Account of Robust Preference Reversal Phenomena (December 2004)	Busemeyer, J. (Co-Chair), Townsend, J. (Co-Chair), Sherman, S. J., Winston, W. L.
Laine, Tei	Agent-Based Model Selection Framework For Complex Adaptive Systems (August 2006)	Menczer, F. (Chair), Gasser, M., Busemeyer, J., Janssen, M.
Trueblood, Jennifer	An Investigation of Context Effects in Multi-Alternative Choice Behavior Through Experimentation and Cognitive Modeling (June 2012)	Busemeyer, J. (Chair), Kruschke, J., Shiffrin, R., Townsend, J.
Yurovsky, Daniel	Mechanisms of Statistical Word Learning (September 2012)	Yu, C. (Co-Chair), Smith, L. (Co-Chair), Shiffrin, R., Jones, S., Busemeyer, J.,

Edit your profile