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Short Vita for Jerome R. Busemeyer

Distinguished Professor, Indiana University, Bloomington IN, 1997-present

 Psychological and Brain Science and Cognitive Science Program,

Adjunct Professor, Department of Statistics, 2006-present

 

Former Manager, Cognition and Decision Program

 Air Force Office of Scientific Research, 2005-2007

 

Post Doctoral Fellow, Quantitative Methods, (1980) University of Illinois

Ph.D. (1979) University of South Carolina

M.A. (1976) University of South Carolina

B.A. (1973) University of Cincinnati, cum laude

Research Specialization

Judgment and Decision Making

Concept Learning

Mathematical Models

Editorial Boards and Grant Review Panels:

Decision  Editor, 2012-2019

Journal of Mathematical Psychology 1990-present,

(Chief Editor 2005 to 2010)

Associate Editor Psychological Review 2012-2015

Psychological Bulletin 1996-1998

Psychological Review 1999-2015

Psychonomic Bulletin and Review 2002-2005

Journal of Experimental Psychology: Learning, Memory, Cognition 1988-2002

Member NIMH Perception and Cognition Review Committee 1993-1998

Advisory Panel NSF Methodology, Measurement, and Statistics Program 1999-2000.

Professional Affiliations

Member of the Society for Mathematical Psychology

Past Member of the Psychometric Society  

Member of the  Psychonomic Society 

Past Member of the Cognitive Neuroscience Society

Member of Society of Experimental Psychologists

Member of Cognitive Science Society

 National Awards

NSF

Memory & Cognition, $67,810, 1987-1989, PI

Decision, Risk, Mgt Science, $102,000, 1996-1998 , PI

Methodology, Measurement, Statistics,  $2,749,198, 2001-2006, Co-PI

Methodology, Measurement, Statistics, $450,000  2009-2011

Methodology, Measurement, Statistics, $500,000  2016-2019

 

 

NIH

Perception & Cognition, $244,000, 1996-1999, PI

Perception & Cognition, $209,578, 1990-1993, PI

Perception & Cognition, $599,607,  2004-2007, PI

 

NIDA

$880,027, 2001-2005,  Co-PI

$800,000, 2005-2008,  Co-PI

 $462,000, 2011-2104 ,  PI

 

AFOSR

$607,712 2012-2015  PI

$813,095 2015-2018  PI

$1,282,692 2020 – 2023 PI

 

ACADEMIC AWARDS

2019    Honorary Doctorate University of Basel

2017    Fellow Cognitive Science Society

2017    Fellow American Academy of Arts and Science

2015    Warren Medal awarded by Society of Experimental Psychologists

2012    Provost Professor of Indiana University

2006    Fellow of the Society of Experimental Psychologists

 

 

Available Preprints

Selective List of Publications

Busemeyer, J. R. (1980). The importance of measurement theory, error theory, and experimental design for testing the significance of interactions. Psychological Bulletin, 88, 237-244.

Busemeyer, J. R., & Jones, L. E. (1983). The analysis of multiplicative combination rules when the causal variables are measured with error. Psychological Bulletin, 93 (3), 549-562.

Busemeyer, J. R. (1985). Decision making under uncertainty: Simple scalability, fixed sample, and sequential sampling models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11, 538-564.

Busemeyer, J. R., & Rapoport, A. (1988). Psychological models of deferred decision making. Journal of Mathematical Psychology, 32, 1-44.

Busemeyer, J. R., & Myung, I. J. (1988) A new method for investigating prototype learning. Journal of Experimental Psychology: Learning, Memory, Cognition, 14, 3-11.

Townsend, J. T. & Busemeyer, J. R. (1989) Approach-avoidance: Return to dynamic decision behavior. In Chizuko Izawa (Ed.) Current Issues in Cognitive Processes: The Tulane Flowerree Symposium on Cognition. Hillsdale, NJ: Erlbaum.

Myung, I. J., & Busemeyer, J. R. (1992). Measurement free tests of a general state-space model of prototype learning. Journal of Mathematical Psychology, 36, 32-67.

Busemeyer, J. R., & Townsend, J. T. (1992). Fundamental derivations from decision field theory. Mathematical Social Sciences, 23, 255-282.

Busemeyer, J. R., & Myung, I. J. (1992). An adaptive approach to human decision making: Learning theory, decision theory, and human performance. Journal of Experimental Psychology:General, 121, 177-194.

Busemeyer, J. R., Myung, I. J., & McDaniel, M. A. (1993). Cue competition effects: Theoretical implications for adaptive network learning models. Psychological Science, 4, 196-202.

Busemeyer, J. R., & Townsend, J. T. (1993) Decision Field Theory: A dynamic cognition approach to decision making. Psychological Review, 100, 432-459.

Busemeyer, J. R., Hastie, R., & Medin, D. L. (1995). Decision Making from a Cognitive Perspective. Psychology of Learning and Motivation (Vol. 32). Academic Press.

Townsend, J. T., & Busemeyer, J. R. (1995) Dynamic representation of decision making. In R. F. Port and T. van Gelder (Eds.) Mind as Motion. MIT press.

Busemeyer, J. R., McDaniel, M. A., & Byun, E. (1996) The use of intervening variables in causal learning. Psychology of Learning and Motivation, 34, 357-391.

Busemeyer, J. R., McDaniel, M. A., & Byun, E. (1997) The abstraction of intervening concepts from experience with multiple input - multiple output causal environments. Cognitive Psychology, 32, 1-48.

Delosh, E., Busemeyer, J. R., & McDaniel, M. A. (1997) Extrapolation: The sine qua non of abstraction in function learning. Journal of Experimental Psychology: Learning, Memory, Cognition, 23, 968-986.

Busemeyer, J. R., Byun, E., Delosh, E., & McDaniel, M. A. (1997) Function Learning based on experience with input - output pairs by humans and artificial neural networks. In K. Lamberts and D. Shanks (Eds.) Concepts and Categories. Hove, East Sussex, UK: Psychology Press.

Diederich, A. & Busemeyer, J. R. (1999) Conflict and the stochastic dominance principle of decision making. Psychological Science,10, 353-359

Busemeyer, J. R. & Wang, Y. (2000). Model Comparisons and model selections based on the generalization criterion methodology. Journal of Mathematical Psychology, 44, 171-189.

Busemeyer, J. R., Weg, E. Barkan, R., Li, X., & Ma, Z. (2000) Dynamic and consequential consistency of choices between paths of decision trees. Journal of Experimental Psychology: General, 129, 530-545.

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

Johnson, J. G. & Busemeyer, J. R. (2001)  Multiple stage decision making; The effect of planning horizon on dynamic consistency. Theory and Decision, 51, 217-246.

 

Busemeyer, J. R. & Diederich, A. (2002)  Survey of decision field theory. Mathematical Social Sciences, 43, 345-370.

 

Busemeyer, J. R. & Stout, J. C. (2002)  A Contribution of Cognitive Decision Models to Clinical Assessment: Decomposing Performance on the Bechara Gambling Task.  Psychological Assessment, 14, 253-262

   

Busemeyer, J. R., Townsend, J. T., & Stout, J. C. (2002)  Motivational Underpinnings of Utility in Decision Making: Decision Field Theory Analysis of Deprivation and Satiation. In S. Moore (Ed.) Emotional Cognition. Amsterdam: John Benjamins.

 

Barkan, R. & Busemeyer, J. R. (2003) Modeling Dynamic Inconsistency with a Changing Reference Point. Journal of Behavioral Decision Making 16, 235-255

 

Diederich, A., & Busemeyer, J. R . (2003) Simple Matrix Methods for Analyzing Diffusion Models of Choice Probability, Choice Response Time and Simple Response Time. Journal of Mathematical Psychology, 47, 304-322.  (Best Paper Award for 2006).

 

Rieskamp, Jörg, Jerome Busemeyer, and Tei Laine. (2003) How do people learn to allocate resources? Comparing Two Learning Theories. Journal of Experimental Psychology: Learning, Memory and Cognition. 29, 1066-1081.

 

Stout, J. C., Busemeyer, J. R., Lin, A., Grant, S. R., & Bonson, K. R. (2004) Cognitive Modeling Analysis of the Decision-Making Processes Used by Cocaine Abusers.  Psychonomic Bulletin and Review, 11 (4), 742-747.

Busemeyer, J. R. & Johnson, J. G. (2004) Computational models of decision making. In D. Koehler & N. Harvey (Eds.) Handbook of Judgment and Decision Making. Blackwell Publishing Co. Ch. 7, Pp 133-154.

 

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.

 

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.

 

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.

 

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.

 

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.

 

Busemeyer, J. R. & Johnson, J. G. (2008) Micro-process models of decision-making. In R. Sun (Ed.) Cambridge Handbook of Computational Cognitive Modeling. Cambridge University Press.

 

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. (2010) Cognitive Modeling. Sage.

 

Johnson, J. G. & Busemeyer, J. R. (2010) Decision-making under risk and uncertainty. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 736-749.

 

Pleskac, T. J. & Busemeyer, J. R. (2010).Two Stage Dynamic Signal Detection: A Theory of Choice, Decision Time, and Confidence. PDF  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

 

Note: More articles appear in the

Quantum articles link below

Quantum Cognition Link

 

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