Representative publications
Submitted papers
Publications by topic
Full publication list

Representative publications

  • Lee, M.D., & Wagenmakers, E.-J. (2013). Bayesian cognitive modeling: A practical course. Cambridge University Press.  [Book Website] [Google Books] [Amazon US] [Amazon UK] [Cambridge University Press].
  • Lee, M.D. (2018). Bayesian methods in cognitive modeling. In J. Wixted & E.-J. Wagenmakers (Eds.) The Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, Volume 5: Methodology (Fourth Edition). John Wiley & Sons. [pdf] [osf]
  • Lee, M.D., & Vanpaemel, W. (2018). Determining informative priors for cognitive models. Psychonomic Bulletin & Review, 25, 114-127. [pdf]
  • Villarreal, M., Etz, A., & Lee, M.D. (2023). Evaluating the complexity and falsifiability of psychological models. Psychological Review, 130, 853-872. [pdf]
  • Lee, M.D., & Sarnecka, B.W. (2011). Number knower-levels in young children: Insights from a Bayesian model. Cognition, 120, 391-402. [doi] [supplementary note]
  • Lee, M.D. (2015). Evidence for and against a simple interpretation of the less-is-more effect. Judgment and Decision Making, 10, 18-33. [pdf] [data and code] [link]
  • Lee, M.D., Gluck, K.A., & Walsh, M.M. (2019). Understanding the complexity of simple decisions: Modeling multiple behaviors and switching strategies. Decision, 6, 335-368. [pdf] [osf]
  • Lee, M.D., Bock, J.R., Cushman, I., & Shankle, W.R. (2020). An application of multinomial processing tree models and Bayesian methods to understanding memory impairment. Journal of Mathematical Psychology, 95, 102328. [pdf]
  • Westfall, H.A., & Lee, M.D. (2021). A model-based analysis of the impairment of semantic memory. Psychonomic Bulletin & Review, 28, 1484-1494. [pdf]
  • Lee, M.D. (2024). Using cognitive models to improve the wisdom of the crowd. Current Directions in Psychological Science. Accepted 5-Jun-2024. [pdf]

Submitted Papers

Bayes

Cognitive modeling

  • Lee, M.D., & Wagenmakers, E.-J. (2013). Bayesian cognitive modeling: A practical course. Cambridge University Press.  [Book Website] [Google Books] [Amazon US] [Amazon UK] [Cambridge University Press]. You can download drafts of the first two parts of the book, the associated code, and some draft answers.
  • Lee, M.D. (2018). Bayesian methods in cognitive modeling. In J. Wixted & E.-J. Wagenmakers (Eds.), The Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, Volume 5: Methodology (Fourth Edition). John Wiley & Sons. [pdf] [osf]
  • Lee, M.D., & Vanpaemel, W. (2018). Determining informative priors for cognitive models. Psychonomic Bulletin & Review, 25, 114-127. [pdf]
  • Banavar, N.V., Lee, M.D., & Bornstein, A.M. (2021). Sequential effects in non-sequential tasks. In T. Stewart (Ed.), Proceedings of the 19th International Conference on Cognitive Modeling. [pdf]
  • Villarreal, M., Velázquez, C. A., Baroja, J. L., Segura, A., Bouzas, A., & Lee, M.D. (2019). Bayesian methods applied to the generalized matching law. Journal of the Experimental Analysis of Behavior, 111, 252-273. [pdf] [osf]
  • Steingroever, H., Jepma, M., Lee, M.D., Jansen, B.R.J., & Huizenga, H.M. (2019). Modeling decision strategies in the developmental sciences. Computational Brain & Behavior, 2, 128-140. [osf] [link]
  • Mistry, P., & Lee, M.D. (2019). Violence in the intifada: A demonstration of Bayesian generative cognitive modeling. Advances in Econometrics, 40, 65-90. [pdf] [osf]
  • Lee, M.D. (2018). Bayesian methods for analyzing true-and-error models. Judgment and Decision Making, 13, 622-635. [pdf] [osf]
  • Steingroever, H., Pachur, T., Smira, M., & Lee, M.D. (2018). Bayesian techniques for analyzing group differences in the Iowa Gambling Task: A case study of intuitive and deliberate decision makers. Psychonomic Bulletin & Review, 25, 951–970. [pdf] [supplement]
  • Danileiko, I., & Lee, M.D. (2016). Inferring individual differences between and within exemplar and decision-bound models of categorization. In J. Trueswell, A. Papafragou, D. Grodner, & D. Mirman (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society, pp. 2825-2830. Austin, TX: Cognitive Science Society. [pdf] [osf]
  • Danileiko, I., Lee, M.D., & Kalish, M.L. (2015). A Bayesian latent mixture approach to modeling individual differences in categorization using General Recognition Theory. In D.C. Noelle & R. Dale (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society, pp. 501-506. Austin, TX: Cognitive Science Society. [pdf] [supplement]
  • Bartlema, A., Lee, M.D., Wetzels, R., & Vanpaemel, W. (2014). A Bayesian hierarchical mixture approach to individual differences: Case studies in selective attention and representation in category learning. Journal of Mathematical Psychology, 59, 132-150. [pdf] [code]
  • van Ravenzwaaij, D., Moore, C.P., Lee, M.D., & Newell, B.R. (2014). A hierarchical Bayesian modeling approach to searching and stopping in multi-attribute judgment. Cognitive Science, 38, 1384–1405. [pdf]
  • Asher, D., Zhang, S., Zaldivar, A., Lee, M.D., & Krichmar, J. (2012). Modeling individual differences in socioeconomic game playing. In N. Miyake, D. Peebles, & R. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society, pp. 90-95. Austin, TX: Cognitive Science Society. [pdf]
  • Vanpaemel, W., & Lee, M.D. (2012). Using priors to formalize theory: Optimal attention and the Generalized Context Model. Psychonomic Bulletin & Review, 19, 1047-1056. [pdf]
  • Vanpaemel, W., & Lee, M.D. (2012). The Bayesian evaluation of categorization models: Comment on Wills and Pothos (2012). Psychological Bulletin, 138, 1253-1258. [pdf]
  • Lee, M.D. (2011).  How cognitive modeling can benefit from hierarchical Bayesian models. Journal of Mathematical Psychology, 55, 1-7. [pdf]
  • Lee, M.D., & Newell, B.R. (2011). Using hierarchical Bayesian methods to examine the tools of decision making. Judgment and Decision Making, 6, 832-842. [pdf] [code]
  • Zeigenfuse, M.D., & Lee, M.D. (2010). A general latent-assignment approach for modeling psychological contaminants. Journal of Mathematical Psychology, 54, 352-362. [pdf]
  • Lee, M.D., & Wetzels, R. (2010). Individual differences in attention during category learning. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 387-392. Austin, TX: Cognitive Science Society. [pdf]
  • Zeigenfuse, M.D., & Lee, M.D. (2009). Bayesian nonparametric modeling of individual differences: A case study using decision making on bandit problems.  In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 1412-1415. Austin, TX: Cognitive Science Society. [pdf]
  • Lee, M.D., & Vanpaemel, W. (2008). Exemplars, prototypes, similarities and rules in category representation: An example of hierarchical Bayesian analysis. Cognitive Science, 32, 1403-1424. [pdf]
  • Lee, M.D. (2008). Three case studies in the Bayesian analysis of cognitive models. Psychonomic Bulletin & Review, 15, 1-15. [pdf]
  • Shiffrin, R.M., Lee, M.D., Wagenmakers, E.-J., & Kim, W.J. (2008). A survey of model evaluation approaches with a focus on hierarchical Bayesian methods. Cognitive Science, 32, 1248-1284. [pdf]
  • Navarro, D.J., Griffiths, T.L., Steyvers, M., & Lee, M.D. (2006). Modeling individual differences with Dirichlet processes. Journal of Mathematical Psychology, 50, 101-102. [pdf]
  • Lee, M.D., & Webb, M.R. (2005). Modeling individual differences in cognition. Psychonomic Bulletin & Review, 12, 605-621. [pdf]
  • Navarro, D.J., Griffiths, T.L., Steyvers, M., & Lee, M.D. (2005). Modeling individual differences with Dirichlet processes In B.G. Bara, L.W. Barsalou & M. Bucciarelli, (Eds.),  Proceedings of the 27th Annual Conference of the Cognitive Science Society, pp. 1594-1599. Mahwah, NJ: Erlbaum. [pdf]

Statistics

  • Villarreal, M., Etz, A., & Lee, M.D. (2023). Evaluating the complexity and falsifiability of psychological models. Psychological Review, 130, 853-872. [pdf]
  • Lee, M.D., & Wagenmakers, E.-J. (2005). Bayesian statistical inference in psychology: Comment on Trafimow (2003). Psychological Review, 112, 662-668. [pdf]
  • Morey, R.D., Hoekstra, R., Rouder, J.N., Lee, M.D.., & Wagenmakers, E.-J. (2016). The fallacy of placing confidence in confidence intervals. Psychonomic Bulletin & Review, 23, 103-123. [pdf]
  • Heck, D., Boehm, U., Böing-Messing, F., Bürkner, P., Derks, K., Dienes, Z., … Hoijtink, H. (2022). A review of applications of the Bayes factor in psychological research. Psychological Methods. Accepted 27-Sep-2021. [pdf] [osf]
  • van Doorn, J., Westfall, H.A., & Lee, M.D. (2021). Using the weighted Kendall’s distance to analyze rank data in psychology. The Quantitative Methods for Psychology, 17, 154-165. [pdf] [osf]
  • Aczel, B., Hoekstra, R., Gelman, A., Wagenmakers, E.-J., Kluglist, I. G., Rouder, J. N., Vandekerckhove, J., Lee, M.D., Morey, R.D., Vanpaemel, W., Dienes, Z., & van Ravenzwaaij, D. (2020). Discussion points for Bayesian inference. Nature Human Behavior. https://doi.org/10.1038/s41562-019-0807-z. [osf] [sharedIt]
  • Wagenmakers, E.-J., Lee, M.D., Rouder, J.N., & Morey, R.D. (2020). The principle of predictive irrelevance, or why intervals should not be used for model comparison featuring a point null hypothesis. In C. Gruber (Ed.), The Theory of Statistics in Psychology — Applications, Use and Misunderstandings, pp. 111-119. New York: Springer. [osf]
  • Matzke, D., Ly, A., Selker, R., Weeda, W.D., Scheibehenne, B., Lee, M.D., & Wagenmakers, E.-J. (2017). Bayesian inference for correlations in the presence of measurement error and estimation uncertainty. Collabra: Psychology, 3, 25. [link]
  • Wagenmakers, E.-J., Morey, R.D., & Lee, M.D. (2016). Bayesian benefits for the pragmatic researcher. Current Directions in Psychological Science, 25, 169-176. [pdf] [osf]
  • Wagenmakers, E.-J., Verhagen, A.J., Ly, A., Bakker, M., Lee, M.D., Matzke, D., Rouder, J.N., & Morey, R.D. (2015). A power fallacy. Behavior Research Methods, 47, 913-917 [pdf]
  • Wetzels, R., Matzke, D., Lee, M.D., Rouder, J.N., Iverson, G.J., & Wagenmakers, E.-J. (2011). Statistical evidence in experimental psychology: An empirical comparison using 855 t-tests. Perspectives in Psychological Science, 6, 291-298. [pdf]
  • Iverson, G.J, Lee, M.D., & Wagenmakers, E.-J. (2010). The random-effects prep continues to mispredict the probability of replication. Psychonomic Bulletin & Review, 17, 270-272. [pdf] Accompanying technical note [pdf]
  • Iverson, G.J., Wagenmakers, E.-J., & Lee, M. D. (2010). A model averaging approach to replication: The case of prep. Psychological Methods, 15, 172-181. [pdf]
  • Iverson, G.J., Lee, M.D., Zhang, S., & Wagenmakers, E.-J. (2009). prep: An agony in five fits. Journal of Mathematical Psychology, 53, 195-202. [pdf]
  • Iverson, G.J., Lee, M.D., & Wagenmakers, E.-J. (2009). prep misestimates the probability of replication. Psychonomic Bulletin & Review, 16, 424-429. [pdf]
  • Wagenmakers, E.-J., Lee, M.D., Lodewyckx, T., & Iverson, G. (2008). Bayesian versus frequentist inference. In H. Hoijtink, I. Klugkist, and P. Boelen (Eds.), Practical Evaluation of Informative Hypotheses, pp. 181-207. Springer: New York. [pdf]
  • Lee, M.D., & Pope, K.J. (2006). Model selection for the rate problem: A comparison of significance testing, Bayesian, and minimum description length statistical inference.  Journal of Mathematical Psychology, 50, 193-202. [pdf]

Methods and Software

  • Villarreal, M., Stark, C.E.L., & Lee, M.D. (2022). Adaptive design optimization for a Mnemonic Similarity Task. Journal of Mathematical Psychology, 108, 102665. [pdf] [git]
  • Coon, J., & Lee, M.D. (2022). A Bayesian method for measuring risk propensity in the Balloon Analogue Risk Task. Behavior Research Method, 54, 1010-1026. [pdf] [sharedIt] [osf]
  • Lee, M.D. (2019). A simple and flexible Bayesian method for inferring step changes in cognition. Behavior Research Methods, 51, 948-960. [pdf] [osf]
  • Lee, M.D. (2016). Bayesian outcome-based strategy classification. Behavior Research Methods, 48, 29-41. [pdf] [osf]
  • Lodewyckx, T., Kim, W.-J., Lee, M.D., Tuerlinckx, F., Kuppens, P., & Wagenmakers, E.-J. (2011). A tutorial on Bayes Factor estimation with the product space method. Journal of Mathematical Psychology, 55, 331-347. [pdf]
  • Zhang, S., & Lee, M.D. (2010). Optimal experimental design for a class of bandit problems. Journal of Mathematical Psychology, 54, 499-508. [pdf]
  • Wetzels, R., Lee, M.D., & Wagenmakers, E.-J. (2010). Bayesian inference using WBDev: A tutorial for social scientists. Behavior Research Methods, 42, 884-897. [pdf]
  • Welsh, M.B., Lee, M.D., & Begg, S.H. (2009). Repeated judgments in elicitation tasks: Efficacy of the MOLE method. In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 1529-1534 Austin, TX: Cognitive Science Society. [pdf]
  • Lee, M.D. (2008). BayesSDT: Software for Bayesian inference with signal detection theory. Behavior Research Methods, 40, 450-456. [pdf]
  • Welsh, M.B., Lee, M.D., & Begg, S.H. (2008). More-Or-Less Elicitation (MOLE): Testing a heuristic elicitation model. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 493-498. Austin, TX: Cognitive Science Society. [pdf]

Decision making

Strategies and heuristics

  • Lee, M.D., Gluck, K.A., & Walsh, M.M. (2019). Understanding the complexity of simple decisions: Modeling multiple behaviors and switching strategies. Decision, 6, 335-368. [pdf] [osf]
  • Lee, M.D., & Gluck, K.A. (2021). Modeling strategy switches in multi-attribute decision making. Computational Brain & Behavior, 4, 148-163. [pdf] [sharedIt] [git]
  • Lee, M.D., Doering, S., & Carr. A. (2019). A model for understanding recognition validity. Computational Brain & Behavior, 2, 49-63. [pdf] [osf] [link]
  • Mistry, P.K., Lee, M.D., & Newell, B.R. (2016). An empirical evaluation of models for how people learn cue search orders. In J. Trueswell, A. Papafragou, D. Grodner, & D. Mirman (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society, pp. 211-216. Austin, TX: Cognitive Science Society. [pdf] [osf]
  • Lee, M.D., Blanco, G., & Bo, N. (2016). Testing take-the-best in new and changing environments. Behavior Research Methods, 49, 1420-1431. [pdf] [osf]
  • Lee, M.D. (2015). Evidence for and against a simple interpretation of the less-is-more effect. Judgment and Decision Making, 10, 18-33. [pdf] [data and code] [link]
  • van Ravenzwaaij, D., Newell, B.R., Moore, C.P., & Lee, M.D. (2013). Using recognition in multi-attribute decision environments. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society, pp. 3627-3632. Austin, TX: Cognitive Science Society. [pdf]
  • Lee, M.D., & Zhang, S. (2012). Evaluating the process coherence of take-the-best in structured environments. Judgment and Decision Making, 7, 360-372. [link]
  • Newell, B.R., & Lee, M.D. (2011).  The right tool for the job? Comparing an evidence accumulation and a naive strategy selection model of decision making. Journal of Behavioral Decision Making, 24, 456-481. [pdf]
  • Pachur, T., Raaijmakers, J. G. W., Davelaar, E. J., Daw, N. D., Dougherty, M. R., Hommel, B., Lee, M. D., Polyn, S. M., Ridderinkhof, K. R., Todd, P. M., & Wolfe, J. M. (2012). Unpacking cognitive search: Mechanisms and processes. In: P. M. Todd, T. T. Hills, & T. W. Robbins (eds.), Cognitive search: Evolution, algorithms, and the brain. Strüngmann Forum Reports, Vol. 9. Cambridge, MA: MIT Press. [pdf]
  • Lee, M.D., & Cummins, T.D.R. (2004). Evidence accumulation in decision making: Unifying the ‘take the best’ and ‘rational’ models. Psychonomic Bulletin & Review, 11, 343-352. [pdf] [data]
  • Lee, M.D., Loughlin, N., & Lundberg, I.B. (2002). Applying one reason decision making: The prioritization of literature searches. Australian Journal of Psychology, 54, 137-143. [pdf] Reprinted in G. Gigerenzer, R. Hertwig, and T. Pachur (Eds.),  Heuristics: The Foundations of Adaptive Behavior. Oxford University Press.)
  • Lee, M.D., Chandrasena, L.H., & Navarro, D.J. (2002). Using cognitive decision models to prioritize e-mails. In W.G. Gray & C. D. Schunn, (Eds.), Proceedings of the 24th Annual Conference of the Cognitive Science Society, pp. 478-483. Mahwah, NJ: Erlbaum. [pdf]

Sequential decision tasks

  • Lee, M.D., & Chong, S. (in press). Strategies people use buying airline tickets: A cognitive modeling analysis of optimal stopping in a changing environment. Experimental Economics. Accepted 27-May-204. [pdf]
  • Lee, M.D., & Liu, S. (2022). Drafting strategies in fantasy football: A study of competitive sequential human decision making. Judgment and Decision Making, 17, 691-719. [pdf]
  • Lee, M.D., & Courey, K.A. (2021). Modeling optimal stopping in changing environments: A case study in mate selection. Computational Brain & Behavior, 4, 1-17. [pdf] [link] [sharedIt] [git]
  • Guan, H., Stokes, R., Vandekerckhove, J., & Lee, M. D. (2020). A cognitive modeling analysis of risk in sequential choice tasks. Judgment and Decision Making, 15, 823-850. [pdf] [link] [osf]
  • Okada, K., Vandekerckhove, J. & Lee, M.D. (2018). Modeling when people quit: Bayesian censored geometric models with hierarchical and latent-mixture extensions. Behavior Research Methods, 50, 406-415. [pdf] [osf]
  • Guan, H., & Lee, M.D. (2018). The effect of goals and environments on human performance in optimal stopping problems. Decision, 5, 339-361. [pdf]
  • Guan,  H,. Lee, M.D., & Vandekerckhove, J. (2015). A hierarchical cognitive threshold model of human decision making on different length optimal stopping problems. In D.C. Noelle & R. Dale (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society, pp. 824-829. Austin, TX: Cognitive Science Society. [pdf] [supplement]
  • Guan, H., Lee, M.D., & Silva, A. (2014). Threshold models of human decision making on optimal stopping problems in different environments. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society, pp. 553-558. Austin, TX: Cognitive Science Society. [pdf] [data]
  • Lee, M.D., Zhang, S., Munro, M.N., & Steyvers, M. (2011). Psychological models of human and optimal performance on bandit problems. Cognitive Systems Research, 12, 164-174. [pdf] [data]
  • Steyvers, M., Lee, M.D., & Wagenmakers, E.-J. (2009). A Bayesian analysis of human decision- making on bandit problems. Journal of Mathematical Psychology, 53, 168-179. [pdf]
  • Zhang, S., Lee, M.D., & Munro. M.N. (2009). Human and optimal exploration and exploitation in bandit problems. In A. Howes, D. Peebles, & R. Cooper (Eds.), 9th International Conference on Cognitive Modeling – ICCM2009, Manchester, UK. [pdf]
  • Lee, M.D., Zhang, S., Munro. M.N., & Steyvers, M. (2009). Using heuristic models to understand human and optimal decision making on bandit problems. In A. Howes, D. Peebles, R. Cooper (Eds.), 9th International Conference on Cognitive Modeling – ICCM2009, Manchester, UK. [pdf]
  • Yi, S.K.M., Steyvers, M., & Lee, M.D. (2009). Modeling human performance in restless bandits using particle filters. Journal of Problem Solving, 2, 33-53. [pdf]
  • Lee, M.D. (2006). A hierarchical Bayesian model of human decision making on an optimal stopping problem. Cognitive Science, 30, 555-580.  [pdf]
  • Campbell, J., & Lee, M.D. (2006). The effect of feedback and financial reward on human performance solving ‘secretary’ problems.  In R. Sun (Ed.), Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp. 1068-1073. Mahwah, NJ: Erlbaum. [pdf]
  • Lee, M.D., O’Connor, T.A., & Welsh, M.B. (2004). Decision making on the full-information secretary problem. In K. Forbus, D. Gentner & T. Regier, (Eds.), Proceedings of the 26th Annual Conference of the Cognitive Science Society, pp. 819-824. Mahwah, NJ: Erlbaum. [pdf]

Sequential sampling models

  • Lee, M.D., & Corlett, E.Y. (2003). Sequential sampling models of human text classification. Cognitive Science, 27, 159-193. [pdf]
  • Lee, M.D., & Dry, M.J. (2006). Decision making and confidence given uncertain advice. Cognitive Science. 30, 1081-1095. [pdf]
  • Villarreal, M., Chávez De la Peña, A.F., Mistry, P.K., Menon, V., Vandekerckhove, J., & Lee, M.D. (in press). Bayesian graphical modeling with the circular drift diffusion model. Computational Brain & Behavior. Accepted 3-Nov-2023. [pdf]
  • Zhang, S., Lee. M.D., Vandekerckhove, J., Maris, G., and Wagenmakers, E.-J. (2014). Time-varying boundaries for diffusion models of decision making and response time. Frontiers in Psychology, Quantitative Psychology and Measurement, 5, 1-11. [pdf] [link]
  • Lee, M.D., Newell, B.R., & Vandekerckhove, J. (2014). Modeling the adaptation of search termination in human decision making. Decision, 1, 223-251. [pdf]
  • Vandekerckhove, J., Tuerlinckx, F., & Lee, M.D. (2011). Hierarchical diffusion models for two-choice response time. Psychological Methods, 16, 44-62. [pdf]
  • Newell, B.R., & Lee, M.D. (2009). Learning to adapt evidence thresholds in decision making. In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 473-478. Austin, TX: Cognitive Science Society. [pdf]
  • Vandekerckhove, J., Tuerlinckx, F., & Lee, M.D. (2008). A Bayesian approach to diffusion process models of decision making. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 1429-1434. Austin, TX: Cognitive Science Society. [pdf]
  • Newell, B.R., Collins, P., & Lee, M.D. (2007). Adjusting the spanner: Testing an evidence accumulation model of decision making. In D. McNamara and G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society, pp. 535-538. Austin, TX: Cognitive Science Society. [pdf]
  • Lee, M.D., Fuss, I.G, & Navarro, D.J. (2006). A Bayesian approach to diffusion models of decision making and response time. In B. Schölkopf, J.C. Platt, & T. Hoffman (Eds.), Advances in Neural Information Processing Systems 19, pp. 809-815. Cambridge, MA: MIT Press. [pdf]
  • Lee, M.D. (2001). Fast text classification using sequential sampling processes. In M. Stumptner, D. Corbett, and M. Brooks (Eds.), AI 2001: Advances in Artificial Intelligence, Springer-Verlag Lecture Notes on Artificial Intelligence, 2256, pp. 309-320. Berlin: Springer-Verlag. [pdf]
  • Vickers, D., & Lee, M.D. (2000). Dynamic models of simple judgments: II. Properties of a Parallel, Adaptive, Generalised Accumulator Network (PAGAN) model for multi-choice tasks. Non-linear Dynamics, Psychology, and Life Sciences, 4, 1-31. [pdf]
  • Vickers, D., & Lee, M.D. (1998). Dynamic models of simple judgments: I. Properties of a self-regulating accumulator module. Non-linear Dynamics, Psychology, and Life Sciences, 2, 169-194. [pdf]

Categorization, context, and generalization

  • Lee, M.D., & Navarro, D.J. (2002). Extending the ALCOVE model of category learning to featural stimulus domains. Psychonomic Bulletin & Review, 9, 43-58. [pdf]
  • Mehlhorn, K., Newell, B.R., Todd, P.M., Lee, M.D., Morgan, K. Braithwaite, V.A., Hausmann, D., Fielder, K., & Gonzalez, C. (2015). Beyond the exploration-exploitation tradeoff: A synthesis of human and animal literatures. Decision, 2, 191-215. [pdf]

 

  • Villarreal, M., & Lee, M. D. (in press). A Coupled Hidden Markov Model framework for measuring the dynamics of categorization. Journal of Mathematical Psychology. Accepted 15-Sep-2024. [pdf] [psyarxiv]
  • Lee, M.D., & Ke, M.Y. (in press). Modeling individual differences in beliefs and opinions using Thurstonian models. In J. Musolino, P. Hemmer, & J. Sommer (Eds.), The Science of Beliefs. Cambridge University Press. [pdf] [osf]
  • Villarreal, M., Vaday, S., & Lee, M.D. (in press). Categorization in environments that change when people learn. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
  • Lee, M.D., & Ke, M.Y. (2022). Framing effects and preference reversals in crowd-sourced ranked opinions. Decision, 9, 153-171. [pdf]
  • Courey, K.A., & Lee, M.D. (2021). A model-based examination of scale effects in student evaluations of teaching. AERA Open, 7, 1-13. [pdf] [osf]
  • Hayes, B.K., Stephens, R.G., Lee, M.D., Dunn, J.C., Kaluve, A., Choi-Christou, J., & Cruz, N. (2022). Always look on the bright side of logic? Testing explanations of intuitive sensitivity to logic in perceptual tasks. Journal of Experimental Psychology: Learning, Memory, and Cognition. [pdf]
  • Navarro, D.J., Dry, M.J., & Lee, M.D. (2012). Sampling assumptions in inductive generalization. Cognitive Science, 36, 187-223. [pdf] [data]
  • Navarro, D.J, Lee, M.D., Dry, M.J, & Schultz, B. (2008). Extending and testing the Bayesian theory of generalization. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 1746-1751. Austin, TX: Cognitive Science Society. [pdf]
  • Vanpaemel, W., & Lee, M.D. (2007). A model of building representations for category learning.  In D. McNamara and G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society, pp. 1605-1610. Austin, TX: Cognitive Science Society. [pdf]
  • Mackie, S.I., Welsh, M.B., & Lee, M.D. (2006). An oil and gas decision-making taxonomy. SPE paper 100699 in Proceedings of the 2006 SPE Asia Pacific Oil and Gas Conference and Exhibition. Adelaide, Australia: SPE. [pdf]
  • Webb, M.R., & Lee, M.D. (2004). Modeling individual differences in category learning. In K. Forbus, D. Gentner & T. Regier, (Eds.), Proceedings of the 26th Annual Conference of the Cognitive Science Society, pp. 1440-1445. Mahwah, NJ: Erlbaum. [pdf]
  • Welsh, M.B., Begg, S.H., Bratvold, R.B., & Lee, M.D. (2004). Problems with the elicitation of uncertainty. SPE paper 90338 in Proceedings of the 80th Annual Technical Conference and Exhibition of the Society of Petroleum Engineers. Richardson, TX: SPE.

Memory and clinical

  • Westfall, H.A., & Lee, M.D. (2021). A model-based analysis of the impairment of semantic memory. Psychonomic Bulletin & Review, 28, 1484-1494. [pdf]
  • Lee, M.D., Bock, J.R., Cushman, I., & Shankle, W.R. (2020). An application of multinomial processing tree models and Bayesian methods to understanding memory impairment. Journal of Mathematical Psychology, 95, 102328. [pdf]
  • Westfall, H. A., & Lee, M.D. (in press). An extension and clinical application of the SIMPLE model to the free recall of repeated and semantically-related items. Computational Brain & Behavior. Accepted 16 Aug 2023. [pdf]
  • Vanderlip, C., Lee, M.D., & Stark, C.E.L. (in press). Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer’s Disease. Alzheimer’s & Dementia. Accepted 10-Jul-2024. [bioRxiv]
  • Brendler, A., Schneider, M., Elbau, I.G., Sun, R., Nantawisarakul, T., Pöhlchen, D., Brückl, T., BeCOME Working Group, Czisch, M., Sämann, P.G., Lee, M.D., & Spoormaker, V.J. (2024). Assessing hypo‑arousal during reward anticipation with pupillometry in patients with major depressive disorder: replication and correlations with anhedonia. Scientific Reports, 13, 344. [pdf] [doi]
  • Chwiesko, C., Janecek, J., Doering, S., Hollearn, M., McMillan, L., Vandekerckhove, J., Lee, M.D., Ratcliff, R., & Yassa, M.A. (in press). Parsing memory and non-memory contributions to age-related declines in mnemonic discrimination performance: A hierarchical Bayesian diffusion decision modeling approach. Learning and Memory.
  • Lee, M.D., & Stark, C.E.L. (in press). Bayesian modeling of the Mnemonic Similarity Task using multinomial processing trees. Behaviormetrika. Accepted 30-Dec-2022. [pdf]
  • Westfall, H.A., & Lee, M.D. (in press). A model of free recall for multiple encounters of semantically-related stimuli with an application to understanding cognitive impairment. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
  • Matsumoto, N., Kobayashi, M., Takano, K., & Lee, M.D. (2022). Autobiographical memory specificity and mnemonic discrimination. Journal of Memory and Language, 127, 104366. [pdf] [osf]
  • Lee, M.D., Mistry, P.K., & Menon, V. (2022). A multinomial processing tree model of the 2-back working memory task. Computational Brain & Behavior. Accepted 7-May-2022. [pdf] [osf]
  • Westfall, H.A., & Lee, M.D. (2021). A model-based analysis of changes in the semantic structure of free recall due to cognitive impairment. In T. Fitch, C. Lamm, H. Leder, & K. Teßmar-Raible (Eds.), Proceedings of the 43rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
  • Bock, J.R., Hara, J., Fortier, D., Lee, M.D., Petersen, R.C., & Shankle, W.R. (2020). Application of digital cognitive biomarkers for Alzheimer’s disease: Toward predicting impending cognitive decline. The Journal of Prevention of Alzheimer’s Disease. [pdf] [sharedIt]
  • Schneider, M., Elbau, I.G., Nantawisarakul, T., Pöhlchen, D., Brückl, T., BeCOME working group, Czisch, M., Saemann P.G., Lee, M.D., Binder, E.B., & Spoormaker V. (2020). Reduced arousal during reward anticipation in unmedicated depressed patients. Brain Sciences, 10, 906. [pdf] [medrxiv]
  • Mistry, P.K., Skewes, J., & Lee, M.D. (2018). An adaptive signal detection model applied to understanding autism spectrum disorder. In C. Kalish, M. Rau, J. Zhu, & T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society, pp. 774-779. Austin, TX: Cognitive Science Society. [pdf]
  • Lee, M.D., Abramyan, M., & Shankle. W.R. (2016). New methods, measures, and models for analyzing memory impairment using triadic comparisons. Behavior Research Methods, 48, 1492-1507. [pdf]
  • Lee, M.D., Lodewyckx, T., & Wagenmakers, E.-J. (2015). Three Bayesian analyses of memory deficits in patients with dissociative identity disorder. In J. R. Raaijmakers, A. Criss, R. Goldstone, R. Nosofsky, & M. Steyvers (Eds.), Cognitive modeling in perception and memory: A festschrift for Richard M. Shiffrin, pp. 189-200. Psychology Press. [pdf]
  • Lee, M.D., Liu, E.C., & Steyvers, M. (2015). The roles of knowledge and memory in generating top-10 lists. In D.C. Noelle & R. Dale (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society, pp. 1267-1272. Austin, TX: Cognitive Science Society. [pdf]
  • Lee, M.D., & Pooley. J.P. (2013). Correcting the SIMPLE model of free recall. Psychological Review, 120, 293-296. [pdf]
  • Shankle, W.R., Hara, J., Mangrola, T., Hendrix, S., Alva, G., & Lee, M.D. (2013). Hierarchical Bayesian cognitive processing models to analyze clinical trial data. Alzheimer’s & Dementia, 9, 422-428. [pdf]
  • Shankle, W.R., Pooley, J.P., Steyvers, M., Hara. J., Mangrola, T., Reisberg, B., & Lee, M.D. (2013). Relating memory to functional capacity in normal aging to dementia using hierarchical Bayesian cognitive processing models. Alzheimer Disease & Associated Disorders, 27, 16-22. [pdf]
  • Ortega, A., Wagenmakers, E.-J., Lee, M.D., Markowitsch, H.J., & Piefke, M. (2012). A Bayesian latent group analysis for detecting poor effort in the assessment of malingering. Archives of Clinical Neuropsychology, 27, 453-465. [pdf]
  • Pooley, J.P., Lee, M.D., & Shankle, W.R. (2011). Modeling multitrial free recall with unknown rehearsal times. In L. Carlson, C. Hölscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, pp. 108-113. Austin, TX: Cognitive Science Society. [pdf]
  • Pooley. J.P., Lee, M.D., & Shankle. W.R. (2011). Understanding Alzheimer’s using memory models and hierarchical Bayesian analysis. Journal of Mathematical Psychology, 55, 47-56. [pdf]
  • Macguire, A.M., Humphreys, M.S., Dennis, S.J., & Lee, M.D. (2010). Global similarity accounts of embedded-category designs: Test of the global matching models. Journal of Memory & Language, 63, 131-148. [pdf]
  • Pooley, J.P., Lee, M.D., & Shankle, W.R. (2010). Modeling change in recognition bias with the progression of Alzheimer’s. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 103-108. Austin, TX: Cognitive Science Society. [pdf]
  • Pooley, J.P., Lee, M.D., & Shankle, W.R. (2009). Recognition memory deficits in Alzheimer’s disease: Modeling clinical groups and individual patients. In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 2849-2954. Austin, TX: Cognitive Science Society. [pdf]
  • Dennis, S.J., Lee, M.D., & Kinnell, A. (2008). Bayesian analysis of recognition memory: The case of the list-length effect. Journal of Memory & Language, 59, 361-376. [pdf] [code]
  • Lee, M.D. (2004). A Bayesian analysis of retention functions. Journal of Mathematical Psychology, 48, 310-321. [pdf]
  • Vickers, D., & Lee, M.D. (1998). Never cross the path of a traveling salesman: The neural network generation of Halstead-Reitan trail making tests. Behavior Research, Methods, Instruments, & Computers, 30, 423-431. [pdf]
  • Vickers, D., & Lee, M.D. (1997). Towards a dynamic connectionist model of memory. Behavioral and Brain Sciences, 20, 40-41. [pdf]
  • Lee, M.D., Vickers, D., & Brown, M. (1997). Neural network and tree search algorithms for the generation of path-following (trail making) tests. Journal of Intelligent Systems, 7, 117-143. [pdf]

Wisdom of the crowd

  • Lee, M.D. (2024). Using cognitive models to improve the wisdom of the crowd. Current Directions in Psychological Science. Accepted 5-Jun-2024. [pdf]
  • Thomas, B., Coon, J., Westfall, H.A., & Lee, M.D. (2021). Model-based wisdom of the crowd for sequential decision-making tasks. Cognitive Science, 45, e13011. [pdf] [osf]
  • Montgomery, L.E., Bradford, N., & Lee, M.D. (in press). The wisdom of the crowd with partial rankings: A Bayesian approach implementing the Thurstone model in JAGS. Behavior Research Methods. Accepted 8-Jul-2024. [pdf]
  • Montgomery, L.E., Baldini, C.M., Vandekerckhove, J., & Lee, M.D. (in press). Where’s Waldo, Ohio? Using cognitive models to improve the aggregation of spatial knowledge. Computational Brain & Behavior. Accepted 18-Feb-2024. [pdf]
  • Montgomery, L.E., & Lee, M.D. (in press). The wisdom of the crowd and framing effects in spatial knowledge. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
  • Montgomery, L.E., & Lee, M.D. (2021). Expert and novice sensitivity to environmental regularities in predicting NFL games. Judgment and Decision Making, 16, 1370-1391. [pdf] [osf]
  • Lee, M.D., Danileiko, I., & Vi, J. (2018). Testing the ability of the surprisingly popular method to predict NFL games. Judgment and Decision Making, 13, 322-333. [pdf] [osf] [link] [corrigendum]
  • Danileiko, I. & Lee, M.D. (2017). A model-based approach to the wisdom of the crowd in category learning. Cognitive Science, 42, 861-883. [pdf] [osf]
  • Lee, M.D., & Lee, M.N. (2017). The relationship between crowd majority and accuracy for binary decisions. Judgment and Decision Making, 12, 328-343. [pdf] [osf] [link]
  • Selker, R., Lee, M.D., & Iyer, R. (2017). Thurstonian cognitive models for aggregating top-n lists. Decision, 4, 87-101. [pdf] [osf]
  • Lee, M.D., Steyvers, M., & Miller, B.J. (2014). A cognitive model for aggregating people’s rankings. PLoS ONE, 9. [pdf] [supplementary material] [data] [link] [git]
  • Lee, M.D., & Danileiko, I. (2014). Using cognitive models to combine probability estimates. Judgment and Decision Making, 9, 259-273.[pdf] [data1] [data2] [code] [link]
  • Lee, M.D., Steyvers, M., de Young, M., & Miller. B.J. (2012). Inferring expertise in knowledge and prediction ranking tasks. Topics in Cognitive Science, 4, 151-163. [pdf]
  • Yi, S.K., Steyvers, M., Lee, M.D, & Dry, M.D. (2012). The wisdom of the crowd in combinatorial problems. Cognitive Science, 36,452-470. [pdf]
  • Lee, M.D., Zhang, S., & Shi, J. (2011). The wisdom of the crowd playing the Price is Right. Memory & Cognition, 39, 914-923. [pdf] [accompanying technical note] [data]
  • Lee, M.D., Steyvers, M., de Young, M., & Miller, B. (2011). A model-based approach to measuring expertise in ranking tasks. In L. Carlson, C. Hölscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, pp. 1304-1309. Austin, TX: Cognitive Science Society. [pdf]
  • Yi, S.K., Steyvers, M., Lee, M.D., & Dry, M.J. (2010). Wisdom of the crowds in minimum spanning tree problems. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 1840-1845. Austin, TX: Cognitive Science Society. [pdf]
  • Zhang, S., & Lee, M.D., (2010). Cognitive models and the wisdom of crowds: A case study using the bandit problem. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 1118-1123. Austin, TX: Cognitive Science Society. [pdf]
  • Lee, M.D., & Shi, J. (2010).  The accuracy of small-group estimation and the wisdom of crowds. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 1124-1129. Austin, TX: Cognitive Science Society. [pdf]
  • Lee, M.D., Grothe, E., & Steyvers, M. (2009). Conjunction and disjunction fallacies in prediction markets.  In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 1639-1644. Austin, TX: Cognitive Science Society. [pdf]
  • Steyvers, M., Lee, M.D., Miller, B., & Hemmer, P. (2009). The wisdom of crowds in the recollection of order information. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 22, pp. 1785-1793. Cambridge: MA:  MIT Press. [pdf]
  • Miller, B., Hemmer, P., Steyvers, M., & Lee, M.D. (2009). The wisdom of crowds in rank ordering problems. In A. Howes, D. Peebles, & R. Cooper (Eds.), 9th International Conference on Cognitive Modeling – ICCM2009, Manchester, UK. [pdf]
  • Lee, M.D., & Paradowski, M.J. (2007). Group performance on an optimal stopping problem. Journal of Problem Solving, 1, 53-73. [pdf] (Accompanying technical note [pdf]).
  • Malhotra, V., Lee, M.D., & Khurana, A.K. (2007). Domain experts influence decision quality: Towards a robust method for their identification. Journal of Petroleum Science and Engineering, 57, 181-194. [pdf]
  • Malhotra, V., Lee, M.D., & Khurana, A.K. (2004). Decisions and uncertainty management: Expertise Matters. SPE paper 88511 in Proceedings of the 2004 SPE Asia Pacific Oil and Gas Conference and Exhibition. Perth, Australia: SPE.

Representation

  • Gronau, Q.F., & Lee, M.D. (2020). Bayesian inference for multidimensional scaling representations with psychologically-interpretable metrics. Computational Brain & Behavior, 3, 322-340. [osf] [sharedIt]
  • Okada, K., & Lee, M.D. (2016). A Bayesian approach to modeling group and individual differences in multidimensional scaling. Journal of Mathematical Psychology, 70, 35-44. [pdf]
  • Zeigenfuse, M.D., & Lee, M.D. (2011). A comparison of three measures of the association between a feature and a concept.  In L. Carlson, C. Hölscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, pp. 243-248. Austin, TX: Cognitive Science Society. [pdf]
  • Lee, M.D., & Sarnecka, B.W. (2011). Number knower-levels in young children: Insights from a Bayesian model. Cognition, 120, 391-402. [doi] [supplementary note]
  • Lee, M.D., & Sarnecka, B.W. (2011). Number knower-levels in young children: Insights from a Bayesian model. Cognition, 120, 391-402. [doi] [supplementary note]
  • Zhang, S, Lee, M.D., Yu, M., & Xin, J. (2011). Modeling category identification using sparse instance representation. In L. Carlson, C. Hölscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, pp. 2574-2579. Austin, TX: Cognitive Science Society. [pdf]
  • Lee, M.D., & Sarnecka, B.W. (2010). A model of knower-level behavior in number-concept development. Cognitive Science, 34, 51-67. [pdf]
  • Zeigenfuse, M.D., & Lee, M.D. (2010). Finding the features that represent stimuli. Acta Psychologica, 133, 283-295. [pdf]
  • Zeigenfuse, M.D., & Lee, M.D. (2010). Heuristics for choosing features to represent stimuli. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 1565-1570. Austin, TX: Cognitive Science Society. [pdf]
  • Sarnecka, B.W., & Lee, M.D. (2009). Levels of number knowledge in early childhood. Journal of Experimental Child Psychology, 103, 325-337. [pdf]
  • Lee, M.D., Pincombe, B.M., & Welsh, M.B. (2005). An empirical evaluation of models of text document similarity. In B.G. Bara, L.W. Barsalou & M. Bucciarelli, (Eds.),  Proceedings of the 27th Annual Conference of the Cognitive Science Society, pp. 1254-1259. Mahwah, NJ: Erlbaum. [pdf] [data]
  • Zeigenfuse, M.D., & Lee, M.D. (2008). Finding feature representations of stimuli: Combining feature generation and similarity judgment tasks. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 1825-1830. Austin, TX: Cognitive Science Society. [pdf]
  • Lee, M.D., & Navarro, D.J. (2005). Minimum description length and psychological clustering models. In P.D. Grünwald, I.J. Myung and M.A. Pitt (Eds.), Advances in Minimum Description Length: Theory and Applications, pp. 355-384. Cambridge, MA: MIT Press. [pdf]
  • Navarro, D.J., & Lee, M.D. (2004). Common and distinctive features in stimulus representation: A modified version of the contrast model. Psychonomic Bulletin & Review, 11, 961–974. [pdf]
  • Navarro, D.J., & Lee, M.D. (2003). Combining dimensions and features in similarity-based representations. In S. Becker, S. Thrun and K. Obermayer (Eds.), Advances in Neural Information Processing Systems 15, pp. 59-66. Cambridge, MA: MIT Press. [pdf]
  • Lee, M.D., & Pope, K.J. (2003). Avoiding the dangers of averaging across subjects when using multidimensional scaling. Journal of Mathematical Psychology, 47, 32-46. [pdf]
  • Navarro, D.J., & Lee, M.D. (2002). Commonalities and distinctions in featural stimulus representations. In W.G. Gray & C. D. Schunn, (Eds.), Proceedings of the 24th Annual Conference of the Cognitive Science Society, pp. 685-690. Mahwah, NJ: Erlbaum.
  • Lee, M.D. (2002). Generating additive clustering models with limited stochastic complexity. Journal of Classification, 19, 69-85. [pdf]
  • Lee, M.D. (2002). A simple method for generating additive clustering models with limited complexity. Machine Learning, 49, 39-58. [pdf]
  • Navarro, D.J., & Lee, M.D. (2001). Clustering using the contrast model. In J.D. Moore & K. Stenning, (Eds.), Proceedings of the 23rd Annual Conference of the Cognitive Science Society, pp. 686-691. Mahwah, NJ: Erlbaum. [pdf]
  • Lee, M.D. (2001). Extending Bayesian concept learning to deal with representational complexity and adaptation. Behavioral and Brain Sciences, 24, 685-686. [pdf]
  • Lee, M.D. (2001). Determining the dimensionality of multidimensional scaling models for cognitive modeling. Journal of Mathematical Psychology, 45, 149-166. [pdf]
  • Lee, M.D. (2001). On the complexity of additive clustering models. Journal of Mathematical Psychology, 45, 131-148. [pdf]
  • Lee, M.D. (1999). An extraction and regularization approach to additive clustering. Journal of Classification, 16, 255-281. [pdf]
  • Lee, M.D. (1998). Neural feature abstraction from judgments of similarity. Neural Computation, 10, 1815-1830. [pdf]
  • Lee, M.D. (1997). The connectionist construction of psychological spaces. Connection Science, 9, 323-351. [pdf]
  • Lee, M.D. (1996). A neural network which [sic] learns psychological internal representations. Proceedings of the 1996 Australian New-Zealand Conference on Intelligent Information Systems, 182-185.

Perception and visualization

  • Butavicius, M.A., Lee, M.D., Pincombe, B.M., Mullen, L.G., Navarro, D.J., Parsons, K.M., & McCormac, A. (2012). An assessment of email and spontaneous dialogue visualizations. International Journal of Human-Computer Studies, 70, 432-439. [pdf]
  • Dry, M.J., Navarro, D.J., Preiss, A.K., & Lee, M.D. (2009). The perceptual organization of point constellations. In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 1151-1156. Austin, TX: Cognitive Science Society.  [pdf]
  • Lee, M.D., & Habibi, A. (2009). A cyclic sequential sampling model of bistable auditory perception. In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 2669-2674. Austin, TX: Cognitive Science Society. [pdf]
  • Fletcher, K.I., Butavicius, M.A., & Lee, M.D. (2008).  Attention to internal features in unfamiliar face matching. British Journal of Psychology, 99, 379-394. [pdf]
  • Rubin, T.N., Lee, M.D., & Chubb, C.F. (2008). Hierarchical Bayesian modeling of individual differences in texture discrimination. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 1404-1409 Austin, TX: Cognitive Science Society. [pdf]
  • Butavicius, M.A., & Lee, M.D. (2007). An empirical evaluation of four data visualization techniques for displaying short news text similarities. International Journal of Human-Computer Studies, 65, 931-944. [pdf] (Reprinted in R. Dale, D. Burnham, & C.J. Stevens (Eds.), Human Communication Science: A Compendium, pp. 125-148. Sydney: ARC Research Network in Communication Science.)
  • Lee, M.D., Vast, R.L., & Butavicius, M.A. (2006). Face matching under time pressure and task demands. In R. Sun (Ed.), Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp. 1675-1680. Mahwah, NJ: Erlbaum. [pdf]
  • Navarro, D.J., Lee, M.D., & Nikkerud, H. (2005). Learned categorical perception for natural faces. In B.G. Bara, L.W. Barsalou & M. Bucciarelli, (Eds.),  Proceedings of the 27th Annual Conference of the Cognitive Science Society, pp. 1600-1605. Mahwah, NJ: Erlbaum. [pdf]
  • Lee, M.D., Butavicius, M.A., & Reilly, R.E. (2003). Visualizations of binary data: A comparative evaluation. International Journal of Human-Computer Studies, 59, 569-602. [pdf]
  • Lee, M.D., Reilly, R.E., & Butavicius, M.A. (2003). An empirical evaluation of Chernoff faces, star glyphs, and spatial visualizations for binary data. In T. Pattison & B. Thomas, (Eds.), Proceeding of the Australian Symposium on Information Visualisation, pp. 1-10. Sydney: Australian Computer Society Inc.
  • Vickers, D., Navarro, D.J., & Lee, M.D. (2000). Towards a transformational approach to perceptual organization. In: R.J. Howlett & L.C. Jain (Eds.), KES 2000: Proceedings of the Fourth International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, Vol. 1, pp. 325-328. Piscataway, NJ: IEEE.
  • Lee, M.D. (1998). Interactive visualisation of similarity structures. In P. Calder and B.H. Thomas (Eds.), Proceedings of OZCHI 98, pp. 292-299. Piscataway, NJ: IEEE.

Problem solving

  • Chronicle, E.P., MacGregor, J.N., Lee, M.D., Ormerod, T.C., & Hughes, P. (2008). Individual differences in optimization problem solving: Reconciling conflicting results. Journal of Problem Solving, 2, 41-49. [pdf]
  • Dry, M.J., Lee, M.D., Vickers, D., & Hughes, P. (2006). Human performance on visually presented traveling salesperson problems with varying numbers of nodes. Journal of Problem Solving, 1, 20-32. [pdf]
  • Burns, N.R., Lee, M.D., & Vickers, D. (2006). Are individual differences in performance on perceptual and cognitive optimization problems determined by general intelligence? Journal of Problem Solving, 1, 5-19. [pdf]
  • Vickers, D., Lee, M.D., Dry, M., Hughes, P., & McMahon, J.A. (2006). The aesthetic appeal of minimal structures: Judging the attractiveness of solutions to Traveling Salesperson problems. Perception & Psychophysics, 68, 32-42. [pdf]
  • Vickers, D., Mayo, T., Heitman, M., Lee, M.D., & Hughes, P. (2004). Intelligence and individual differences in performance on three types of visually presented optimisation problems. Personality and Individual Differences, 36, 1059-1071. [pdf]
  • Vickers, D., Lee, M.D., Dry, M., & Hughes, P. (2003). The roles of the convex hull and number of intersections upon performance on visually presented traveling salesperson problems. Memory & Cognition, 31, 1094-1104. [pdf]
  • Vickers, D., Bovet, P., Lee, M.D., & Hughes, P. (2003). The perception of minimal structures: Performance on open and closed versions of visually presented Euclidean Traveling Salesperson problems. Perception, 32, 871-886. [pdf]
  • Vickers, D., Butavicius, M.A., Lee, M.D., & Medvedev, A. (2001). Human performance on visually presented traveling salesman problems. Psychological Research, 65, 34-45.
  • Lee, M.D., & Vickers, D. (2000). The importance of the convex hull for human performance on the traveling salesman problem: Comment on Macgregor & Ormerod (1996). Perception & Psychophysics, 62, 226-228. [pdf]

Commentaries and other

  • Vandekerckhove, J., White, C.N., Trueblood, J.S., Rouder, J.N., Matzke, D., Etz, A., Leite, F.P., Donkin, C., Devezer, B., Criss, A.H., & Lee, M.D. (2019). Robust diversity in cognitive science. Computational Brain & Behavior, 2, 271-276. [osf]
  • Lee, M.D., Criss, A.H., Devezer, B., Donkin, C., Etz, A., Leite, F.P., Matzke, D., Rouder, J.N., Trueblood, J.S., White, C.N., & Vandekerckhove, J. (2019). Robust modeling in cognitive science. Computational Brain & Behavior, 2, 141-153. [osf]
  • Falmagne, J.-C., & Lee, M.D. (2015). Mathematical psychology. In Wright, J. D. (Ed.), International Encyclopedia of the Social and Behavioral Sciences (second edition), pp. 800-807. Elsevier. [pdf]
  • Lee, M.D., & Vanpaemel, W. (2013). Quantum models of cognition as Orwellian newspeak. Behavioral and Brain Sciences, 36,  295-296. [pdf]
  • Lee, M.D. (2010). Emergent and structured cognition in Bayesian models: Comment on Griffiths et al and McClelland et al. Trends in Cognitive Sciences, 14, 345-346. [pdf]
  • Lee, M.D. (2011). In praise of ecumenical Bayes. Behavioral and Brain Sciences, 34, 206-207. [pdf]
  • Mackay, M., & Lee, M.D., (2005). Choice of models for the analysis and forecasting of hospital beds. Health Care Management Science Journal, 8, 221-230. [pdf]

Books

  1. Lee, M.D., & Wagenmakers, E.-J. (2013). Bayesian cognitive modeling: A practical course. Cambridge University Press.  [Book Website] [Google Books] [Amazon US] [Amazon UK] [Cambridge University Press]. You can download drafts of the first two parts of the book, the associated code, and some draft answers.

Book Chapters

  1. Lee, M.D., & Navarro, D.J. (2005). Minimum description length and psychological clustering models. In P.D. Grünwald, I.J. Myung and M.A. Pitt (Eds.), Advances in Minimum Description Length: Theory and Applications, pp. 355-384. Cambridge, MA: MIT Press. [pdf]
  2. Wagenmakers, E.-J., Lee, M.D., Lodewyckx, T., & Iverson, G. (2008). Bayesian versus frequentist inference. In H. Hoijtink, I. Klugkist, and P. Boelen (Eds.), Practical Evaluation of Informative Hypotheses, pp. 181-207. Springer: New York. [pdf]
  3. Lee, M.D., Loughlin, N., & Lundberg, I. (2011). Applying one-reason decision making: The prioritization of literature searches. In G. Gigerenzer, R. Hertwig, and T. Pachur (Eds.),  Heuristics: The Foundations of Adaptive Behavior. Oxford University Press. (Reprinted from Australian Journal of Psychology, 54, 137-143).  [pdf]
  4. Butavicius, M.A. & Lee, M.D. (2011). An empirical evaluation of four data visualization techniques for displaying short news text similarities. In R. Dale, D. Burnham, & C.J. Stevens (Eds.), Human Communication Science: A Compendium, pp. 125-148. Sydney: ARC Research Network in Communication Science. (Reprinted from International Journal of Human-Computer Studies, 65 (11), 931-944). [pdf]
  5. Pachur, T., Raaijmakers, J. G. W., Davelaar, E. J., Daw, N. D., Dougherty, M. R., Hommel, B., Lee, M. D., Polyn, S. M., Ridderinkhof, K. R., Todd, P. M., & Wolfe, J. M. (2012). Unpacking cognitive search: Mechanisms and processes. In: P. M. Todd, T. T. Hills, & T. W. Robbins (eds.), Cognitive search: Evolution, algorithms, and the brain. Strüngmann Forum Reports, Vol. 9. Cambridge, MA: MIT Press. [pdf]
  6. Falmagne, J.-C., & Lee, M.D. (2015). Mathematical psychology. In Wright, J. D. (Ed.), International Encyclopedia of the Social and Behavioral Sciences (second edition), pp. 800-807. Elsevier. [pdf]
  7. Lee, M.D., Lodewyckx, T., & Wagenmakers, E.-J. (2015). Three Bayesian analyses of memory deficits in patients with dissociative identity disorder. In J. R. Raaijmakers, A. Criss, R. Goldstone, R. Nosofsky, & M. Steyvers (Eds.), Cognitive modeling in perception and memory: A festschrift for Richard M. Shiffrin, pp. 189-200. Psychology Press. [pdf]
  8. Lee, M.D. (2018). Bayesian methods in cognitive modeling. In J. Wixted & E.-J. Wagenmakers (Eds.), The Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, Volume 5: Methodology (Fourth Edition). John Wiley & Sons. [pdf] [osf]
  9. Wagenmakers, E.-J., Lee, M.D., Rouder, J.N., & Morey, R.D. (2020). The principle of predictive Irrelevance, or why intervals should not be used for model comparison featuring a point null hypothesis. In C. Gruber (Ed.), The Theory of Statistics in Psychology — Applications, Use and Misunderstandings, pp. 111-119. New York: Springer. [osf]
  10. Lee, M.D., & Ke, M.Y. (in press). Modeling individual differences in beliefs and opinions using Thurstonian models. In J. Musolino, P. Hemmer, & J. Sommer (Eds.), The Science of Beliefs. Cambridge University Press. [pdf] [osf]

Journal Articles

  1. Lee, M.D. (1997). The connectionist construction of psychological spaces. Connection Science, 9, 323-351. [pdf]
  2. Lee, M.D., Vickers, D., & Brown, M. (1997). Neural network and tree search algorithms for the generation of path-following (trail making) tests. Journal of Intelligent Systems, 7, 117-143. [pdf]
  3. Vickers, D., & Lee, M.D. (1997). Towards a dynamic connectionist model of memory. Behavioral and Brain Sciences, 20, 40-41. [pdf]
  4. Lee, M.D. (1998). Neural feature abstraction from judgments of similarity. Neural Computation, 10, 1815-1830. [pdf]
  5. Vickers, D., & Lee, M.D. (1998). Dynamic models of simple judgments: I. Properties of a self-regulating accumulator module. Non-linear Dynamics, Psychology, and Life Sciences, 2, 169-194. [pdf]
  6. Vickers, D., & Lee, M.D. (1998). Never cross the path of a traveling salesman: The neural network generation of Halstead-Reitan trail making tests. Behavior Research, Methods, Instruments, & Computers, 30, 423-431. [pdf]
  7. Lee, M.D. (1999). An extraction and regularization approach to additive clustering. Journal of Classification, 16, 255-281. [pdf]
  8. Lee, M.D., & Vickers, D. (2000). The importance of the convex hull for human performance on the traveling salesman problem: Comment on Macgregor & Ormerod (1996). Perception & Psychophysics, 62, 226-228. [pdf]
  9. Vickers, D., & Lee, M.D. (2000). Dynamic models of simple judgments: II. Properties of a Parallel, Adaptive, Generalised Accumulator Network (PAGAN) model for multi-choice tasks. Non-linear Dynamics, Psychology, and Life Sciences, 4, 1-31. [pdf]
  10. Lee, M.D. (2001). On the complexity of additive clustering models. Journal of Mathematical Psychology, 45, 131-148. [pdf]
  11. Lee, M.D. (2001). Determining the dimensionality of multidimensional scaling models for cognitive modeling. Journal of Mathematical Psychology, 45, 149-166. [pdf]
  12. Lee, M.D. (2001). Extending Bayesian concept learning to deal with representational complexity and adaptation. Behavioral and Brain Sciences, 24, 685-686. [pdf]
  13. Vickers, D., Butavicius, M.A., Lee, M.D., & Medvedev, A. (2001). Human performance on visually presented traveling salesman problems. Psychological Research, 65, 34-45.
  14. Lee, M.D. (2002). A simple method for generating additive clustering models with limited complexity. Machine Learning, 49, 39-58. [pdf]
  15. Lee, M.D. (2002). Generating additive clustering models with limited stochastic complexity. Journal of Classification, 19, 69-85. [pdf]
  16. Lee, M.D., Loughlin, N., & Lundberg, I.B. (2002). Applying one reason decision making: The prioritization of literature searches. Australian Journal of Psychology, 54, 137-143. [pdf]
  17. Lee, M.D., & Navarro, D.J. (2002). Extending the ALCOVE model of category learning to featural stimulus domains. Psychonomic Bulletin & Review, 9, 43-58. [pdf]
  18. Lee, M.D., & Corlett, E.Y. (2003). Sequential sampling models of human text classification. Cognitive Science, 27, 159-193. [pdf]
  19. Lee, M.D., & Pope, K.J. (2003). Avoiding the dangers of averaging across subjects when using multidimensional scaling. Journal of Mathematical Psychology, 47, 32-46. [pdf]
  20. Lee, M.D., Butavicius, M.A., & Reilly, R.E. (2003). Visualizations of binary data: A comparative evaluation. International Journal of Human-Computer Studies, 59, 569-602. [pdf]
  21. Vickers, D., Bovet, P., Lee, M.D., & Hughes, P. (2003). The perception of minimal structures: Performance on open and closed versions of visually presented Euclidean Traveling Salesperson problems. Perception, 32, 871-886. [pdf]
  22. Vickers, D., Lee, M.D., Dry, M., & Hughes, P. (2003). The roles of the convex hull and number of intersections upon performance on visually presented traveling salesperson problems. Memory & Cognition, 31, 1094-1104. [pdf]
  23. Vickers, D., Mayo, T., Heitman, M., Lee, M.D., & Hughes, P. (2004). Intelligence and individual differences in performance on three types of visually presented optimisation problems. Personality and Individual Differences, 36, 1059-1071. [pdf]
  24. Lee, M.D., & Cummins, T.D.R. (2004). Evidence accumulation in decision making: Unifying the ‘take the best’ and ‘rational’ models. Psychonomic Bulletin & Review, 11, 343-352. [pdf] [data]
  25. Lee, M.D. (2004). A Bayesian analysis of retention functions. Journal of Mathematical Psychology, 48, 310-321. [pdf]
  26. Navarro, D.J., & Lee, M.D. (2004). Common and distinctive features in stimulus representation: A modified version of the contrast model. Psychonomic Bulletin & Review, 11, 961–974. [pdf]
  27. Lee, M.D., & Wagenmakers, E.-J. (2005). Bayesian statistical inference in psychology: Comment on Trafimow (2003). Psychological Review, 112, 662-668. [pdf]
  28. Mackay, M., & Lee, M.D., (2005). Choice of models for the analysis and forecasting of hospital beds. Health Care Management Science Journal, 8, 221-230. [pdf]
  29. Lee, M.D., & Webb, M.R. (2005). Modeling individual differences in cognition. Psychonomic Bulletin & Review, 12, 605-621. [pdf]
  30. Vickers, D., Lee, M.D., Dry, M., Hughes, P., & McMahon, J.A. (2006). The aesthetic appeal of minimal structures: Judging the attractiveness of solutions to Traveling Salesperson problems. Perception & Psychophysics, 68, 32-42. [pdf]
  31. Navarro, D.J., Griffiths, T.L., Steyvers, M., & Lee, M.D. (2006). Modeling individual differences with Dirichlet processes. Journal of Mathematical Psychology, 50, 101-102. [pdf]
  32. Lee, M.D., & Pope, K.J. (2006). Model selection for the rate problem: A comparison of significance testing, Bayesian, and minimum description length statistical inference.  Journal of Mathematical Psychology, 50, 193-202. [pdf]
  33. Lee, M.D. (2006). A hierarchical Bayesian model of human decision making on an optimal stopping problem. Cognitive Science, 30, 555-580.  [pdf]
  34. Burns, N.R., Lee, M.D., & Vickers, D. (2006). Are individual differences in performance on perceptual and cognitive optimization problems determined by general intelligence? Journal of Problem Solving, 1, 5-19. [pdf]
  35. Dry, M.J., Lee, M.D., Vickers, D., & Hughes, P. (2006). Human performance on visually presented traveling salesperson problems with varying numbers of nodes. Journal of Problem Solving, 1, 20-32. [pdf]
  36. Lee, M.D., & Dry, M.J. (2006). Decision making and confidence given uncertain advice. Cognitive Science. 30, 1081-1095. [pdf]
  37. Butavicius, M.A., & Lee, M.D. (2007). An empirical evaluation of four data visualization techniques for displaying short news text similarities. International Journal of Human-Computer Studies, 65, 931-944. [pdf]
  38. Malhotra, V., Lee, M.D., & Khurana, A.K. (2007). Domain experts influence decision quality: Towards a robust method for their identification. Journal of Petroleum Science and Engineering, 57, 181-194. [pdf]
  39. Lee, M.D., & Paradowski, M.J. (2007). Group performance on an optimal stopping problem. Journal of Problem Solving, 1, 53-73. [pdf] (Accompanying technical note [pdf]).
  40. Lee, M.D. (2008). Three case studies in the Bayesian analysis of cognitive models. Psychonomic Bulletin & Review, 15, 1-15. [pdf]
  41. Fletcher, K.I., Butavicius, M.A., & Lee, M.D. (2008).  Attention to internal features in unfamiliar face matching. British Journal of Psychology, 99, 379-394. [pdf]
  42. Lee, M.D. (2008). BayesSDT: Software for Bayesian inference with signal detection theory. Behavior Research Methods, 40, 450-456. [pdf]
  43. Lee, M.D., & Vanpaemel, W. (2008). Exemplars, prototypes, similarities and rules in category representation: An example of hierarchical Bayesian analysis. Cognitive Science, 32, 1403-1424. [pdf]
  44. Chronicle, E.P., MacGregor, J.N., Lee, M.D., Ormerod, T.C., & Hughes, P. (2008). Individual differences in optimization problem solving: Reconciling conflicting results. Journal of Problem Solving, 2, 41-49. [pdf]
  45. Dennis, S.J., Lee, M.D., & Kinnell, A. (2008). Bayesian analysis of recognition memory: The case of the list-length effect. Journal of Memory & Language, 59, 361-376. [pdf] [code]
  46. Shiffrin, R.M., Lee, M.D., Wagenmakers, E.-J., & Kim, W.J. (2008). A survey of model evaluation approaches with a focus on hierarchical Bayesian methods. Cognitive Science, 32, 1248-1284. [pdf]
  47. Iverson, G.J., Lee, M.D., & Wagenmakers, E.-J. (2009). prep misestimates the probability of replication. Psychonomic Bulletin & Review, 16, 424-429. [pdf]
  48. Steyvers, M., Lee, M.D., & Wagenmakers, E.-J. (2009). A Bayesian analysis of human decision making on bandit problems. Journal of Mathematical Psychology, 53, 168-179. [pdf]’
  49. Iverson, G.J., Lee, M.D., Zhang, S., & Wagenmakers, E.-J. (2009). prep: An agony in five fits. Journal of Mathematical Psychology, 53, 195-202. [pdf]
  50. Sarnecka, B.W., & Lee, M.D. (2009). Levels of number knowledge in early childhood. Journal of Experimental Child Psychology, 103, 325-337. [pdf]
  51. Yi, S.K.M., Steyvers, M., & Lee, M.D. (2009). Modeling human performance in restless bandits using particle filters. Journal of Problem Solving, 2, 33-53. [pdf]
  52. Lee, M.D., & Sarnecka, B.W. (2010). A model of knower-level behavior in number-concept development. Cognitive Science, 34, 51-67. [pdf]
  53. Zeigenfuse, M.D., & Lee, M.D. (2010). Finding the features that represent stimuli. Acta Psychologica, 133, 283-295. [pdf]
  54. Iverson, G.J., Wagenmakers, E.-J., & Lee, M. D. (2010). A model averaging approach to replication: The case of prep. Psychological Methods, 15, 172-181. [pdf]
  55. Iverson, G.J, Lee, M.D., & Wagenmakers, E.-J. (2010). The random-effects prep continues to mispredict the probability of replication. Psychonomic Bulletin & Review, 17, 270-272. [pdf] Accompanying technical note [pdf]
  56. Wetzels, R., Lee, M.D., & Wagenmakers, E.-J. (2010). Bayesian inference using WBDev: A tutorial for social scientists. Behavior Research Methods, 42, 884-897. [pdf]
  57. Macguire, A.M., Humphreys, M.S., Dennis, S.J., & Lee, M.D. (2010). Global similarity accounts of embedded-category designs: Test of the global matching models. Journal of Memory & Language, 63, 131-148. [pdf]
  58. Zeigenfuse, M.D., & Lee, M.D. (2010). A general latent-assignment approach for modeling psychological contaminants. Journal of Mathematical Psychology, 54, 352-362. [pdf]
  59. Lee, M.D. (2010). Emergent and structured cognition in Bayesian models: Comment on Griffiths et al and McClelland et al. Trends in Cognitive Sciences, 14, 345-346. [pdf]
  60. Zhang, S., & Lee, M.D. (2010). Optimal experimental design for a class of bandit problems. Journal of Mathematical Psychology, 54, 499-508. [pdf]
  61. Vandekerckhove, J., Tuerlinckx, F., & Lee, M.D. (2011). Hierarchical diffusion models for two-choice response time. Psychological Methods, 16, 44-62. [pdf]
  62. Lee, M.D., Zhang, S., Munro, M.N., & Steyvers, M. (2011). Psychological models of human and optimal performance on bandit problems. Cognitive Systems Research, 12, 164-174. [pdf] [data]
  63. Lee, M.D. (2011).  How cognitive modeling can benefit from hierarchical Bayesian models. Journal of Mathematical Psychology, 55, 1-7. [pdf]
  64. Pooley. J.P., Lee, M.D., & Shankle. W.R. (2011). Understanding Alzheimer’s using memory models and hierarchical Bayesian analysis. Journal of Mathematical Psychology, 55, 47-56. [pdf]
  65. Lee, M.D., Zhang, S., & Shi, J. (2011). The wisdom of the crowd playing the Price is Right. Memory & Cognition, 39, 914-923. [pdf] [accompanying technical note] [data]
  66. Lee, M.D., & Sarnecka, B.W. (2011). Number knower-levels in young children: Insights from a Bayesian model. Cognition, 120, 391-402. [doi] [supplementary note]
  67. Wetzels, R., Matzke, D., Lee, M.D., Rouder, J.N., Iverson, G.J., & Wagenmakers, E.-J. (2011). Statistical evidence in experimental psychology: An empirical comparison using 855 t-tests. Perspectives in Psychological Science, 6, 291-298. [pdf]
  68. Lee, M.D. (2011). In praise of ecumenical Bayes. Behavioral and Brain Sciences, 34, 206-207. [pdf]
  69. Newell, B.R., & Lee, M.D. (2011).  The right tool for the job? Comparing an evidence accumulation and a naive strategy selection model of decision making. Journal of Behavioral Decision Making, 24, 456-481. [pdf]
  70. Lodewyckx, T., Kim, W.-J., Lee, M.D., Tuerlinckx, F., Kuppens, P., & Wagenmakers, E.-J. (2011). A tutorial on Bayes Factor estimation with the product space method. Journal of Mathematical Psychology, 55, 331-347. [pdf]
  71. Navarro, D.J., Dry, M.J., & Lee, M.D. (2012). Sampling assumptions in inductive generalization. Cognitive Science, 36, 187-223. [pdf] [data]
  72. Yi, S.K., Steyvers, M., Lee, M.D, & Dry, M.D. (2012). The wisdom of the crowd in combinatorial problems. Cognitive Science, 36,452-470. [pdf]
  73. Negen, J., Sarnecka, B.W., & Lee, M.D. (2012). An Excel sheet for inferring children’s number-knower-levels from Give-N data. Behavior Research Methods, 44, 57-66. [pdf]
  74. Lee, M.D., & Newell, B.R. (2011). Using hierarchical Bayesian methods to examine the tools of decision making. Judgment and Decision Making, 6, 832-842. [pdf] [code]
  75. Lee, M.D., Steyvers, M., de Young, M., & Miller. B.J. (2012). Inferring expertise in knowledge and prediction ranking tasks. Topics in Cognitive Science, 4, 151-163. [pdf]
  76. Butavicius, M.A., Lee, M.D., Pincombe, B.M., Mullen, L.G., Navarro, D.J., Parsons, K.M., & McCormac, A. (2012). An assessment of email and spontaneous dialogue visualizations. International Journal of Human-Computer Studies, 70, 432-439. [pdf]
  77. Ortega, A., Wagenmakers, E.-J., Lee, M.D., Markowitsch, H.J., & Piefke, M. (2012). A Bayesian latent group analysis for detecting poor effort in the assessment of malingering. Archives of Clinical Neuropsychology, 27, 453-465. [pdf]
  78. Vanpaemel, W., & Lee, M.D. (2012). The Bayesian evaluation of categorization models: Comment on Wills and Pothos (2012). Psychological Bulletin, 138, 1253-1258. [pdf]
  79. Lee, M.D., & Zhang, S. (2012). Evaluating the process coherence of take-the-best in structured environments. Judgment and Decision Making, 7, 360-372. [link]
  80. Vanpaemel, W., & Lee, M.D. (2012). Using priors to formalize theory: Optimal attention and the Generalized Context Model. Psychonomic Bulletin & Review, 19, 1047-1056. [pdf]
  81. Shankle, W.R., Pooley, J.P., Steyvers, M., Hara. J., Mangrola, T., Reisberg, B., & Lee, M.D. (2013). Relating memory to functional capacity in normal aging to dementia using hierarchical Bayesian cognitive processing models. Alzheimer Disease & Associated Disorders, 27, 16-22. [pdf]
  82. Shankle, W.R., Hara, J., Mangrola, T., Hendrix, S., Alva, G., & Lee, M.D. (2013). Hierarchical Bayesian cognitive processing models to analyze clinical trial data. Alzheimer’s & Dementia, 9, 422-428. [pdf]
  83. Lee, M.D., & Pooley. J.P. (2013). Correcting the SIMPLE model of free recall. Psychological Review, 120, 293-296. [pdf]
  84. Lee, M.D., & Vanpaemel, W. (2013). Quantum models of cognition as Orwellian newspeak. Behavioral and Brain Sciences, 36,  295-296. [pdf]
  85. van Ravenzwaaij, D., Moore, C.P., Lee, M.D., & Newell, B.R. (2014). A hierarchical Bayesian modeling approach to searching and stopping in multi-attribute judgment. Cognitive Science, 38, 1384–1405. [pdf]
  86. Bartlema, A., Lee, M.D., Wetzels, R., & Vanpaemel, W. (2014). A Bayesian hierarchical mixture approach to individual differences: Case studies in selective attention and representation in category learning. Journal of Mathematical Psychology, 59, 132-150. [pdf] [code]
  87. Lee, M.D., & Danileiko, I. (2014). Using cognitive models to combine probability estimates. Judgment and Decision Making, 9, 259-273.[pdf] [data1] [data2] [code] [link]
  88. Lee, M.D., Steyvers, M., & Miller, B.J. (2014). A cognitive model for aggregating people’s rankings. PLoS ONE, 9. [pdf] [supplementary material] [data] [link] [git]
  89. Lee, M.D., Newell, B.R., & Vandekerckhove, J. (2014). Modeling the adaptation of search termination in human decision making. Decision, 1, 223-251. [pdf]
  90. Zhang, S., Lee. M.D., Vandekerckhove, J., Maris, G., and Wagenmakers, E.-J. (2014). Time-varying boundaries for diffusion models of decision making and response time. Frontiers in Psychology, Quantitative Psychology and Measurement, 5, 1-11. [pdf] [link]
  91. Wagenmakers, E.-J., Verhagen, A.J., Ly, A., Bakker, M., Lee, M.D., Matzke, D., Rouder, J.N., & Morey, R.D. (2015). A power fallacy. Behavior Research Methods, 47, 913-917 [pdf]
  92. Lee, M.D. (2015). Evidence for and against a simple interpretation of the less-is-more effect. Judgment and Decision Making, 10, 18-33. [pdf] [data and code] [link]
  93. Mehlhorn, K., Newell, B.R., Todd, P.M., Lee, M.D., Morgan, K. Braithwaite, V.A., Hausmann, D., Fielder, K., & Gonzalez, C. (2015). Beyond the exploration-exploitation tradeoff: A synthesis of human and animal literatures. Decision, 2, 191-215. [pdf]
  94. Lee, M.D., Abramyan, M., & Shankle. W.R. (2016). New methods, measures, and models for analyzing memory impairment using triadic comparisons. Behavior Research Methods, 48, 1492-1507. [pdf]
  95. Lee, M.D. (2016). Bayesian outcome-based strategy classification. Behavior Research Methods, 48, 29-41. [pdf] [osf]
  96. Morey, R.D., Hoekstra, R., Rouder, J.N., Lee, M.D.., & Wagenmakers, E.-J. (2016). The fallacy of placing confidence in confidence intervals. Psychonomic Bulletin & Review, 23, 103-123. [pdf]
  97. Okada, K., & Lee, M.D. (2016). A Bayesian approach to modeling group and individual differences in multidimensional scaling. Journal of Mathematical Psychology, 70, 35-44. [pdf]
  98. Wagenmakers, E.-J., Morey, R.D., & Lee, M.D. (2016). Bayesian benefits for the pragmatic researcher. Current Directions in Psychological Science, 25, 169-176. [pdf] [osf]
  99. Lee, M.D., Blanco, G., & Bo, N. (2016). Testing take-the-best in new and changing environments. Behavior Research Methods, 49, 1420-1431. [pdf] [osf]
  100. Selker, R., Lee, M.D., & Iyer, R. (2017). Thurstonian cognitive models for aggregating top-n lists. Decision, 4, 87-101. [pdf] [osf]
  101. Lee, M.D., & Lee, M.N. (2017). The relationship between crowd majority and accuracy for binary decisions. Judgment and Decision Making, 12, 328-343. [pdf] [osf] [link]
  102. Danileiko, I. & Lee, M.D. (2017). A model-based approach to the wisdom of the crowd in category learning. Cognitive Science, 42, 861-883. [pdf] [osf]
  103. Matzke, D., Ly, A., Selker, R., Weeda, W.D., Scheibehenne, B., Lee, M.D., & Wagenmakers, E.-J. (2017). Bayesian inference for correlations in the presence of measurement error and estimation uncertainty. Collabra: Psychology, 3, 25. [link]
  104. Lee, M.D., & Vanpaemel, W. (2018). Determining informative priors for cognitive models. Psychonomic Bulletin & Review, 25, 114-127. [pdf]
  105. Okada, K., Vandekerckhove, J. & Lee, M.D. (2018). Modeling when people quit: Bayesian censored geometric models with hierarchical and latent-mixture extensions. Behavior Research Methods, 50, 406-415. [pdf] [osf]
  106. Guan, H., & Lee, M.D. (2018). The effect of goals and environments on human performance in optimal stopping problems. Decision, 5, 339-361. [pdf]
  107. Steingroever, H., Pachur, T., Smira, M., & Lee, M.D. (2018). Bayesian techniques for analyzing group differences in the Iowa Gambling Task: A case study of intuitive and deliberate decision makers. Psychonomic Bulletin & Review, 25, 951–970. [pdf] [supplement]
  108. Lee, M.D., Danileiko, I., & Vi, J. (2018). Testing the ability of the surprisingly popular method to predict NFL games. Judgment and Decision Making, 13, 322-333. [pdf] [osf] [link] [corrigendum]
  109. Lee, M.D. (2018). Bayesian methods for analyzing true-and-error models. Judgment and Decision Making, 13, 622-635. [pdf] [osf]
  110. Lee, M.D. (2019). A simple and flexible Bayesian method for inferring step changes in cognition. Behavior Research Methods, 51, 948-960. [pdf] [osf]
  111. Lee, M.D., Gluck, K.A., & Walsh, M.M. (2019). Understanding the complexity of simple decisions: Modeling multiple behaviors and switching strategies. Decision, 6, 335-368. [pdf] [osf]
  112. Lee, M.D., Doering, S., & Carr. A. (2019). A model for understanding recognition validity. Computational Brain & Behavior, 2, 49-63. [pdf] [osf] [link]
  113. Steingroever, H., Jepma, M., Lee, M.D., Jansen, B.R.J., & Huizenga, H.M. (2019). Modeling decision strategies in the developmental sciences. Computational Brain & Behavior, 2, 128-140. [osf] [link]
  114. Villarreal, M., Velázquez, C. A., Baroja, J. L., Segura, A., Bouzas, A., & Lee, M.D. (2019). Bayesian methods applied to the generalized matching law. Journal of the Experimental Analysis of Behavior, 111, 252-273. [pdf] [osf]
  115. Mistry, P., & Lee, M.D. (2019). Violence in the intifada: A demonstration of Bayesian generative cognitive modeling. Advances in Econometrics, 40, 65-90. [pdf] [osf]
  116. Lee, M.D., Criss, A.H., Devezer, B., Donkin, C., Etz, A., Leite, F.P., Matzke, D., Rouder, J.N., Trueblood, J.S., White, C.N., & Vandekerckhove, J. (2019). Robust modeling in cognitive science. Computational Brain & Behavior, 2, 141-153. [osf]
  117. Vandekerckhove, J., White, C.N., Trueblood, J.S., Rouder, J.N., Matzke, D., Etz, A., Leite, F.P., Donkin, C., Devezer, B., Criss, A.H., & Lee, M.D. (2019). Robust diversity in cognitive science. Computational Brain & Behavior, 2, 271-276. [osf]
  118. Aczel, B., Hoekstra, R., Gelman, A., Wagenmakers, E.-J., Kluglist, I. G., Rouder, J. N., Vandekerckhove, J., Lee, M.D., Morey, R.D., Vanpaemel, W., Dienes, Z., & van Ravenzwaaij, D. (2020). Discussion points for Bayesian inference. Nature Human Behavior. https://doi.org/10.1038/s41562-019-0807-z. [osf] [sharedIt]
  119. Lee, M.D., Bock, J.R., Cushman, I., & Shankle, W.R. (2020). An application of multinomial processing tree models and Bayesian methods to understanding memory impairment. Journal of Mathematical Psychology, 95, 102328. [pdf]
  120. Gronau, Q.F., & Lee, M.D. (2020). Bayesian inference for multidimensional scaling representations with psychologically-interpretable metrics. Computational Brain & Behavior, 3, 322-340. [osf] [sharedIt]
  121. Guan, H., Stokes, R., Vandekerckhove, J., & Lee, M. D. (2020). A cognitive modeling analysis of risk in sequential choice tasks. Judgment and Decision Making, 15, 823-850. [pdf] [link] [osf]
  122. Bock, J.R., Hara, J., Fortier, D., Lee, M.D., Petersen, R.C., & Shankle, W.R. (2020). Application of digital cognitive biomarkers for Alzheimer’s disease: Toward predicting impending cognitive decline. The Journal of Prevention of Alzheimer’s Disease. [pdf] [sharedIt]
  123. Schneider, M., Elbau, I.G., Nantawisarakul, T., Pöhlchen, D., Brückl, T., BeCOME working group, Czisch, M., Saemann P.G., Lee, M.D., Binder, E.B., & Spoormaker V. (2020). Reduced arousal during reward anticipation in unmedicated depressed patients. Brain Sciences, 10, 906. [pdf] [medrxiv]
  124. Lee, M.D., & Courey, K.A. (2021). Modeling optimal stopping in changing environments: A case study in mate selection. Computational Brain & Behavior, 4, 1-17. [pdf] [link] [sharedIt] [git]
  125. Lee, M.D., & Gluck, K.A. (2021). Modeling strategy switches in multi-attribute decision making. Computational Brain & Behavior, 4, 148-163. [pdf] [sharedIt] [git]
  126. Westfall, H.A., & Lee, M.D. (2021). A model-based analysis of the impairment of semantic memory. Psychonomic Bulletin & Review, 28, 1484-1494. [pdf]
  127. Thomas, B., Coon, J., Westfall, H.A., & Lee, M.D. (2021). Model-based wisdom of the crowd for sequential decision-making tasks. Cognitive Science, 45, e13011. [pdf] [osf]
  128. van Doorn, J., Westfall, H.A., & Lee, M.D. (2021). Using the weighted Kendall’s distance to analyze rank data in psychology. The Quantitative Methods for Psychology, 17, 154-165. [pdf] [osf]
  129. Courey, K.A., & Lee, M.D. (2021). A model-based examination of scale effects in student evaluations of teaching. AERA Open, 7, 1-13. [pdf] [osf]
  130. Montgomery, L.E., & Lee, M.D. (2021). Expert and novice sensitivity to environmental regularities in predicting NFL games. Judgment and Decision Making, 16, 1370-1391. [pdf] [osf]
  131. Heck, D., Boehm, U., Böing-Messing, F., Bürkner, P., Derks, K., Dienes, Z., … Hoijtink, H. (2022). A review of applications of the Bayes factor in psychological research. Psychological Methods. Accepted 27-Sep-2021. [pdf] [osf]
  132. Coon, J., & Lee, M.D. (2022). A Bayesian method for measuring risk propensity in the Balloon Analogue Risk Task. Behavior Research Method, 54, 1010-1026. [pdf] [sharedIt] [osf]
  133. Hayes, B.K., Stephens, R.G., Lee, M.D., Dunn, J.C., Kaluve, A., Choi-Christou, J., & Cruz, N. (2022). Always look on the bright side of logic? Testing explanations of intuitive sensitivity to logic in perceptual tasks. Journal of Experimental Psychology: Learning, Memory, and Cognition. [pdf]
  134. Lee, M.D., & Ke, M.Y. (2022). Framing effects and preference reversals in crowd-sourced ranked opinions. Decision, 9, 153-171. [pdf]
  135. Villarreal, M., Stark, C.E.L., & Lee, M.D. (2022). Adaptive design optimization for a Mnemonic Similarity Task. Journal of Mathematical Psychology, 108, 102665. [pdf] [git]
  136. Lee, M.D., Mistry, P.K., & Menon, V. (2022). A multinomial processing tree model of the 2-back working memory task. Computational Brain & Behavior. Accepted 7-May-2022. [pdf] [osf]
  137. Lee, M.D., & Liu, S. (2022). Drafting strategies in fantasy football: A study of competitive sequential human decision making. Judgment and Decision Making, 17, 691-719. [pdf]
  138. Matsumoto, N., Kobayashi, M., Takano, K., & Lee, M.D. (2022). Autobiographical memory specificity and mnemonic discrimination. Journal of Memory and Language, 127, 104366. [pdf] [osf]
  139. Lee, M.D., & Stark, C.E.L. (2023). Bayesian modeling of the Mnemonic Similarity Task using multinomial processing trees. Behaviormetrika, 50, 517-539. [pdf]
  140. Villarreal, M., Etz, A., & Lee, M.D. (2023). Evaluating the complexity and falsifiability of psychological models. Psychological Review, 130, 853-872. [pdf]
  141. Chwiesko, C., Janecek, J., Doering, S., Hollearn, M., McMillan, L., Vandekerckhove, J., Lee, M.D., Ratcliff, R., & Yassa, M.A. (2023). Parsing memory and non-memory contributions to age-related declines in mnemonic discrimination performance: A hierarchical Bayesian diffusion decision modeling approach. Learning and Memory, 30(11), 296-309.
  142. Westfall, H. A., & Lee, M.D. (2024). An extension and clinical application of the SIMPLE model to the free recall of repeated and semantically-related items. Computational Brain & Behavior, 7, 65–79. [pdf]
  143. Villarreal, M., Chávez De la Peña, A.F., Mistry, P.K., Menon, V., Vandekerckhove, J., & Lee, M.D. (in press). Bayesian graphical modeling with the circular drift diffusion model. Computational Brain & Behavior. Accepted 3-Nov-2023. [pdf]
  144. Brendler, A., Schneider, M., Elbau, I.G., Sun, R., Nantawisarakul, T., Pöhlchen, D., Brückl, T., BeCOME Working Group, Czisch, M., Sämann, P.G., Lee, M.D., & Spoormaker, V.J. (2024). Assessing hypo‑arousal during reward anticipation with pupillometry in patients with major depressive disorder: replication and correlations with anhedonia. Scientific Reports, 13, 344. [pdf] [doi]
  145. Montgomery, L.E., Baldini, C.M., Vandekerckhove, J., & Lee, M.D. (in press). Where’s Waldo, Ohio? Using cognitive models to improve the aggregation of spatial knowledge. Computational Brain & Behavior. Accepted 18-Feb-2024. [pdf]
  146. Lee, M.D., & Chong, S. (in press). Strategies people use buying airline tickets: A cognitive modeling analysis of optimal stopping in a changing environment. Experimental Economics. Accepted 27-May-204. [pdf]
  147. Lee, M.D. (2024). Using cognitive models to improve the wisdom of the crowd. Current Directions in Psychological Science. Accepted 5-Jun-2024. [pdf]
  148. Montgomery, L.E., Bradford, N., & Lee, M.D. (in press). The wisdom of the crowd with partial rankings: A Bayesian approach implementing the Thurstone model in JAGS. Behavior Research Methods. Accepted 8-Jul-2024. [pdf]
  149. Vanderlip, C., Lee, M.D., & Stark, C.E.L. (in press). Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer’s Disease. Alzheimer’s & Dementia. Accepted 10-Jul-2024. [bioRxiv]
  150. Villarreal, M., & Lee, M. D. (in press). A Coupled Hidden Markov Model framework for measuring the dynamics of categorization. Journal of Mathematical Psychology. Accepted 15-Sep-2024. [pdf] [psyarxiv]

Refereed Conference Papers

  1. Lee, M.D. (1996). A neural network which [sic] learns psychological internal representations. Proceedings of the 1996 Australian New-Zealand Conference on Intelligent Information Systems, 182-185.
  2. Lee, M.D. (1998). Interactive visualisation of similarity structures. In P. Calder and B.H. Thomas (Eds.), Proceedings of OZCHI 98, pp. 292-299. Piscataway, NJ: IEEE.
  3. Vickers, D., Navarro, D.J., & Lee, M.D. (2000). Towards a transformational approach to perceptual organization. In: R.J. Howlett & L.C. Jain (Eds.), KES 2000: Proceedings of the Fourth International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, Vol. 1, pp. 325-328. Piscataway, NJ: IEEE.
  4. Lee, M.D. (2001). Fast text classification using sequential sampling processes. In M. Stumptner, D. Corbett, and M. Brooks (Eds.), AI 2001: Advances in Artificial Intelligence, Springer-Verlag Lecture Notes on Artificial Intelligence, 2256, pp. 309-320. Berlin: Springer-Verlag. [pdf]
  5. Navarro, D.J., & Lee, M.D. (2001). Clustering using the contrast model. In J.D. Moore & K. Stenning, (Eds.), Proceedings of the 23rd Annual Conference of the Cognitive Science Society, pp. 686-691. Mahwah, NJ: Erlbaum. [pdf]
  6. Lee, M.D., Chandrasena, L.H., & Navarro, D.J. (2002). Using cognitive decision models to prioritize e-mails. In W.G. Gray & C. D. Schunn, (Eds.), Proceedings of the 24th Annual Conference of the Cognitive Science Society, pp. 478-483. Mahwah, NJ: Erlbaum. [pdf]
  7. Navarro, D.J., & Lee, M.D. (2002). Commonalities and distinctions in featural stimulus representations. In W.G. Gray & C. D. Schunn, (Eds.), Proceedings of the 24th Annual Conference of the Cognitive Science Society, pp. 685-690. Mahwah, NJ: Erlbaum.
  8. Lee, M.D., Reilly, R.E., & Butavicius, M.A. (2003). An empirical evaluation of Chernoff faces, star glyphs, and spatial visualizations for binary data. In T. Pattison & B. Thomas, (Eds.), Proceeding of the Australian Symposium on Information Visualisation, pp. 1-10. Sydney: Australian Computer Society Inc.
  9. Navarro, D.J., & Lee, M.D. (2003). Combining dimensions and features in similarity-based representations. In S. Becker, S. Thrun and K. Obermayer (Eds.), Advances in Neural Information Processing Systems 15, pp. 59-66. Cambridge, MA: MIT Press. [pdf]
  10. Lee, M.D. (2004). An efficient method for the minimum description length evaluation of cognitive models. In K. Forbus, D. Gentner & T. Regier, (Eds.), Proceedings of the 26th Annual Conference of the Cognitive Science Society, pp. 807-812. Mahwah, NJ: Erlbaum. [pdf]
  11. Webb, M.R., & Lee, M.D. (2004). Modeling individual differences in category learning. In K. Forbus, D. Gentner & T. Regier, (Eds.), Proceedings of the 26th Annual Conference of the Cognitive Science Society, pp. 1440-1445. Mahwah, NJ: Erlbaum. [pdf]
  12. Lee, M.D., O’Connor, T.A., & Welsh, M.B. (2004). Decision making on the full-information secretary problem. In K. Forbus, D. Gentner & T. Regier, (Eds.), Proceedings of the 26th Annual Conference of the Cognitive Science Society, pp. 819-824. Mahwah, NJ: Erlbaum. [pdf]
  13. Malhotra, V., Lee, M.D., & Khurana, A.K. (2004). Decisions and uncertainty management: Expertise Matters. SPE paper 88511 in Proceedings of the 2004 SPE Asia Pacific Oil and Gas Conference and Exhibition. Perth, Australia: SPE.
  14. Welsh, M.B., Begg, S.H., Bratvold, R.B., & Lee, M.D. (2004). Problems with the elicitation of uncertainty. SPE paper 90338 in Proceedings of the 80th Annual Technical Conference and Exhibition of the Society of Petroleum Engineers. Richardson, TX: SPE.
  15. Lee, M.D., Pincombe, B.M., & Welsh, M.B. (2005). An empirical evaluation of models of text document similarity. In B.G. Bara, L.W. Barsalou & M. Bucciarelli, (Eds.),  Proceedings of the 27th Annual Conference of the Cognitive Science Society, pp. 1254-1259. Mahwah, NJ: Erlbaum. [pdf] [data]
  16. Navarro, D.J., Griffiths, T.L., Steyvers, M., & Lee, M.D. (2005). Modeling individual differences with Dirichlet processes In B.G. Bara, L.W. Barsalou & M. Bucciarelli, (Eds.),  Proceedings of the 27th Annual Conference of the Cognitive Science Society, pp. 1594-1599. Mahwah, NJ: Erlbaum. [pdf]
  17. Navarro, D.J., Lee, M.D., & Nikkerud, H. (2005). Learned categorical perception for natural faces. In B.G. Bara, L.W. Barsalou & M. Bucciarelli, (Eds.),  Proceedings of the 27th Annual Conference of the Cognitive Science Society, pp. 1600-1605. Mahwah, NJ: Erlbaum. [pdf]
  18. Navarro, D.J., & Lee, M.D. (2005). An application of minimum description length clustering to partitioning learning curves. Proceedings of the 2005 IEEE International Symposium on Information Theory, pp. 587-591. Piscataway, NJ: IEEE. [pdf]
  19. Lee, M.D., Vast, R.L., & Butavicius, M.A. (2006). Face matching under time pressure and task demands. In R. Sun (Ed.), Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp. 1675-1680. Mahwah, NJ: Erlbaum. [pdf]
  20. Campbell, J., & Lee, M.D. (2006). The effect of feedback and financial reward on human performance solving ‘secretary’ problems.  In R. Sun (Ed.), Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp. 1068-1073. Mahwah, NJ: Erlbaum. [pdf]
  21. Mackie, S.I., Welsh, M.B., & Lee, M.D. (2006). An oil and gas decision-making taxonomy. SPE paper 100699 in Proceedings of the 2006 SPE Asia Pacific Oil and Gas Conference and Exhibition. Adelaide, Australia: SPE. [pdf]
  22. Lee, M.D., Fuss, I.G, & Navarro, D.J. (2006). A Bayesian approach to diffusion models of decision making and response time. In B. Schölkopf, J.C. Platt, & T. Hoffman (Eds.), Advances in Neural Information Processing Systems 19, pp. 809-815. Cambridge, MA: MIT Press. [pdf]
  23. Vanpaemel, W., & Lee, M.D. (2007). A model of building representations for category learning.  In D. McNamara and G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society, pp. 1605-1610. Austin, TX: Cognitive Science Society. [pdf]
  24. Newell, B.R., Collins, P., & Lee, M.D. (2007). Adjusting the spanner: Testing an evidence accumulation model of decision making. In D. McNamara and G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society, pp. 535-538. Austin, TX: Cognitive Science Society. [pdf]
  25. Navarro, D.J, Lee, M.D., Dry, M.J, & Schultz, B. (2008). Extending and testing the Bayesian theory of generalization. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 1746-1751. Austin, TX: Cognitive Science Society. [pdf]
  26. Zeigenfuse, M.D., & Lee, M.D. (2008). Finding feature representations of stimuli: Combining feature generation and similarity judgment tasks. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 1825-1830. Austin, TX: Cognitive Science Society. [pdf]
  27. Vandekerckhove, J., Tuerlinckx, F., & Lee, M.D. (2008). A Bayesian approach to diffusion process models of decision making. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 1429-1434. Austin, TX: Cognitive Science Society. [pdf]
  28. Welsh, M.B., Lee, M.D., & Begg, S.H. (2008). More-Or-Less Elicitation (MOLE): Testing a heuristic elicitation model. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 493-498. Austin, TX: Cognitive Science Society. [pdf]
  29. Rubin, T.N., Lee, M.D., & Chubb, C.F. (2008). Hierarchical Bayesian modeling of individual differences in texture discrimination. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 1404-1409 Austin, TX: Cognitive Science Society. [pdf]
  30. Lee, M.D., & Habibi, A. (2009). A cyclic sequential sampling model of bistable auditory perception. In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 2669-2674. Austin, TX: Cognitive Science Society. [pdf]
  31. Pooley, J.P., Lee, M.D., & Shankle, W.R. (2009). Recognition memory deficits in Alzheimer’s disease: Modeling clinical groups and individual patients. In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 2849-2954. Austin, TX: Cognitive Science Society. [pdf]
  32. Zeigenfuse, M.D., & Lee, M.D. (2009). Bayesian nonparametric modeling of individual differences: A case study using decision making on bandit problems.  In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 1412-1415. Austin, TX: Cognitive Science Society. [pdf]
  33. Welsh, M.B., Lee, M.D., & Begg, S.H. (2009). Repeated judgments in elicitation tasks: Efficacy of the MOLE method. In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 1529-1534 Austin, TX: Cognitive Science Society. [pdf]
  34. Newell, B.R., & Lee, M.D. (2009). Learning to adapt evidence thresholds in decision making. In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 473-478. Austin, TX: Cognitive Science Society. [pdf]
  35. Dry, M.J., Navarro, D.J., Preiss, A.K., & Lee, M.D. (2009). The perceptual organization of point constellations. In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 1151-1156. Austin, TX: Cognitive Science Society.  [pdf]
  36. Lee, M.D., Grothe, E., & Steyvers, M. (2009). Conjunction and disjunction fallacies in prediction markets.  In N. Taatgen, H. van Rijn, J. Nerbonne, & L. Shonmaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 1639-1644. Austin, TX: Cognitive Science Society. [pdf]
  37. Lee, M.D., Zhang, S., Munro. M.N., & Steyvers, M. (2009). Using heuristic models to understand human and optimal decision making on bandit problems. In A. Howes, D. Peebles, R. Cooper (Eds.), 9th International Conference on Cognitive Modeling – ICCM2009, Manchester, UK. [pdf]
  38. Miller, B., Hemmer, P., Steyvers, M., & Lee, M.D. (2009). The wisdom of crowds in rank ordering problems. In A. Howes, D. Peebles, & R. Cooper (Eds.), 9th International Conference on Cognitive Modeling – ICCM2009, Manchester, UK. [pdf]
  39. Zhang, S., Lee, M.D., & Munro. M.N. (2009). Human and optimal exploration and exploitation in bandit problems. In A. Howes, D. Peebles, & R. Cooper (Eds.), 9th International Conference on Cognitive Modeling – ICCM2009, Manchester, UK. [pdf]
  40. Steyvers, M., Lee, M.D., Miller, B., & Hemmer, P. (2009). The wisdom of crowds in the recollection of order information. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 22, pp. 1785-1793. Cambridge: MA:  MIT Press. [pdf]
  41. Stephens, R.G., Navarro, D.J., Dunn, J.C., & Lee, M.D. (2009). The effect of causal strength on the use of causal and similarity-based information in feature inference. In ASC09: Proceedings of the 9th Conference of the Australasian Society for Cognitive Science. Edited by Wayne Christensen, Elizabeth Schier, and John Sutton. Sydney: Macquarie Centre for Cognitive Science. [pdf]
  42. Lee, M.D., & Shi, J. (2010).  The accuracy of small-group estimation and the wisdom of crowds. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 1124-1129. Austin, TX: Cognitive Science Society. [pdf]
  43. Lee, M.D., & Wetzels, R. (2010). Individual differences in attention during category learning. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 387-392. Austin, TX: Cognitive Science Society. [pdf]
  44. Zhang, S., & Lee, M.D., (2010). Cognitive models and the wisdom of crowds: A case study using the bandit problem. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 1118-1123. Austin, TX: Cognitive Science Society. [pdf]
  45. Zeigenfuse, M.D., & Lee, M.D. (2010). Heuristics for choosing features to represent stimuli. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 1565-1570. Austin, TX: Cognitive Science Society. [pdf]
  46. Pooley, J.P., Lee, M.D., & Shankle, W.R. (2010). Modeling change in recognition bias with the progression of Alzheimer’s. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 103-108. Austin, TX: Cognitive Science Society. [pdf]
  47. Yi, S.K., Steyvers, M., Lee, M.D., & Dry, M.J. (2010). Wisdom of the crowds in minimum spanning tree problems. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 1840-1845. Austin, TX: Cognitive Science Society. [pdf]
  48. Zhang, S, Lee, M.D., Yu, M., & Xin, J. (2011). Modeling category identification using sparse instance representation. In L. Carlson, C. Hölscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, pp. 2574-2579. Austin, TX: Cognitive Science Society. [pdf]
  49. Zeigenfuse, M.D., & Lee, M.D. (2011). A comparison of three measures of the association between a feature and a concept.  In L. Carlson, C. Hölscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, pp. 243-248. Austin, TX: Cognitive Science Society. [pdf]
  50. Lee, M.D., Steyvers, M., de Young, M., & Miller, B. (2011). A model-based approach to measuring expertise in ranking tasks. In L. Carlson, C. Hölscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, pp. 1304-1309. Austin, TX: Cognitive Science Society. [pdf]
  51. Pooley, J.P., Lee, M.D., & Shankle, W.R. (2011). Modeling multitrial free recall with unknown rehearsal times. In L. Carlson, C. Hölscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, pp. 108-113. Austin, TX: Cognitive Science Society. [pdf]
  52. Asher, D., Zhang, S., Zaldivar, A., Lee, M.D., & Krichmar, J. (2012). Modeling individual differences in socioeconomic game playing. In N. Miyake, D. Peebles, & R. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society, pp. 90-95. Austin, TX: Cognitive Science Society. [pdf]
  53. van Ravenzwaaij, D., Newell, B.R., Moore, C.P., & Lee, M.D. (2013). Using recognition in multi-attribute decision environments. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society, pp. 3627-3632. Austin, TX: Cognitive Science Society. [pdf]
  54. Guan, H., Lee, M.D., & Silva, A. (2014). Threshold models of human decision making on optimal stopping problems in different environments. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society, pp. 553-558. Austin, TX: Cognitive Science Society. [pdf] [data]
  55. Lee, M.D., Liu, E.C., & Steyvers, M. (2015). The roles of knowledge and memory in generating top-10 lists. In D.C. Noelle & R. Dale (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society, pp. 1267-1272. Austin, TX: Cognitive Science Society. [pdf]
  56. Guan,  H,. Lee, M.D., & Vandekerckhove, J. (2015). A hierarchical cognitive threshold model of human decision making on different length optimal stopping problems. In D.C. Noelle & R. Dale (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society, pp. 824-829. Austin, TX: Cognitive Science Society. [pdf] [supplement]
  57. Danileiko, I., Lee, M.D., & Kalish, M.L. (2015). A Bayesian latent mixture approach to modeling individual differences in categorization using General Recognition Theory. In D.C. Noelle & R. Dale (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society, pp. 501-506. Austin, TX: Cognitive Science Society. [pdf] [supplement]
  58. Mistry, P.K., Lee, M.D., & Newell, B.R. (2016). An empirical evaluation of models for how people learn cue search orders. In J. Trueswell, A. Papafragou, D. Grodner, & D. Mirman (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society, pp. 211-216. Austin, TX: Cognitive Science Society. [pdf] [osf]
  59. Danileiko, I., & Lee, M.D. (2016). Inferring individual differences between and within exemplar and decision-bound models of categorization. In J. Trueswell, A. Papafragou, D. Grodner, & D. Mirman (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society, pp. 2825-2830. Austin, TX: Cognitive Science Society. [pdf] [osf]
  60. Mistry, P.K., Skewes, J., & Lee, M.D. (2018). An adaptive signal detection model applied to understanding autism spectrum disorder. In C. Kalish, M. Rau, J. Zhu, & T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society, pp. 774-779. Austin, TX: Cognitive Science Society. [pdf]
  61. Westfall, H.A., & Lee, M.D. (2021). A model-based analysis of changes in the semantic structure of free recall due to cognitive impairment. In T. Fitch, C. Lamm, H. Leder, & K. Teßmar-Raible (Eds.), Proceedings of the 43rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
  62. Banavar, N.V., Lee, M.D., & Bornstein, A.M. (2021). Sequential effects in non-sequential tasks. In T. Stewart (Ed.), Proceedings of the 19th International Conference on Cognitive Modeling. [pdf]
  63. Westfall, H.A., & Lee, M.D. (in press). A model of free recall for multiple encounters of semantically-related stimuli with an application to understanding cognitive impairment. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
  64. Villarreal, M., Vaday, S., & Lee, M.D. (in press). Categorization in environments that change when people learn. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
  65. Montgomery, L.E., & Lee, M.D. (in press). The wisdom of the crowd and framing effects in spatial knowledge. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]

 Reports

  1. Lee, M.D., & Vickers, D. (1998). Psychological approaches to data visualisation. Defence Science and Technology Organisation Research Report, DSTO-RR-0135. [link here]
  2. Lee, M.D. (1999). Algorithms for representing similarity data. Defence Science and Technology Organisation Research Report, DSTO-RR-0152. [link here]
  3. Dry, M., Lee, M.D., Vickers, D., & Huf, S. (2005). Psychological implications for submarine display design. Defence Science and Technology Organisation Technical Report, DSTO-TR-1766. [link here]
  4. Pooley, J.P., & Lee, M.D. (2012). A correction to the SIMPLE model of free recall. Institute for Mathematical and Behavioral Science, Technical Report MBS-12-04. [link here]

Other

  1. Lee, M.D. (2002). Book review of R. Decker and W. Gaul, Eds., Classification and Information Processing at the Turn of the Millenium. Journal of Classification, 19(1), 183-186.
  2. Obituary for Professor Douglas Vickers, The Advertiser, Saturday 18th December, 2004, p. 80.
  3. Obituary for Professor Douglas Vickers, The Adelaidean, Volume 13, Number 11, December 2004, p. 12. (with T. Nettelbeck)
  4. Storms, G., Navarro, D.J., & Lee, M.D. (2010). Introduction to the special issue on formal modeling of semantic concepts. Acta Psychologica, 133, 213-215. [pdf]
  5. Lee, M.D., Vi, J., & Danileiko, I. (2017), Testing the ability of the surprisingly popular algorithm to predict the 2017 NBA playoffs. Working paper. [pdf] [osf]
  6. Lee, M.D., Narens, L., & Wagenmakers, E.-J. (2018). In memorium: William H. Batchelder. [link] [pdf] [chess endgame study]
  7. Lee, M.D. (2018). In vivo: Multiple approaches to hierarchical modeling. In S. Farrell and S. Lewandowsky, Computational Modeling of Cognition and Behavior. Cambridge University Press. [pdf]