Research Interests

Current Research Efforts:

Adaptive Design Optimization (ADO): Experimentation is fundamental to the advancement of psychological science. One of the greatest challenges in model comparison is designing an experiment that clearly distinguishes between formal models. In design optimization, we are interested in identifying an experimental design under which the experiment is likely to produce the most differentiating outcomes between the models under consideration in some defined sense. For example, in designing an experiment investigating whether the rate of forgetting over time follows a power curve or an exponential curve, the experimenter must determine “optimally” the values of time intervals between the study and test sessions. Recent developments in sampling-based Monte Carlo methods in Bayesian statistics make it possible to determine these values and thereby identify an optimal design. In the lab, we have applied and are currently applying ADO for optimizing experiments in the fields of retention memory, decision making, cognitive development, visual psychophysics, skill learning, and cognitive neuroscience. The work has been supported by the National Institute of Mental Health. (See the Wordle image on the right.)

Manuscripts in Preparation and under Review.

Aranovich, G. J., Cavagnaro, D. R., Pitt, M. A., Myung, J. I., & Mathews, C. A. (under review). Dimensional analysis of decision making under risk in obsessive-compulsive and hoarding disorders.

Walsh, M. W., Gluck, K. A., Gunzelmann, G., Jastrzembski, T. Krusmark, M., Myung, J. I., Pitt, M. A., & Zhou, R. (under review). The effects of spaced practice on retention and relearning.

Recent Presentation Slides

Hierarchical adaptive design optimization (HADO)

Tutorial on adaptive design optimization (ADO)

Model Evaluation and Selection in Cognitive Computational Modeling

Tutorial on model comparison methods.


Publications 

Journal Articles

Kim, W., Pitt, M. A., Lu, Z.-L., & Myung, J. I. (in press). Planning beyond the next trial in adaptive experiments: A dynamic programming approach. Cognitive Science.

Cavagnaro, D. R., Aranovich, G. J., McClure, S. M., Pitt, M. A., & Myung, J. I. (2016). On the functional form of temporal discounting: An optimized adaptive test. Journal of Risk and Uncertainty, 52, 233-254.

Hou, F., Lesmes, L., Kim, W., Gu, H., Pitt, M. A., Myung, J. I., & Lu, Z.-L. (2016). Evaluating the performance of the quick CSF method in detecting contrast sensitivity function changes. Journal of Vision, 16(6):18, 1-19.

Gu, H., Kim, W., Hou, F., Lesmes, L., Pitt, M. A., Lu, Z.-L., & Myung, J. I. (2016). A hierarchical Bayesian approach to adaptive vision testing: A case study with the contrast sensitivity function. Journal of Vision, 16(6):15, 1-17.

Kim, W., Pitt, M. A., Lu, Z.-L., Steyvers, M., & Myung, J. I.. (2014). A hierarchical adaptive approach to optimal experimental design. Neural Computation, 26, 2463-2492.

Montenegro, M., Myung, J. I., & Pitt, M. A. (2014). Analytic expressions for the REM model of recognition memory. Journal of Mathematical Psychology, 60, 23-28.

Cavagnaro, D. R., Pitt, M. A., Gonzalez, R., & Myung, J. I. (2013). Discriminating among probability weighting functions using adaptive design optimization. Journal of Risk and Uncertainty, 47(3), 255-289.

Kim, W., Pitt, M. A., & Myung, J. I. (2013). How do PDP models learn quasiregularity? Psychological Review, 120 (4), 903-916 .

Myung, J. I., Cavagnaro, D. R., & Pitt, M. A. (2013). A tutorial on adaptive design optimization. Journal of Mathematical Psychology, 57, 53-67.

Cavagnaro, D. R., Gonzalez, R., Myung, J. I., & Pitt, M. A. (2013). Optimal decision stimuli for risky choice experiments: An adaptive approach. Management Science, 59(2), 358-375.

Cavagnaro, D. R., Pitt, M. A. & Myung, J. I. (2011). Model discrimination through adaptive experimentation. Psychonomic Bulletin & Review, 18(1), 204-210.

Wu, H., Myung, J. I., & Batchelder, W. H. (2010). Minimum description length model selection of multinomial processing tree models. Psychonomic Bulletin & Review, 17, 275-286.

Wu, H., Myung, J. I., & Batchelder, W. H. (2010). On the minimum description length complexity of multinomial processing tree models. Journal of Mathematical Psycholog, 54, 291-303.

Cavagnaro, D. R., Myung, J. I., Pitt, M. A. & Kujala, J. (2010). Adaptive design optimization: A mutual information-based approach to model discrimination in cognitive science. Neural Computation, 22, 887-905.

Myung, J. I & Pitt, M. A. (2009). Optimal experimental design for model discrimination. Psychological Review, 116, 499-518. [Click here  to go to the design optimization (DO) page with documentation and C++ code.]

Myung, J. I., Tang, Y. & Pitt, M. A. (2009). Evaluation and comparison of computational models. Methods in Enzymology, 454, 287-304.

Pitt, M. A., Myung, J. I., Montenegro, M. & Pooley, J. (2008). Measuring model flexibility with parameter space partitioning: An introduction and application example. Cogntive Science, 32, 1285-1303.

Myung, J. I., Pitt, M. A. & Navarro, D. J. (2007). Does response scaling cause the generalized context model to mimic a prototype model? Psychonomic Bulletin & Review, 14, 1043-1050.

Pitt, M. A., Myung, J. I. & Altieri, A. (2007). Modeling the word recognition data of Vitevitch and Luce (1998): Is it ARTful? Psychonomic Bulletin & Review, 14, 442-448.

Myung, J. I., Montenegro, M., & Pitt, M. A. (2007). Analytic expressions for the BCDMEM model of recognition memory. Journal of Mathematical Psychology, 51, 198-204.

Pitt, M. A., Kim, W., Navarro, D. J. & Myung, J. I. (2006). Global model analysis by parameter space partitioning. Psychological Review, 113, 57-83. [Click here  to go to the Parameter Space Partitioning (PSP) homepage with tutorial, Java applets, and Matlab programs.]

Myung, J. I., Navarro, D. J. & Pitt, M. A. (2006). Model selection by normalized maximum likelihood. Journal of Mathematical Psychology, 50, 167-179.

Myung, J. I., Karabatsos, G., & Iverson, G. (2005). A Bayesian approach to testing decision making axioms. Journal of Mathematical Psychology, 49(3), 205-225.

Navarro, D. J., Pitt, M. A. & Myung, I. J. (2004). Assessing the distinguishability of models and the informativeness of data . Cognitive Psychology, 49, 47-84.

Myung, I. J. & Pitt, M. A. (2004). Model comparison methods . Methods in Enzymology, 383, 351-366. 

Myung, I. J. (2003).  Tutorial on maximum likelihood estimation . Journal of Mathematical Psychology , 47, 90-100.

Pitt, M. A., Kim, W., & Myung, I. J. (2003). Flexibility vs generalizability in model selection . Psychonomic Bulletin & Review , 10, 29-44.

Pitt, M A & Myung, I J. (2002). When a good fit can be bad . Trends in Cognitive Sciences , 6(10) , 421-425. (Reprint posted with permission from Elsevier Science. Single copies of this article can be downloaded and printed for the reader's personal research and study.)

Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition . Psychological Review, 109(3) , 472-491.

Myung, I. J., Balasubramanian, V., & Pitt, M. A. (2000). Counting probability distributions: Differential geometry and model selection . Proceedings of the National Academy of Sciences USA, 97, 11170-11175.

Myung, I. J., Kim, C., & Pitt, M. A. (2000). Toward an explanation of the power-law artifact: Insights from response surface analysis. Memory & Cognition, 28 , 832-840.

Myung, I. J. (2000) The importance of complexity in model selection.    Journal of Mathematical Psychology, 44 (1) , 190-204.

Myung, I. J., & Pitt, M. A. (1997). Applying Occam's razor in modeling cognition: A Bayesian approach. Psychonomic Bulletin & Review, 4 , 79-95.

Myung, I. J., & Shepard, R. N. (1996). Maximum entropy inference and stimulus generalization. Journal of Mathematical Psychology, 40 , 342-347.

Myung, I. J., Ramamoorti, S, & Bailey, A. D., Jr (1996). Maximum entropy aggregation of expert predictions. Management Science. 42(10) , 1420-1436.

Myung, I. J. (1994). Maximum entropy interpretation of decision bound and context models of categorization. Journal of Mathematical Psychology, 38 , 335-365.

Myung, I. J. (1994). Is the representation meaningful?: A measurement theoretic view. Behavioral and Brain Sciences, 17(4) , 677-678.

Myung, I. J., Colbert, C. M., & Levy, W. B. (1994). A computational hypothesis of probability inference in neural networks and some relations to psychological models. Journal of Biological Systems, 2(3) , 367-384.

Busemeyer, J. R., Myung, I. J., & McDaniel M. A. (1993). Cue competition effects: empirical tests of adaptive network learning models. Psychological Science, 4(3) , 190-195.

Busemeyer, J. R., Myung, I. J., & McDaniel M. A. (1993). Cue competition effects: theoretical implications for adaptive network learning models. Psychological Science, 4(3) , 196-202.

Myung, I. J., and Busemeyer, J. R. (1992). Measurement free tests of a general state space model of prototype learning. Journal of Mathematical Psychology, 36(1), 32-67.

Busemeyer, J. R., and Myung, I. J. (1992). An adaptive approach to human decision making: learning theory, decision theory, and human performance. Journal of Experimental Psychology: General, 121(2) , 177-194.

Myung, I. J., and Busemeyer, J. R. (1989). Criterion learning in a deferred decision making task. American Journal of Psychology, 102 , 1-16.

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

Busemeyer, J. R., and Myung, I. J. (1987). Resource allocation decision making in an uncertain environment. Acta Psychologica, 66 , 1-19.

Edited volumes/journal issues

Batchelder, W. H., Colonius, H., Dzhafarov, E. and Myung, I. J., eds., (in press). New Handbook of Mathematical Psychology, Vol. 1: Measurement and Methodology. Cambridge, U.K.: Cambridge University Press.

Grunwald, P., Myung, I. J., & Pitt, M. A., eds., (2005). Advances in Minimum Description Length: Theory and Applications . MIT Press.

Myung, I. J., Forster, M., & Browne, M. W., eds. (2000). Special issue on model selection . Journal of Mathematical Psychology, 44 , 1-2.

Chapters/Encyclopedic Entries

Myung, J. I. & Pitt, M. A. (in press). Model comparison in psychology. In J. Wixted (Editor-in-Chief) & E.-J. Wagenmakers (Volume Editor), Steven’s Handbook of Experimental Psychology and Cognitive Neuroscience, Fourth Edition, Vol.5: Methodology. New York, NY: John Wiley & Sons.

Myung, J. I., Cavagnaro, D. R., & Pitt, M. A. (in press). Model evaluation and selection. In W. H. Batchelder, H. Colonius, E. Dzhafarov & J. I. Myung (eds.), New Handbook of Mathematical Psychology, Vol. 1: Measurement and Methodology. Cambridge University Press: Cambridge, U.K.

Cavagnaro, D. R., Myung, J. I., & Pitt, M. A. (2013). Mathematical modeling. In Todd D. Little (ed.), The Oxford Handbook of Quantitative Methods, Vol. 1 (pp, 438-453) , Oxford University Press: New York, NY.

Myung, J. I., Tang, Y. & Pitt, M. A. (2010). Evaluation and comparison of computational models. In Michael L. Johnson (ed.), Essential Numerical Computer Methods, pp. 511-527. Elsevier. (Note: This is an updated reprint version of Myung, Tang & Pitt (2009) for a standalone volume.)

Myung, J. I., Karabatsos, G. & Iverson, J. G. (2008). A statisticians view on Bayesian evaluation of informative hypotheses. In H. Hoijtink, I. Klugkist & P. Boelen (eds.) Bayesian Evaluation of Informative Hypotheses (pp. 309-327). Springer, Berlin.

Myung, I. J., & Navarro, D. J. (2005). Information matrix . In B, Everitt & D. Howel (eds.), Encyclopedia of Statistics in Behavioral Science, Vol. 2, pp., 923-924. Wiley.

Navarro, D. J. & Myung, I. J. (2005). Model evaluation. In B, Everitt & D. Howel (eds.), Encyclopedia of Statistics in Behavioral Science, Vol. 3, pp. 1239-1242. Wiley.

Myung, I. J., Pitt, M A., & Kim, W. (2005).  Model evaluation, testing and selection . In K. Lambert and R. Goldstone (eds.) The Handbook of Cognition, pp. 422-436. Sage Publication.

Su, Y., Myung, I. J. & Pitt, M. A. (2005). Minimum description length and cognitive modeling . In P. Grunwald, I. J. Myung, I. J., & M. A. Pitt (eds.) Advances in Minimum Description Length: Theory and Applications, pp.411-433. MIT Press. 

Myung, I. J. & Pitt, M. A. (2003). Model fitting. In L. Nadel (ed.), The Encyclopedia of Cognitive Science , Vol. 3, pp. 47-51. London, UK: Macmillan.

Myung, I. J., & Pitt, M. A. (2002). Mathematical modeling. In J. Wixted (ed.), Stevens' Handbook of Experimental Psychology (Third Edition), Volume IV (Methodology) , pp. 429-459. New York, NY: John Wiley & Sons.

Myung, I. J. (2001). Computational approaches to model evaluation. In N. J. Smelser and P. B. Baltes (eds.), The International Encyclopedia of the Social and Behavioral Sciences , pp. 2453-2457. Oxford, UK: Elsevier.

Myung, I. J., & Pitt, M. A. (1998). Issues in selecting mathematical models of cognition. In J. Grainger & A. M. Jacobs (eds.), Localist Connectionist Approaches to Human Cognition , pp. 327-355. Lawrence Erlbaum Associates.

Busemeyer, J. R., and Myung, I. J. (1989). An adaptive theory of human decision making. In D. Vickers and P. Smith (eds.), Human Information Processing: Measurements, Mechanisms, and Models , pp. 461-469, XXIV International Congress of Psychology. North Holland.

Conference Proceedings


Kim, W., Pitt, M. A., Lu, Z.-L., M., Steyvers, M., Gu, H. & Myung, J. I. (2014). A hierarchical adaptive approach to the optimal design of experiments. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. xxxx-xxxx). Austin, TX: Cognitive Science Society.

Gu, H., Myung, J. I., Pitt, M. A., & Lu, Z.-L. (2013). Bayesian adaptive estimation of psychometric slope and threshold with differential evolution. In M. Knauff, M., Pauen, N., Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35nd Annual Meeting of the Cognitive Science Society (pp. 2452-2457). Austin, TX: Cognitive Science Society.

Pitt, M. A. & Myung, J. I. (2011). How is hair gel quantified? In E. J. Davelaar (ed.), Proceedings of the Twelfth Neural Computation and Psychology Workshop (pp. 356-360). London, UK: World Scientific.

Tang, Y., Young, C., Myung, J. I., Pitt, M. A. & Opfer, J. (2010). Optimal inference and feedback for representational change. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Meeting of the Cognitive Science Society (pp. 2572-2577). Austin, TX: Cognitive Science Society.

Cavagnaro, D. R., Pitt, M. A. & Myung, J. I. (2009). Adaptive design optimization in experiments with people. Advances in Neural Information Processing Systems, 22, 234-242.

Cavagnaro, D. R., Tang, Y., Myung, J. I. & Pitt, M. A. (2009). Better data with fewere participants and trials: Improving experimental efficiency with adaptive design optimization.  In N. A. Taatgen & H. van Rijn (eds.), Austin, TX: Cognitive Science Society.

Myung, J. I., Pitt, M. A., Tang, Y. & Cavagnaro, D. R. (2009). Bayesian adaptive optimal design of psychology experiments.  In Proceedings of the 2nd International Workshop in Sequential Methodologies (IWSM2009), CD ROM format (Troyes, France: June, 2009).

Myung, J. I., Pitt, M. A. & Navarro, D. J. (2005). Model selection in cognitive science as an inverse problem. Proceedings of SPIE, vol. 5674 (Computational Imaging III), 219-228.

Kim, W., Navarro, D. J., Pitt, M. A. & Myung, I. J.  (2004). An MCMC-based method of comparing connectionist models in cognitive science. In B. Schokolpf (ed.), Advanceds in Neural Information Processing Systems , vol 16, 937-944.

Navarro, D. J., Myung, I. J., Pitt, M. A. & Kim, W. (2003). Global model analysis by landscaping.  In R. Alterman & D. Kirsh (eds.), Proceedings of the 25th Annual Meeting of the Cognitive Science Society, CD-ROM format, (Boston, MA: August, 2003).

Myung, I. J. & Pitt, M. A. (2001). A minimum description length approach for selecting among qualitative models of cognition . In L Chen and Y Zhuo (eds.), Proceedings of the Third International Conference on Cognitive Science (ICCS2001: Beijing, China), pp. 364-369. University of Science and Technology of China Press.

Myung, I. J., Pitt, M. A., Zhang, S., & Balasubramanian, V. (2001). The use of MDL to select among computational models of cognition . In T K Leen, T G Dietterich & V Tresp (eds.), Advances in Neural Information Processing Systems, vol. 13 ., pp. 38-44. MIT Press.

Myung, I. J., Brunsman IV, A. E., & Pitt, M. A. (1999). True to thyself: Assessing whether computational models of cognition remain faithful to their theoretical principles. In M. Hahn & S.C. Stoness (eds.), Proceedings of the 21st Annual Conference of the Cognitive Science Society , pp. 462-467. Mahwah, New Jergey: Lawrence Erlbaum Associates.

Myung, I. J., Kim, C., & Levy, W. B. (1997). Context-dependent recognition in a self-organizing recurrent network. In M.G. Shafto & P. Langley (eds.), Proceedings of the Nineteenth Annual Meeting of the Cognitive Science Society , pp. 530-535. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Kim, C., & Myung, I. J. (1995). Incorporating real-time random effects in neural networks: A temporal summation mechanism. In J.D. Moore & J.F. Lehman, Proceedings of the Seventeenth Annual Meeting of the Cognitive Science Society , pp. 472-477. Hillsdale, NJ: Lawrence Erlbaum Associate.

Myung, I. J., and Busemeyer, J. R. (1989). A state-space model for prototype learning. In G. Olson and E. Smith (eds.), Proceedings of the Eleventh Annual Meeting of the Cognitive Science Society , pp. 50-57. Earbaum Associate.  


Unpublished essay:

Pitt, M. A., & Myung, I. J. NHST: Can Psychology Do Better? Unpublished essay.


"[A]cademic research is not a sausage machine." (David Willetts, U.K. Minister of State for Univesities and Science, 2012)

"All that lives must die, passing through nature to eternity." (from Hamlet, William Shakespeare)

"Duncan is in his grave. His life's troubles are over; he sleeps well. Malice domestic, foreign levy, nothing can touch him now." ( from Macbeth, William Shakespeare)

"It was in the reign of George III that the aforementioned personages lived and quarrelled; good or bad, handsome or ugly, rich or poor, they are all equal now." (from movie Barry Lyndon (1975), Stanley Kubrick)