Research Interests

• COGNITIVE AND MATHEMATICAL PSYCHOLOGY
  

  Current Research Efforts:

  • (1) Model evaluation and comparison: My current work, in the field of mathematical and cognitive modeling, involves highly interdisciplinary research efforts spanning psychology, cognitive science, computer science, and statistics. Over the last two decades scientists have proposed many computational models of various cognitive processes, including memory, attention, categorization, and language. In our program of research, we develop quantitative methods that aid in evaluating and understanding the performance of those models. Only with such tools can we learn enough about the behavior of these models to choose the best one or understand what modifications must be made to others in order to improve their performance, thereby further strengthening the link between theory and data. Some of the key questions we investigate include how well a theory is instantiated in a model and how well the model account for human data. The particular approaches that we have employed to attack these issues are Bayesian and Markov chain Monte Carlo. The work has been supported by grants from the National Institute of Health.
  • (2) Experimental design optimization: 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 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. The work is supported by the National Institute of Health.

Manuscripts in Preparation and under Review

Edwards, M. C. & Myung, J. I. (in preparation). A Markov chain Monte Carlo tutorial for applied social scientists.

Myung, J. I., Cavagnaro, D. R. & Pitt, M. A. (in preparation). Mathematical modeling.

Pitt, M. A. & Myung, J. I. (under review). Designin a better experiment.

Wu, H., Myung, J. I., & Batchelder, W. H. (under review). On the minimum description length complexity of multinomial processing tree models.

Recent Presentation Slides

Tutorial on model selection methods.

Regarding the distinguishability of retention functions .

Publications 

Journal Articles

Wu, H., Myung, J. I., & Batchelder, W. H. (in press). Minimum description length model selection of multinomial processing tree models. Psychonomic Bulletin & Review.

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

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 (under construction).]

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 (under construction).]

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

Grünwald, 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., 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

Cavagnaro, D. R., Pitt, M. A. & Myung, J. I. (in press). Adaptive design optimization in experiments with people. Advances in Neural Information Processing Systems, vo. 21.

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.), Proceedings of the 31st Annual Meeting of the Cognitive Science Society (pp. 93-98). 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.