Department of Psychology
1835 Neil Avenue
Ohio State University
Columbus, OH 43210-1287

pitt.2@osu.edu

614-292-4193

 

I have two programs of research. Each is summarized below.

Spoken Language Understanding

Our team examines the mechanisms involved in spoken language understanding. How does the mind of a listener translate the sounds emanating from a talker’s mouth into the words intended by the talker? Projects explore how listeners’ knowledge of language influences recognition, and how this ability is accomplished when there are competing talkers.  How does the brain know which acoustic bits belong to the talker you want to hear? Analyses of the Buckeye Speech Corpus  inform these research questions.

Visit the lab here (publications, people, projects)!

 

Computational Cognitive Modeling

Accurate inference is fundamental to advancing science. We develop methods for improving inference in cognitive modeling.  Early work focused on the use of statistical methods that are applied after data have been collected in an experiment (Bayes Factor, MDL). Our current work is in optimal experimental design, which focuses on improving inference at the front end of an experiment, before data have been collected. How can the the informativeness of data be improved while they are being collected? We have applied active learning in many modeling contexts, which can simultaneously improve inference and make experiments efficient. Most recently this work has expanded to using data-driven methods (Bayesian optimization, Gaussian Proceses) to address a wider range of inference problems in and outside of psychology.  This work is done in collaboration with my close friend and colleague Jay Myung.

Publications

Chang, J., Kim, J., Zhang, B.T., Pitt, M.A., & Myung, J.I. (in press pending minor revisions). Data-driven experimental design and model development using Gaussian process with active learning. Cognitive Psychology.

Haines, N., Beauchaine, T. P., Matthew, G., Rogers, A. H., Hahn, H., Pitt, M. A., Myung, J. I., Turner, B. M., Ahn, W.-Y. (in press). Anxiety predicts diminished preference for immediate rewards in trait-impulsive individuals: A hierarchical Bayesian analysis. Clinical Psychological Science.

Yang, J., Pitt, M. A., Ahn, W.-Y., & Myung, J. I. (in press). ADOpy: A Python package for adaptive design optimization. Behavior Research Methods.

Ahn, W.-Y., Gu, H., Shen, Y., Haines, N., Hahn, H. A., Teater, J. E., Myung, J. I., & Pitt, M. A. (2020). Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm. Scientific Reports, 10(1), 12091. doi.org/10.1038/s41598-020-68587-x

Bahg, G., Sederberg, P. B., Myung, J. I., Li, X., Pitt, M. A., Lu, Z.-L., &Turner, B. M. (2020). Real-time Adaptive Design Optimization Within Functional MRI Experiments. Computational Brain & Behavior. doi.org/10.1007%2Fs42113-020-00079-7

Chang, J., Nikolaev, P., Carpena-Núñez, J., Rao, R., Decker, K., Islam, A. E., Kim, J., Pitt, M. A., Myung, J. I., & Maruyama, B. (2020). Efficient Closed-loop Maximization of Carbon Nanotube Growth Rate using Bayesian Optimization. Scientific Reports, 10(1), 9040. doi.org/10.1038/s41598-020-64397-3

Pitt, M. A., & Myung, J. I. (2019). Robust Modeling Through Design Optimization. Computational Brain & Behavior.doi.org/10.1007/s42113-019-00050-1

Aranovich, G. J., Cavagnaro, D., Pitt, M. A., Myung, J. I., Matthews, C. A. (2017). A model-based analysis of decision making under risk in obsessive-compulsive and hoarding disorders.Journal of Psychiatric Research, 90, 126-132. 10.1016/j.jpsychires.2017.02.017

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.

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), 1-17.

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), 18-29

Kim, W., Pitt, M. A., Lu, Z.-L., & Myung, J. I. (2016). Planning beyond the next trial in adaptive experiments: A dynamic programming approach. Cognitive Science. DOI:10.1111/cogs.12467

Myung, J.I., Cavagnaro, D.R., & Pitt, M.A. (2016). Model evaluation and selection. In W.H. Batchelder et al (Eds.) New Handbook of Mathematical Psychology: Vol 1: Foundations and Methodology,(pp. 552-598). Cambridge, Cambridge

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, 2465-2492.

Montenegro, M., Myung, J. I., & Pitt, M. A. (2014). Analytic expressoins for the REM model of recognition memory. Journal of Mathematical Psychology, 60, 23-28. doi:10.1016/j.jmp.2014.05.003

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, 255-289. doi:10.1007/s11166-013-9179-3

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, 358-375. doi:10.1287/mnsc.1120.1558

Kim, W., Pitt, M. A., & Myung, J. I. (2013). How do PDP models learn quasiregularity? Psychological Review, 120, 903-916. doi:10.1037/a0034195.

Myung, J. I., Cavagnaro, D. R.,& Pitt, M. A. (2013). A tutorial on adaptive design optimization. Journal of Mathematical Psychology, 57, 53-67. doi:10.1016/j.jmp.2013.05.005

Pitt, M.A., & Tang, Y. (2013). What should be the data sharing policy of cognitive science. Topics in Cognitive Science, 5, 214-221.

Cavanaro, D.R., Pitt, M.A., & Myung, J.I. (2011). Model Discrimination through Adaptive Experimentation. Psychonomic Bulletin & Review, 18, 204-210.

Myung, J.I., Tang, Y., & Pitt, M.A. (2009). Evaluation and Comparison of Computational Models. Methods in Enzymology, 287-304.

Pitt, M.A., Myung, J.I., Montenegro, M., & Pooley, J. (2008). Measuring the flexibility of localist connectionist models of speech perception. Cognitive Science, 32, 1285-1303.

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.

Myung, J.I., Pitt, M.A., & Navarro, D.J. (2007). Does Response Scaling Cause the Generalized Context Model to Mimic a Prototype odel? Psychonomic Bulletin & Review, 14, 1043-1050.

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

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

Pitt, M.A., Kim, W., Navarro, D.J., & Myung, J.I. (2006). Global model analysis by parameter space partitioning. Psychological Review, 113, 57-83.

Grunwald, P., Myung, I., & Pitt, M.A. (2005). Advances in Minimum Description Length: Theory and Application. Cambridge, MA: IT Press.

Navarro, D., Pitt, M.A., & Myung, I. (2004). Assessing the Distinguishability of Models and the Informativeness of Data. Cognitive Psychology, 49, 47-84.

Pitt, M.A., Kim, W., & Myung, I.J. (2003). Flexibility versus 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 Science, 6, 421-425. TICS homepage

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

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