Current Research

Adaptive Design Optimization (ADO)

Experimentation is fundamental to the advancement of psychological science, whether one is interested in studying the neural basis of memory dysfunction in a cognitive neuroscience experiment, or understanding the mechanisms governing choice behavior in a decision-making experiment. To ensure measurement episodes are optimal and thus maximize scientific inference, there has been a growing interest by researchers in the design of adaptive experiments that lead to rapid accumulation of information about the phenomenon under study with the fewest possible measurements, which would in turn help accelerate the acquisition of new knowledge. Building on the foundational work in Bayesian statistics and machine learning, our lab has developed and deployed our own adaptive experimentation methodology, dubbed Adaptive Design Optimization (ADO; see the diagrams below), in which a sequence of queries (e.g., choices of experimental stimuli and task parameters) in an experiment is optimized and customized on the fly to maximize information gain. We have applied and are currently applying ADO for optimizing experiments in the areas of decision making, cognitive control, visual psychophysics, delayed discounting, cognitive neuroscience, computational psychiatry, and nanomaterials. The work has been supported by grants from the National Institute of Health (NIH) and the Air Force Office of Scientific Research (AFOSR).

CogSci*2019 Tutorial on ADOpy, Python Package for ADO (package download from GitHub site)

Manuscripts under review

Myung, J. I., Deneault, J. R., Chang, J., Kang, I., Maruyama, B., & Pitt, M. A. (2023). Multi-objective Bayesian optimization: A case study in material extrusion.

Chang, J., Pitt, M. A., & Myung, J. I. (2023). Probing human catagorization of naturalistic images by combining deep neural networks with cognitive models.

Publications 

Journal articles since 2014

De Boeck, P., Pek, J., Walton, K., Wegener, D., Turner, B., Andersen, B., Beauchaine, T., Lecavalier, L., Myung, J. I., & Petty, R. (2023). Questioning psychological constructs: Current issues and proposed changes. Psychological Inquiry, 34(4), 239-257.

Pereira, C. L. W., Zhou, R., Pitt, M. A., Myung, J., Rossi, J., Caverzasi, E., Rah, E., Allen, E., Mandelli, M. L., Meyer, M., Miller, Z. A., & Tempini, L. G. (2022). Probabilistic decision-making in children with dyslexia. Frontiers in Neuroscience, 16 (June 2022, 782306). https://doi.org/10.3389/fnins.2022.782306

Lee, S. H., Kim, D., Opfer, J. , Pitt, M. A. & Myung, J. I. (2022). A number-line task with a Bayesian active learning algorithm provides insights into the development of non-symbolic number estimation. Psychonomic Bulletin & Review, 29, 971-984.  https://doi.org/10.3758/s13423-021-02041-5

Zhou, R., Myung, J. I., & Pitt, M. A. (2021). The scaled target learning model: Revisiting learing in the balloon analogue risk task. Cognitive Psychology, 128 (August 2021, 101407). https://doi.org/10.1016/j.cogpsych.2021.101407

Deneault, J. R., Chang, J., Myung, J., Hooper, D., Armstrong, A., Pitt, M., & Maruyama, B. (2021). Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer. Materials Research Society (MRS) Bulletin, 46, 566-575. https://doi.org/10.1557/s43577-021-00051-1

Zhou, R., Myung, J. I., Mathews, C. A., & Pitt, M. A. (2021). Assessing the validity of three tasks of risk-taking propensity. Journal of Behavioral Decision Making, 34(4), 555-567. https://doi.org/10.1002/bdm.2229

Chang, J., Kim, J., Zhang, B.-T., Pitt, M. A., & Myung, J. I. (2021). Data-driven experimental design and model development using Gaussian Process with active learning. Cognitive Psychology, 125 (March 2021, 101360).   https://doi.org/10.1016/j.cogpsych.2020.101360 

Yang, J., Pitt, M. A., Ahn, W.-Y., & Myung, J. I. (2021). ADOpy: A Python package for adaptive design optimization.  Behavior Research Methods, 53, 874-897. https://doi.org/10.3758/s13428-020-01386-4

Haines, N., Beauchaine, T. P., Galdo, M., Rogers, A. H., Hahn, H., Pitt, M., Myung, J., Turner, B. M., & Ahn, W.-Y. (2020). Anxiety predicts diminished preference for immediate rewards in trait-impulsive individuals: A hierarchical Bayesian analysis. Clinical Psychological Science, 8(6), 1017-1036.

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, 3(4), 400-429.

Ahn, W.-Y., Gu, H., Shen, Y., Haines, N., Hahn, H., Teater, J. E., Myung, J. I., & Pitt, M. A. (2020). Rapid, precise, and reliable phenotyping of delay discounting using a Bayesian learning algorithm. Scientific Reports 10: 12091.

Chang, J., Nikolaev, P., Carpena-Nunez, 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: 9040.

Pitt, M. A., & Myung, J. I. (2019). Robust modeling through design optimization. Computational Brain & Behavior, 2 (3-4), 200-201. Commentary.

Walsh, M. W., Gluck, K. A., Gunzelmann, G., Jastrzembski, T., Krusmark, M., Myung, J. I., Pitt, M. A., & Zhou, R.(2018). Mechanisms underlying the spacing effect in learning: A comparison of three computational models. Journal of Experimental Psychology: General, 147(9), 1325-1348.

Kim, W., Pitt, M. A., Lu, Z.-L., & Myung, J. I. (2017). Planning beyond the next trial in adaptive experiments: A dynamic programming approach. Cognitive Science, 41, 2234-2252.

Aranovich, G. J., Cavagnaro, D. R., Pitt, M. A., Myung, J. I., & Mathews, 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.

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. 

Edited volumes/special journal issues

Batchelder, W. H., Colonius, H., Dzhafarov, E. and Myung, J. I., eds., (2017). 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 , 1-231.