Journal articles since 2016

Waelder, R., Kim, W., Pitt, M. A., Myung, J. I., & Maruyama, B. (2025). Multi-objective Bayesian optimization of carbon nanotube yield and diameter control at synthesis. APL (Applied Physics Letters) Machine Learning, 3(2), 026114. DOI: 10.1063/5.0267704

Deneault, J. R., Kim, W., Kim, J., Gu, Y., Chang, J., Maruyama, B., Myung, J. I. & Pitt, M. A. (2025). Preferential Bayesian optimization improves the efficiency of printing objects with subjective qualities. Digital Discovery, 4, 723-737.  DOI: 10.1039/D4DD00320A

Myung, J. I., Deneault, J. R., Chang, J., Kang, I., Maruyama, B., & Pitt, M. A. (2025). Multi-objective Bayesian optimization: A case study in material extrusion. Digital Discovery, 4, 464-476. DOI: 10.1039/D4DD00281D

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. 

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.