Authors in BOLD = Lab members

2026

Lee, S. H., C., Chung, Oh, M. H., & Ahn, W. Y. (preprint; under review). Inverse reinforcement learning reveals action-oriented value signals in naturalistic decision making. bioRxiv. (link to the paper)

Lee, C., Kim, J., Kim, W., Jung, Y. M., Lee, S. H., Leveraging Psychophysical Attentional Distribution for Gaze-Augmented Reward Modeling (accepted). Advances in Neural Information Processing Systems, 39 (NeurIPS 2026).

Kwon, B., Seo, J., Lee, S. H., Discovering Structurally Plausible and Interpretable Cognitive Models with Large Language Models (accepted). Advances in Neural Information Processing Systems, 39 (NeurIPS 2026).

Lee, J. H., Lee, S. H., Yang, J., Kim, H., Pitt, M., Park, H., ... & Ahn, W. Y. (2026). Rapid and Reliable Computational Markers for Predicting Daily Smoking Behavior and Smoking Cessation Treatment Outcome. Neuropsychoparmacology. (link to the paper)

2025

Kim, J.*, Lee, C.*, Kwon, B., Pitt, M. A., Lee, S. H., A Tutorial on Gaussian Process Active Learning (in revision).

*Denotes equal contribution

2024

Lee, S. H., Oh, M. H., & Ahn, W. Y. (2024). Inverse reinforcement learning captures value representations in the reward circuit in a real-time driving task: a preliminary study. Computational Cognitive Neuroscience. (link to the paper)

Lee, S. H., Song, M. S., Oh, M. H., & Ahn, W. Y. (2024) Bridging the gap between self-report and behavioral laboratory measures: A real-time driving task with inverse reinforcement learning. Psychological Science, 35(4), 345-357. (link to the paper)

Lee, S. H., & Pitt, M. A. (2024). Implementation of an online spacing flanker task and evaluation of its test-retest reliability using measures of inhibitory control and the distribution of spatial attention. Behavior Research Methods, 1-12. (link to the paper)

~ 2023

Kwon, M., Lee, S. H., Ahn, W. Y. (2023). Adaptive design optimization as a promising tool for reliable and efficient computational fingerprinting. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 8(8), 798-804.

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(3), 1-14.

Lee, S. H., & Pitt, M. A. (2022). Individual differences in selective attention reveal the non-monotonicity of visual spatial attention and its association with working memory capacity. Journal of Experimental Psychology: General, 151(4), 749-762.

Lee, S. H., Pitt, M. A., & Myung, J. I. (2018). Computational modeling of cognitive control in a flanker task. Proceedings of the 40th Annual Meeting of the Cognitive Science Society, pp. 671-676.

Kim, S., Lee, S. H., & Cho, Y.S. (2015). Control processes through the suppression of the automatic response activation triggered by task-irrelevant information in the Simon-type tasks. Acta Psychologica, 162, 51-61.

Lee, S. H., Kim, S. P., & Cho, Y. S. (2015). Self-concept in fairness and rule establishment during a competitive game: a computational approach. Frontiers in Psychology, 6, 1321.