My research explores how people differ in cognitive traits such as attention, working memory, and numerical cognition. Human behaviors in psychological tasks typically show general trends at a population level yet differ greatly at an individual level. Exploring individual differences provides additional information about psychological constructs that underlie human behaviors.
I build and evaluate Bayesian cognitive models to provide parameters that would capture relevant properties of complex data, facilitating the analysis of individual differences. I also use machine-learning models based on Gaussian processes and deep neural network to identify individual differences in data-driven manners, and to address theoretical questions in novel ways.
Lee, S. H., Kim, D., Opfer, J., Pitt, M. A., & Myung, J. I. (2021). A number-line task with
a Bayesian active learning algorithm provides insights into the development of non-symbolic number estimation. Psychonomic Bulletin & Review, 1-14.
Lee, S. H., & Pitt, M. A. (2021). 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, Advance online publication
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.