Computational Cognition and Creativity Lab

The Computational Cognition and Creativity Lab uses computational models and empirical data to investigate the mechanisms underlying creativity processes like the generation and evaluation of novel ideas. We are particularly interested in automating creativity assessment and making it widely and freely accessible to researchers and educators. We employ diverse methodologies like natural language processing, large language models, machine learning, distributional semantic modeling, simulation, and interactive web applications. We use the statistical and graphics platform, R, for modeling and data work. Other topics we explore include metacognition in creativity.

Selected Publications

  • Johnson, D. R., Kaufman, J. C., Baker, B. S., Patterson, J. D., Barbot, B., Green, A. E., van Hell, J., Kennedy, E., **Sullivan, G. F., Taylor, C. L., Ward, T., & Beaty, R. E. (2022). Divergent semantic integration (DSI): Extracting creativity from narratives with distributional semantic modeling. Behavior Research Methods.

  • Johnson, D. R., & Hass, R. W. (2022). Semantic context search in creative idea generation. Journal of Creative Behavior, 56(3), 362-381.

  • Beaty, R. E., & Johnson, D. R. (2021). Automating creativity assessment with SemDis: An open platform for computing semantic distance. Behavior Research Methods, 53, 757-780.

  • Johnson, D. R., **Cuthbert, A. S., & **Tynan, M. E. (2021). The neglect of idea diversity in idea generation and evaluation. Psychology of Aesthetics, Creativity, and the Arts, 15, 125-135.

** indicates a W&L undergraduate student