Dan Johnson David G. Elmes Term Professor of Cognitive and Behavioral Science
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.
- CBSC 112: Cognition
- CBSC 114: Social Psychology
- CBSC 118: Psychology Mythbusters
- CBSC 240: Introduction to Data Science: Mind Analytics
- CBSC 250: Statistics and Research Design
- CBSC 259: Cognition and Emotion
- CBSC 359: Advanced Methods in Computational Cognition
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
Data Science Links
Check out our Data Science Program at Washington and Lee
Want to learn how to program and do data science in R? Check out datacamp.
Want to learn Bayesian statistics? Check out John Kruschke's and Eric-Jan Wagenmaker's work.