Dan Johnson Associate Professor of Cognitive and Behavioral Science

Dan Johnson

Parmly Hall 234
Curriculum Vitae


Ph.D. - University of Oklahoma (2009), Psychology
M.S. - University of Oklahoma (2005)
B.A. - Luther College (2003), Psychology and Biology


The Computational Cognition and Creativity Lab uses computational models and empirical data to investigate the mechanisms underlying creativity processes like the generation and selection of novel ideas. We employ diverse methodologies like latent semantic analysis and subjective response coding using inter-rater reliability metrics. We use the statistical and graphics platform, R, for modeling and data work. Other topics we explore include metacognition and the role of reading narrative fiction in the development of empathy, theory of mind, and inferential abilities.


  • PSYC 112: Cognition
  • PSYC 114: Social Psychology
  • PSYC 118: Psychology Mythbusters
  • PSYC 120: Quantitative Literacy in the Behavioral Sciences
  • PSYC 250: Research Design and Analysis Lecture and Lab
  • PSYC 259: Cognition and Emotion
  • PSYC 359: Advanced Methods in Cognition and Emotion Research

Selected Publications

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

**Heinen, D. J., & Johnson, D. R. (2018). Semantic distance: An automated measure of creativity that is novel and appropriate. Psychology of Aesthetics, Creativity, and the Arts, 12, 144-156.

Johnson, D. R., **Tynan, M. E., **Cuthbert, A. S., & **Q’Quinn, J. K. (2018). Metacognition in argument generation: The misperceived relationship between emotional investment and argument quality. Cognition and Emotion, 32, 566-578.

Gavaler, C., & Johnson, D. R. (2017). The genre effect: A science fiction (vs. realism) manipulation decreases inference effort, reading comprehension, and perception of literary merit. Scientific Study of Literature, 7, 79-108.

Data Science Links

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.