Exploring the Creative Personality: Using Machine Learning to Predict Fluency and Originality in Divergent Thinking

ABSTRACT

In this study, 100 self-reported personality items from the Big Five Aspects Scale, responded to by a sample of 334 undergraduate participants, were used to predict quantity (ideational fluency) and quality (originality) of ideas on a divergent thinking (DT) task. The originality of DT responses was scored through a fine-tuned version of the Generative Pre-trained Transformer (GPT) 3.5 (i.e., Ocsai), and a least absolute shrinkage selection operator (LASSO) machine learning model selected the items that were meaningful predictors of each outcome. Results revealed that the personality profiles of highly fluent and highly original individuals were characterized by a tension between seemingly opposed personality attributes. Both ideational fluency and originality were predicted by a playfully open intellectualism that nonetheless avoided more typical work (i.e. was disorderly and unindustrious). Fluency was additionally predicted by a tension between enthusiasm for social interaction and depressive symptoms associated with withdrawal. Originality was predicted by a socially dominant assertiveness that was tempered by awareness and care for others’ feelings (e.g. compassion and politeness) as well as stability (i.e. non-volatility). Taken together, these results demonstrate that the creative personality is likely to be composed of aspects of multiple dimensions of typical personality models like the Big 5, and that the highly fluent and the highly original creative personality is different in important ways.

Acknowledgments

The authors wish to thank Mark Leveling from the University of Denver for his contribution to data collection. The authors declare they have no conflicts of interest or funding to report for this project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. We also conducted a sensitivity check that explored the near-optimal solutions (i.e., at 5% more than the least MSPE), as a way to ensure that the composition of items selected, and the model predictions, remained suitably consistent, before retaining the optimum solution for interpretation. The near-optimal solution retained 47 personality predictors in predicting originality, containing all 19 predictors in the optimal solution. Similarly, the near-optimal solution retained 33 predictors in predicting fluency, containing all 15 in the optimal solution. These results indicated the consistency between the optimal solution and those “nearby” the optimal, as well as the parsimony of the optimum.