Applying the neuroscience of creativity to creativity training
Bibliographic record
Abstract
This article investigates how neuroscience in general, and neuroscience of creativity in particular, can be used in teaching "applied creativity" and the usefulness of this approach to creativity training. The article is based on empirical data and our experiences from the Applied NeuroCreativity (ANC) program, taught at business schools in Denmark and Canada. In line with previous studies of successful creativity training programs the ANC participants are first introduced to cognitive concepts of creativity, before applying these concepts to a relevant real world creative problem. The novelty in the ANC program is that the conceptualization of creativity is built on neuroscience, and a crucial aspect of the course is giving the students a thorough understanding of the neuroscience of creativity. Previous studies have reported that the conceptualization of creativity used in such training is of major importance for the success of the training, and we believe that the neuroscience of creativity offers a novel conceptualization for creativity training. Here we present pre/post-training tests showing that ANC students gained more fluency in divergent thinking (a traditional measure of trait creativity) than those in highly similar courses without the neuroscience component, suggesting that principles from neuroscience can contribute effectively to creativity training and produce measurable results on creativity tests. The evidence presented indicates that the inclusion of neuroscience principles in a creativity course can in 8 weeks increase divergent thinking skills with an individual relative average of 28.5%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".