Do you believe <scp>Red Bull</scp> gives wings? When implicit theories of creativity impair creative performance
Bibliographic record
Abstract
Everyone seems to have something to say about creativity, thus participating in the reproduction of persistent myths about creativity that may influence creative behaviour. This research explores the influence of Laypeople's Implicit Theories of Creativity (LIToCs) regarding the drivers of creativity on creative performance, to ascertain whether having strong convictions about the drivers of creativity either enhances or hinders creative productivity when these convictions align with the actual methods of stimulating creativity. An experiment randomly involved 69 subjects who were invited to drink the exact same fruit juice before performing a creative task. In one condition, they were told this was indeed juice; in the other condition, they were told that it was mixed with Red Bull. Analyses showed an interaction effect with the subjects' LIToC, such that among subjects displaying strong LIToC, individual creative performance was lower when they perceived the conditions to stimulate creativity were activated, than otherwise. These results suggest that having strong beliefs in the effects of some creativity drivers might then trigger a complacent attitude and reduce the invested effort in generating creative ideas. This research contributes to rethinking how we use specific drivers to stimulate creativity, as strong LIToCs about those drivers may have a counterproductive effect on creative performance.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".