Investigating the negative link between perfectionism and emotional divergent thinking
Bibliographic record
Abstract
Previous research has shown that perfectionism was negatively associated with the generation of original ideas in Divergent Thinking (DT) tasks, while striving for excellence was positively associated with it. However, the explanatory variables for these effects remain unclear. This study investigated the mediating roles of doubts about actions, concerns over mistakes, openness to experience, empathy, and emotions during DT tasks. Additionally, it examined an emotional DT task (i.e., naming frustrating things and things that affect one's self-esteem). From a sample of n = 282 university students, we replicated the negative association between perfectionism and DT abilities, though the effect size was smaller than in prior studies. Perfectionism correlated with lower empathy and greater primary negative emotions (e.g., fear) but similar openness to experience compared to excellencism. Mediation analyses revealed that doubts and concerns were unrelated to DT abilities. Openness to experience and empathy were positively correlated with DT abilities. Primary negative emotions during the tasks were negatively associated with the originality of answers. In contrast, positive emotions and secondary negative emotions (e.g., embarrassment) predicted more original ideas. These findings emphasize the importance of promoting excellencism over perfectionism to foster original ideas. This study has implications for overcoming barriers and supporting the creative process of individuals high on perfectionism. It also has implications for creativity researchers investigating the role of empathy and emotions in DT abilities. Supplementary Information: The online version contains supplementary material available at 10.1007/s44202-025-00330-x.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".