The Contributions of Creative Cognition and Schizotypal Symptoms to Creative Achievement
Bibliographic record
Abstract
Although previous research has suggested that people with subclinical levels of schizophrenic symptoms achieve a greater number of creative accomplishments, the contention that there is a creative cognitive advantage in schizotypy has received mixed support. It was hypothesized that accounting for complex relationships between (a) creative cognition abilities (moderated relationships), and (b) creative cognition and schizotypy variables (mediated, moderated, and curvilinear relationships) would significantly increase the ability to predict creative performance and provide a more accurate survey of the schizotypic creative cognitive advantage. One hundred and fourteen participants completed a creative problem solving measure, measures of cognitive creative abilities (Remote Associates Test, a divergent thinking task, and a deductive reasoning task) and measures of positive and negative symptoms of schizotypy. Regression analyses supported the conception of a multistage process in which creative cognition variables interact with each other to predict performance on a creative problem solving task. There was no evidence of a creative cognitive advantage in schizotypy: People high in schizotypy performing the same or worse than people reporting few schizotypic symptoms on measures of creative cognition and creative problem solving performance. If people with schizotypy are, indeed, more creative than those without, it is because of factors other than the cognitive processes surveyed in this investigation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".