Bibliographic record
Abstract
Introduction. Previous research has found associations between creativity (or semantic association), schizotypy, and laterality when each of the three pairings has been studied individually, leading to three relatively distinct bodies of literature. Methods. This study attempted to integrate previous research by providing measures of all three constructs in a within subjects correlational design. Participants were 30 undergraduate students who completed four measures of creativity, three schizotypy scales, and a lateralised lexical decision task. Signal detection theory (SDT) was used to analyse the laterality data. Results. Normal individuals with relatively lower SDT response criteria for stimuli presented to the left visual field/right hemisphere had higher schizotypy scores and higher performance on a verbal creativity test. Conclusions. These results extend previous findings by using SDT analyses to show for the first time that individuals scoring higher on certain schizotypy and creativity tests exhibited differences in response criteria to more readily accept right hemisphere responses, rather than exhibiting hemispheric differences in sensitivity (ability). The findings accord with theories proposing that higher schizotypy and creativity may partly arise from a lowering of criteria for evidence and/or from a shift to reliance on processing strategies that are more dependent on the right cerebral hemisphere.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".