Neuroticism may not reflect emotional variability
Bibliographic record
Abstract
Neuroticism is one of the major traits describing human personality, and a predictor of mental and physical disorders with profound public health significance. Individual differences in emotional variability are thought to reflect the core of neuroticism. However, the empirical relation between emotional variability and neuroticism may be partially the result of a measurement artifact reflecting neuroticism’s relation with higher mean levels—rather than greater variability—of negative emotion. When emotional intensity is measured using bounded scales, there is a dependency between variability and mean levels: at low (or high) intensity, it is impossible to demonstrate high variability. As neuroticism is positively associated with mean levels of negative emotion, this may account for the relation between neuroticism and emotional variability. In a metaanalysis of 11 studies ( N = 1,205 participants; 83,411 observations), we tested whether the association between neuroticism and negative emotional variability was clouded by a dependency between variability and the mean. We found a medium-sized positive association between neuroticism and negative emotional variability, but, when using a relative variability index to correct for mean negative emotion, this association disappeared. This indicated that neuroticism was associated with experiencing more intense, but not more variable, negative emotions. Our findings call into question theory, measurement scales, and data suggesting that emotional variability is central to neuroticism. In doing so, they provide a revisionary perspective for understanding how this individual difference may predispose to mental and physical disorders.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".