Comparing Happiness and Hypomania Risk: A Study of Extraversion and Neuroticism Aspects
Bibliographic record
Abstract
Positive affect has long been considered a hallmark of subjective happiness. Yet, high levels of positive affect have also been linked with hypomania risk: a set of cognitive, affective, and behavioral characteristics that constitute a dispositional risk for future episodes of hypomania and mania. At a personality level, two powerful predictors of affective experience are extraversion and neuroticism: extraversion has been linked to positive affect, and neuroticism to negative affect. As such, a single personality trait--extraversion--has been linked to both beneficial and harmful outcomes associated with positivity. It is clear that positive affect, in different forms, has divergent consequences for well-being, but previous research has struggled to articulate the nature of these differences. We suggest that the relationship between affect and well-being needs to be situated within the psychological context of the individual--both in terms of more specific forms of extraversion and neuroticism, but also in terms of interactions among personality aspects. Consistent with this idea, we found that two aspects of extraversion (enthusiasm and assertiveness) differentially predicted subjective happiness from hypomania risk and two aspects of neuroticism (volatility and withdrawal) interacted to predict hypomania risk: the highest levels of hypomania risk were associated with the combination of high volatility and low withdrawal. These findings underscore the importance of examining personality at the right level of resolution to understand well-being and dysfunction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".