Wise Reasoning Benefits from Emodiversity, Irrespective of Emotional Intensity
Bibliographic record
Abstract
The role of emotions in wise reasoning is not well understood. On the one hand, work on emotional regulation suggests downregulating intense emotions may lead to wiser reasoning. On the other hand, emerging work suggests recognizing and balancing emotions provides critical insights into life experiences, suggesting an alternative path to wiser reasoning. We present a series of observational, diary, and experimental studies (N = 3,678) addressing these possibilities, examining how wisdom-related characteristics of reasoning—epistemic humility, recognition of a world in flux/change, self-transcendence, recognition of diverse perspectives on an issue, search for integration of diverse perspectives/compromise—relate to emotional intensity and to emodiversity (i.e., emotional richness and evenness) in a given situation. Across five studies—testing wisdom nominees and examining individual differences and manipulated wise reasoning, it appeared in conjunction with emodiversity, independent of downregulated emotional intensity. The positive association between emodiversity and wisdom-related characteristics occurred consistently for daily challenges, unresolved interpersonal conflicts, as well as political conflicts. The relationship between emotional intensity and wisdom-related characteristics was less systematic, with some studies suggesting a positive (rather than negative) association between emotional intensity and wisdom. Together, these results demonstrate that wise reasoning does not necessarily require uniform emotional downregulation. Instead, wise reasoning can also benefit from a rich and balanced emotional life.
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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 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".