A Double-Edged Sword? Unpacking the Effects of Rumination on Emotional Clarity
Bibliographic record
Abstract
Rumination, or thinking passively and repetitively about one’s distress, and low emotional clarity, or not understanding one’s emotions, are risk factors for psychopathology. It has been suggested that people attempt to increase emotional clarity by ruminating, but whether ruminating works to help or harm emotional clarity in the moment is unknown. In N = 74 adults, following an idiographic negative mood induction, we experimentally manipulated rumination and two comparison conditions – distraction and mindfulness – to assess their effects on negative emotion, subjective and implicit indices of emotional clarity, and self-insight. Manipulation checks showed that conditions produced a pattern of distinct experiences theoretically consistent with each response style. Compared to comparison conditions, rumination was less effective in alleviating negative emotion. However, all conditions produced similar effects on emotional clarity and self-insight. Whereas each condition failed to influence subjective emotional clarity, they increased implicit clarity and perceived self-insight. Results underscore the importance of incorporating multiple measures of emotional clarity and suggest that, compared to other cognitive emotion response styles, rumination may function as a double-edged sword that keeps one entrenched in negative emotion but without impairing implicit emotional clarity and self-insight. Findings may have implications for why people ruminate despite its negative impact on well-being.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".