State Rumination: Associations with Emotional Stress Reactivity and Attention Biases
Bibliographic record
Abstract
Within dysphoria, rumination has been identified as a particularly maladaptive emotion regulation strategy linked to prolonged negative affect and the onset of depressive episodes. Until now, the majority of research assessing naturally occurring rumination has utilized trait rumination measures; however, additional information may be obtained by assessing state rumination. The current study examined the association between state rumination and participants' emotional recovery from stress. In addition, biased attention toward emotional information was investigated as a mechanism that might underlie state rumination. Participants completed the exogenous cuing task to assess attentional engagement and disengagement from emotional facial expressions followed by a psychosocial stressor. State rumination and self-reported sadness were measured during the recovery period. As expected, state rumination was associated with less recovery in sadness scores, even after controlling for trait rumination and depressive symptoms. Moreover, within the high dysphoria group, participants who had more difficulty disengaging from emotional expressions reported higher levels of rumination in response to the stressor. Results highlight an important association between state rumination and individuals' recovery from stress, and suggest that difficulty disengaging attention from emotional expressions might be one mechanism underlying state rumination in dysphoria.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".