Emotion down-regulation diminishes cognitive control: A neurophysiological investigation.
Bibliographic record
Abstract
Traditional models of cognitive control have explained performance monitoring as a "cold" cognitive process, devoid of emotion. In contrast to this dominant view, a growing body of clinical and experimental research indicates that cognitive control and its neural substrates, in particular the error-related negativity (ERN), are moderated by affective and motivational factors, reflecting the aversive experience of response conflict and errors. To add to this growing line of research, here we use the classic emotion regulation paradigm-a manipulation that promotes the cognitive reappraisal of emotion during task performance-to test the extent to which affective variation in the ERN is subject to emotion reappraisal, and also to explore how emotional regulation of the ERN might influence behavioral performance. In a within-subjects design, 41 university students completed 3 identical rounds of a go/no-go task while electroencephalography was recorded. Reappraisal instructions were manipulated so that participants either down-regulated or up-regulated emotional involvement, or completed the task normally, without engaging any reappraisal strategy (control). Results showed attenuated ERN amplitudes when participants down-regulated their emotional experience. In addition, a mediation analysis revealed that the association between reappraisal style and attenuated ERN was mediated by changes in reported emotion ratings. An indirect effects model also revealed that down-regulation predicted sensitivity of error-monitoring processes (difference ERN), which, in turn, predicted poorer task performance. Taken together, these results suggest that the ERN appears to have a strong affective component that is associated with indices of cognitive control and behavioral monitoring.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".