Induced emotion counter-regulation affects attentional inhibition of emotional information: ERP evidence from a randomized manipulation approach
Bibliographic record
Abstract
Automatic emotion counter-regulation refers to an unintentional attentional shift away from the current emotional state and toward information of the opposite valence. It is a useful emotion regulation skill that prevents the escalation of current emotional state. However, the cognitive mechanisms of emotion counter-regulation are not fully understood. Using a randomization approach, this study investigated how automatic emotion counter-regulation impacted attentional inhibition of emotional stimuli, an important aspect of emotion processing closely associated with emotion regulation and mental health. Forty-six university students were randomly assigned to an emotion counter-regulation group and a control group. The former group watched an anger-inducing video to evoke automatic emotion counter-regulation of anger, while the latter group watched an emotionally neutral video. Next, both groups completed a negative priming task of facial expressions with EEG recorded. In the emotion counter-regulation group, we observed an enhanced attentional inhibition of the angry, but not happy, faces, as indicated by a prolonger response time, a larger N2, and a smaller P3 in response to angry versus happy stimuli. These patterns were not observed in the control group, supporting the role of elicited emotion counter-regulation of anger in causing these modulation patterns in responses.
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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 | 0.003 |
| 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) | 0.002 | 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".