Gender differences in cognitive and affective interpersonal emotion regulation in couples: an fNIRS hyperscanning
Bibliographic record
Abstract
Emotion regulation is vital in maintaining romantic relationships in couples. Although gender differences exist in cognitive and affective strategies during 'intrapersonal' emotion regulation, it is unclear how gender differences through affective bonds work in 'interpersonal' emotion regulation (IER) in couples. Thirty couple dyads and 30 stranger dyads underwent functional near-infrared spectroscopy hyperscanning recordings when targets complied with their partner's cognitive engagement (CE) and affective engagement (AE) strategies after viewing sad and neutral videos. Behaviorally, for males, CE was less effective than AE in both groups, but little difference occurred for females between AE and CE. For couples, Granger causality analysis showed that male targets had less neural activity than female targets in CH06, CH13 and CH17 during CE. For inflow and outflow activities on CH06 and CH13 (frontopolar cortex), respectively, male targets had less activity in the CE condition than in the AE condition, while for outflow activities on CH 17 (dorsolateral prefrontal cortex), female targets had more activity in the CE condition than in the AE condition. However, these differences were not observed in strangers. These results suggest gender differences in CE but not in AE and dissociable flow patterns in male and female targets in couples during sadness regulation.
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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) | 0.002 | 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".