Soothing the Threatened Brain: Leveraging Contact Comfort with Emotionally Focused Therapy
Bibliographic record
Abstract
Social relationships are tightly linked to health and well-being. Recent work suggests that social relationships can even serve vital emotion regulation functions by minimizing threat-related neural activity. But relationship distress remains a significant public health problem in North America and elsewhere. A promising approach to helping couples both resolve relationship distress and nurture effective interpersonal functioning is Emotionally Focused Therapy for couples (EFT), a manualized, empirically supported therapy that is strongly focused on repairing adult attachment bonds. We sought to examine a neural index of social emotion regulation as a potential mediator of the effects of EFT. Specifically, we examined the effectiveness of EFT for modifying the social regulation of neural threat responding using an fMRI-based handholding procedure. Results suggest that EFT altered the brain's representation of threat cues in the presence of a romantic partner. EFT-related changes during stranger handholding were also observed, but stranger effects were dependent upon self-reported relationship quality. EFT also appeared to increase threat-related brain activity in regions associated with self-regulation during the no-handholding condition. These findings provide a critical window into the regulatory mechanisms of close relationships in general and EFT in particular.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".