Negative Partner Attributions Moderate the Association between Heart Rate Reactivity During Relationship Conflict and Relationship Satisfaction
Bibliographic record
Abstract
Numerous theoretical models of relationship distress suggest that strong, negative reactions to conflict are directly associated with lower levels of relationship satisfaction. Consistent with this supposition, substantial evidence links higher levels of subjective negative emotion, more pronounced and frequent expressions of negative affect, and higher levels of negative communication behaviors to lower levels of relationship satisfaction (e.g., Bradbury, Fincham, & Beach, 2000, Journal of Marriage and Family, 62(4), 964). However, the evidence linking stress-related physiological responding during relationship conflict and relationship satisfaction is less compelling than would be anticipated based on theory. We propose that these theoretically unexpected but empirically well-replicated findings may be the result of different patterns in association between physiological reactivity and relationship satisfaction for couples with varying styles in how they typically perceive unwanted behavior in one another. The present study tests negative attributions for undesirable partner behaviors as a moderator of the association between heart rate reactivity (HRR) during relationship conflict and relationship satisfaction in a sample of 60 married couples. A significant interaction emerged between HRR and negative attributions of partner behavior in predicting relationship satisfaction such that higher levels of HRR were associated with lower levels of relationship satisfaction for individuals who typically made more negative attributions for undesirable partner behaviors, but with higher levels of relationship satisfaction for individuals who typically made fewer negative attributions for undesirable partner behaviors. Implications for conceptualizing reactivity during relationship conflict and couple interventions are discussed.
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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 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".