Positive illusions about dyadic perspective-taking as a moderator of the association between attachment insecurity and marital satisfaction
Bibliographic record
Abstract
Attachment insecurity (i.e., attachment anxiety or avoidance) puts people at risk for dissatisfying relationships. However, the dyadic regulation model of insecurity buffering suggests that an understanding and responsive partner may help insecure individuals to regulate emotions, thus improving couples' relationships. It may also be that perceiving partners as understanding and empathic, especially in an exaggeratedly positive way (i.e., positive illusions) will buffer insecurity. In 196 mixed-gender newlywed couples, we investigated whether spouses' positive illusions about partner's dyadic perspective-taking moderated the association between spouses' attachment insecurity and spouses' and partners' marital satisfaction over two years. Positive illusions generally predicted more satisfying relationships and attachment avoidance consistently predicted more dissatisfying relationships. There were also several instances where multilevel modeling indicated that positive illusions of dyadic perspective-taking buffered the negative effects of attachment avoidance on relationship satisfaction. However, there was also potentiation such that in two instances, positive illusions about dyadic perspective-taking strengthened the association between spouses' insecurity (husbands' attachment anxiety and wives' attachment avoidance) and subsequent marital dissatisfaction. In the moment, positive illusions about dyadic perspective-taking may allow spouses to feel happy in their relationship despite fear of emotional intimacy; however, positive illusions may not continue to buffer effects of insecurity on subsequent relationship satisfaction and may even be harmful in the face of insecurity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".