Moody and thin‐skinned? The interplay of neuroticism and momentary affect in older romantic couples
Bibliographic record
Abstract
Neuroticism is associated with heightened reactivity to social stressors. However, little is known about the micro-processes through which neuroticism shapes - and is shaped by - affective experiences in close relationships. We examine the extent to which momentary affect is coupled with one's relationship partner, whether the strength of this coupling differs depending on levels of neuroticism, and whether this coupling and partner's overall level of positive or negative affect prospectively contribute to differential (rank-order) changes in neuroticism. Older couples (N = 82, aged 67-93 years) rated their momentary affect six times per day for one week and provided ratings of trait neuroticism at baseline and 18 months later. Multilevel models revealed that among individuals high in neuroticism, individual positive affect was more closely coupled with partner positive affect compared with individuals low in neuroticism. Moreover, neuroticism decreased over time in those participants who showed a higher degree of coupling with partner positive affect and also had a partner with higher overall positive affect. In contrast, neuroticism increased in individuals whose partner had lower overall positive affect. Similar effects were not observed for negative affect. Our findings highlight how relationship partners contribute to daily affective experiences and longer-term changes in neuroticism.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".