Better Together: Momentary Physiological Benefits of Co-Experienced Positive Emotions in Late Life
Bibliographic record
Abstract
Abstract Although positive emotions tend to be experienced most frequently and intensely within social interactions, most research has focused on positive emotions at the individual level. Yet, emerging research shows that shared positive emotions predict health and longevity over and above individually experienced positive emotions. However, the extent to which relationship partners co-experience positive emotions in daily life remains poorly understood. Moreover, whether positive emotions “get under the skin,” shaping proximal biomarkers in everyday life, is unclear. Drawing on Positivity Resonance Theory, we examined the links between co-experienced positive emotions and momentary cortisol levels in 321 older couples (ages 56–89). Findings showed that both relationship partners reported higher-than-usual positive emotions during 38% of the occasions they were together. Additionally, co-experienced positive emotions were linked to lower cortisol levels at the same occasion, adjusting for individually experienced positive emotions and several additional individual-level and time-varying covariates. Findings did not differ across age, sex, or relationship satisfaction, suggesting that the physiological benefits of co-experienced positive emotions are not dependent on individual or relationship characteristics. Notably, co-experienced positive emotions were also associated with lower cortisol at the subsequent assessment (but not vice versa), indicating that the benefits of co-experienced positive emotions extend beyond the immediate moment. This study highlights the important role that shared positive emotions play in promoting physiological health in older adults, providing evidence that co-experienced emotions offer benefits beyond individual experiences.
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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 | 0.001 |
| 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".