Proficiency in positive vs. negative emotion identification and subjective well-being among long-term married elderly couples
Bibliographic record
Abstract
Evidence is accruing that positive emotions play a crucial role in shaping a healthy interpersonal climate. Inspired by this research, the current investigation sought to shed light on the link between proficiency in identifying positive vs. negative emotions and a close partner's well-being. To this end, we conducted two studies with neurologically intact elderly married couples (Study 1) and an age-matched clinical sample, comprising married couples in which one spouse had been diagnosed with Parkinson's Disease (Study 2), which tends to hinder emotional expressivity. To assess proficiency in identifying emotions from whole body postures, we had participants in both studies complete a pointlight walker task, featuring four actors (two male, two female) expressing one positive (i.e., happiness) and three negative (i.e., sadness, anger, fear) basic emotions. Participants also filled out measures of subjective well-being. Among Study 1's neurologically intact spouses, greater expertise in identifying positive (but not negative) emotions was linked to greater partner life satisfaction (but not hedonic balance). Spouses of PD patients exhibited increased proficiency in identifying positive emotions relative to controls, possibly reflective of compensatory mechanisms. Complementarily, relative to controls, spouses of PD patients exhibited reduced proficiency in identifying negative emotions and a tendency to underestimate their intensity. Importantly, all of these effects attenuated with longer years from PD onset. Finally, there was evidence that it was increased partner expertise in identifying negative (rather than positive) emotional states that predicted greater life satisfaction levels among the PD patients and their spouses. Our results thus suggest that positive vs. negative emotions may play distinct roles in close relationship dynamics as a function of neurological status and disability trajectory.
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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".