To “See” Is to Feel Grateful? A Quasi-Signal Detection Analysis of Romantic Partners’ Sacrifices
Bibliographic record
Abstract
Although gratitude plays a central role in the quality of relationships, little is known about how gratitude emerges, such as in response to partners’ sacrifices. Do people need to accurately see these acts to feel grateful? In two daily experience studies of romantic couples (total N = 426), we used a quasi-signal detection paradigm to examine the prevalence and consequences of (in)accurately “seeing” and missing partners’ sacrifices. Findings consistently showed that sacrifices are equally likely to be missed as they are to be accurately detected, and about half of the time people “see” a sacrifice when the partner declares none. Importantly, “seeing” partners’ sacrifices—accurately or inaccurately—is crucial for boosting gratitude. In contrast, missed sacrifices fail to elicit gratitude, and the lack of appreciation negatively colors the partner’s satisfaction with the relationship when having sacrificed. Thus, these findings illustrate the power that perception holds in romantic couples’ daily lives.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".