Understanding the Links Between Perceiving Gratitude and Romantic Relationship Satisfaction Using an Accuracy and Bias Framework
Bibliographic record
Abstract
Perceiving a partner’s gratitude has several benefits for romantic relationships. We aimed to better understand these associations by decomposing perceptions into accuracy and bias. Specifically, we examined whether accuracy and bias in perceiving a partner’s experience (Study 1: N dyads = 205) and expression (Study 2: N dyads = 309) of gratitude were associated with romantic relationship satisfaction. Using the Truth and Bias Model of Judgment, we found that perceivers generally underestimated their partner’s gratitude, and lower perceptions of gratitude were related to lower perceiver satisfaction. Perceivers reported greater satisfaction when they assumed their partner’s gratitude was similar to their own. Partners reported greater satisfaction when perceivers accurately gauged their partners’ gratitude experience (but not expression) and lower satisfaction when perceivers underestimated their gratitude expression (but not experience). Overall, by decomposing gratitude perceptions into accuracy and bias, we provide insight into how these components differentially relate to relationship satisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".