Do People Know How Their Romantic Partner Views Their Emotions? Evidence for Emotion Meta-Accuracy and Links with Momentary Romantic Relationship Quality
Bibliographic record
Abstract
Do people know how their romantic partner (i.e., the perceiver) views the self’s (i.e., the metaperceiver’s) emotions, displaying emotion meta-accuracy? Is it relevant to relationship quality? Using a sample of romantic couples ( N couples = 189), we found evidence for two types of emotion meta-accuracy across three different interactions: (a) normative emotion meta-accuracy , knowing perceivers’ impressions of metaperceivers’ emotions that are in line with how the average person may feel, and (b) distinctive emotion meta-accuracy , knowing perceivers’ unique impression of metaperceivers’ emotions. Furthermore, across interactions, normative emotion meta-accuracy was positively related to momentary relationship quality for metaperceivers and perceivers and this link was especially strong in the conflict interaction. Distinctive emotion meta-accuracy was negatively related to momentary relationship quality across interactions for perceivers and in the conflict interaction for metaperceivers. Overall, it may be adaptive for metaperceivers to accurately infer perceivers’ normative impressions and to remain blissfully unaware of their unique impressions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".