Love Lost in Translation: Avoidant Individuals Inaccurately Perceive Their Partners’ Positive Emotions During Love Conversations
Bibliographic record
Abstract
Empathic accuracy—the ability to decipher others’ thoughts and feelings—promotes relationship satisfaction. Those high in attachment avoidance tend to be less empathically accurate; however, past research has been limited to relatively negative or neutral contexts. We extend work on attachment and empathic accuracy to the positive context of love. To do so, we combined data from three dyadic studies ( N = 303 dyads) in which couple members shared a time of love and rated each other’s positive emotions. Using the Truth and Bias Model of Judgment, we found that individuals higher (vs. lower) in attachment avoidance were less accurate in inferring their partners’ positive emotions during the conversation, but did not systematically over- or under-perceive their partners’ positive emotions. Our results suggest that avoidant individuals may be less sensitive to positive cues in their relationships, potentially reducing relational intimacy.
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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 | 0.006 |
| 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 | 0.002 | 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".