Trust and biased memory of transgressions in romantic relationships.
Bibliographic record
Abstract
Relative to people with low trust in their romantic partner, people with high trust tend to expect that their partner will act in accordance with their interests. Consequently, we suggest, they have the luxury of remembering the past in a way that prioritizes relationship dependence over self-protection. In particular, they tend to exhibit relationship-promoting memory biases regarding transgressions the partner had enacted in the past. In contrast, at the other end of the spectrum, people with low trust in their partner tend to be uncertain about whether their partner will act in accordance with their interests. Consequently, we suggest, they feel compelled to remember the past in a way that prioritizes self-protection over relationship dependence. In particular, they tend to exhibit self-protective memory biases regarding transgressions the partner had enacted in the past. Four longitudinal studies of participants involved in established dating relationships or fledgling romantic relationships demonstrated that the greater a person's trust in their partner, the more positively they tend to remember the number, severity, and consequentiality of their partner's past transgressions-controlling for their initial reports. Such trust-inspired memory bias was partner-specific; it was more reliably evident for recall of the partner's transgressions and forgiveness than for recall of one's own transgressions and forgiveness. Furthermore, neither trust-inspired memory bias nor its partner-specific nature was attributable to potential confounds such as relationship commitment, relationship satisfaction, self-esteem, or attachment orientations.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".