Taking Stock of Reality: Biased Perceptions of the Costs of Romantic Partners’ Sacrifices
Bibliographic record
Abstract
When romantic partners sacrifice their own self-interest to benefit the relationship, the sacrificer or recipient may—for various reasons—be biased in how they perceive the costs that the sacrificer incurs. In Study 1, romantic couples ( N = 125) rated their own and their partner’s costs after a conversation about a sacrifice in the laboratory, followed by extensive experience sampling in their natural environment. In Study 2, a preregistered experiment, individuals ( N = 775) imagined a scenario in which they, their partner, or an unknown person sacrificed and rated the associated costs and benefits. Both studies demonstrated a consistent discrepancy between perceptions of own and partner sacrifice, driven primarily by people underestimating their own sacrifice costs and overestimating the benefits (Study 2). Results across studies showed that this underestimation bias helps people to feel better and feel more satisfied in the relationship when giving up their own goals and preferences for the relationship.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".