Nothing ventured, nothing gained: People anticipate more regret from missed romantic opportunities than from rejection
Bibliographic record
Abstract
Romantic pursuit decisions often require a person to risk one of the two errors: pursuing a romantic target when interest is not reciprocated (resulting in rejection) or failing to pursue a romantic target when interest is reciprocated (resulting in a missed romantic opportunity). In the present research, we examined how strongly people wish to avoid these two competing negative outcomes. When asked to recall a regrettable dating experience, participants were more than three times as likely to recall a missed opportunity rather than a rejection (Study 1). When presented with romantic pursuit dilemmas, participants perceived missed opportunities to be more regrettable than rejection (Studies 2–4), partially because they perceived missed opportunities to be more consequential to their lives (Studies 3 and 4). Participants were also more willing to risk rejection rather than missed romantic opportunities in the context of imagined (Study 4) and actual (Study 5) pursuit decisions. These effects generally extended even to less secure individuals (low self-esteem, high attachment anxiety). Overall, these studies suggest that motivation to avoid missed romantic opportunities may help to explain how people overcome fears of rejection in the pursuit of potential romantic partners.
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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 | 0.009 |
| 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 | 0.001 | 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".