An Experimental Test of Jealousy's Evolved Function: Imagined Partner Infidelity Induces Jealousy, Which Predicts Positive Attitude Towards Mate Retention
Bibliographic record
Abstract
Jealousy may have evolved to motivate adaptive compensatory behavior in response to threats to a valued relationship. This suggests that jealousy follows a temporal sequence: A perceived relational threat induces state feelings of jealousy which in turn motivates compensatory behavior, such as mate retention effort. Yet to date, tests of this mediation model have been limited to cross-sectional data. This study is the first to experimentally test this theoretical model. Men and women ( N = 222) who were currently in committed romantic relationships were primed with an imagined partner infidelity (versus control) scenario. Participants then completed measures of state jealousy and intended mate retention behavior. Results found that those primed with the infidelity threat scenario experienced an increase in state jealousy, which in turn predicted more intended benefit-provisioning and cost-inflicting mate retention. Findings suggest that jealousy mediated the relationship between infidelity threat and intended mate retention behavior, supporting the evolutionary account of state jealousy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".