Pair-Bonding, Romantic Love, and Evolution
Bibliographic record
Abstract
This article evaluates a thesis containing three interconnected propositions. First, romantic love is a "commitment device" for motivating pair-bonding in humans. Second, pair-bonding facilitated the idiosyncratic life history of hominins, helping to provide the massive investment required to rear children. Third, managing long-term pair bonds (along with family relationships) facilitated the evolution of social intelligence and cooperative skills. We evaluate this thesis by integrating evidence from a broad range of scientific disciplines. First, consistent with the claim that romantic love is an evolved commitment device, our review suggests that it is universal; suppresses mate-search mechanisms; has specific behavioral, hormonal, and neuropsychological signatures; and is linked to better health and survival. Second, we consider challenges to this thesis posed by the existence of arranged marriage, polygyny, divorce, and infidelity. Third, we show how the intimate relationship mind seems to be built to regulate and monitor relationships. Fourth, we review comparative evidence concerning links among mating systems, reproductive biology, and brain size. Finally, we discuss evidence regarding the evolutionary timing of shifts to pair-bonding in hominins. We conclude there is interdisciplinary support for the claim that romantic love and pair-bonding, along with alloparenting, played critical roles in the evolution of Homo sapiens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".