EFFECTS ON POPULATION DIVERGENCE OF WITHIN-GENERATIONAL LEARNING ABOUT PROSPECTIVE MATES
Bibliographic record
Abstract
Although learned mate preferences are suspected to have important effects during speciation, theoretical models have largely neglected the effects on speciation and population divergence of within-generational learning, that is, learning based upon prior experience with potential mates. Here, we use population genetic models to address this deficit. Focusing on the situation of secondary contact between populations that still hybridize, we consider models of learning by females and by males under polygyny. We assess the effects of learning to prefer conspecifics from previous conspecific encounters, learning to avoid heterospecifics from previous heterospecific encounters, and learning to prefer familiar types. We examine the amount of population divergence that results from learning in these models. We also assess the effect of learning on the spread of an allele that strengthens assortative mating in both models. We find that learning can have counterintuitive, but logical and understandable effects that differ with the version of the model assessed. In general, population divergence is expected to increase most consistently when females learn to strengthen their preferences for conspecifics from previous encounters with conspecifics. Our results also suggest that within-generational learning will generally inhibit the spread of alleles strengthening assortative mating.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".