Costs of Learning and the Evolution of Mimetic Signals
Bibliographic record
Abstract
Predators must use the appearance of their prey to decide whether it is likely to be defended. Most theory assumes that predators have completed learning about prey appearance, yet we do not understand how predators learn which aspects of appearance to use for classifying prey. If sampling prey can be risky, predators might forgo opportunities to learn about the relationship between prey appearance and defense. Using Bayesian inference and dynamic programming, we modeled how the immediate risks and future rewards of learning about prey appearance influence how predators learn. In addition, we explored how variation in predator learning affects the evolution of mimicry, which occurs when two prey evolve to share a common signal to predators. We found that when learning about prey with distinct appearances was expensive, optimal predators tended to lump them into the same category or exhibit an unwillingness to sample at all (neophobia). This resulted in a reduction in selection for defensive mimicry. However, the same predator behavior favored the evolution of aggressive mimicry, because in that case, mimics benefited from being sampled. When prey were very rare and costs of sampling them were high, predators exhibited neophobia, refusing to attack. This behavior could forestall the evolution of mimicry and instead select for polymorphism.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".