The perfection of mimicry: an information approach
Bibliographic record
Abstract
We consider why imperfect deceptive mimics can persist when it appears to be in the predator's interest to discriminate finely between mimics and their models. One theory is that a receiver will accept being duped if the model and mimic overlap in appearance and the relative costs of attacking the model are high. However, a more fundamental explanation for the difficulty of discrimination is not based on perceptual uncertainty, but simply based on a lack of information. In particular, predators in the process of learning may cease sampling imperfect mimics entirely because the immediate pay-off and future value of information is low, allowing such mimics to persist. This outcome will be particularly likely when the model is relatively costly to attack and/or the discriminative rules the predator has to learn are complex. Information limitations neatly explain why predators tend to adopt discriminative rules based on single traits (such as stripe colour), rather than on combinations of traits (such as stripe order). They also explain why predators utilize certain salient discriminative traits while ignoring equally informative ones (a phenomenon known as overshadowing), and why imperfect mimics may be more common in phenotypically diverse prey communities.This article is part of the themed issue 'Animal coloration: production, perception, function and application'.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".