Multicomponent deceptive signals reduce the speed at which predators learn that prey are profitable
Bibliographic record
Abstract
Many prey use multicomponent deceptive signals to fool predators into mistaking them for inedible objects, toxic prey, or dangerous animals. However, recent experiments have suggested that multicomponent deceptive signals are no more effective in deterring predators than single-component signals, making it difficult to understand how they have evolved. Here, we use an established experimental system in which naive domestic chicks are presented with models of snake-mimicking caterpillars to test the idea that multicomponent deceptive signals reduce the speed at which predators learn that prey are profitable. We presented chicks with a series of 4 trials in which they encountered a single type of caterpillar model. The type of model differed among our 4 experimental groups that were arranged in a 2×2 factorial design: models either possessed eyespots or did not and were in either the resting or defensive posture. Chicks’ responses to the same model prey were then retested following an extended 72-h retention period. Chicks rapidly attacked prey with no defensive traits and initially showed similar levels of wariness to prey with either 1 or 2 deceptive traits. However, chicks learned that single-trait caterpillars were profitable more quickly than 2-trait caterpillars and retained their learned responses better. This suggests that prey with multicomponent deceptive signals may have a selective advantage over prey with single-component deceptive signals when predators repeatedly encounter such prey.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".