Generalization of learned predator recognition: an experimental test and framework for future studies
Bibliographic record
Abstract
While some prey species possess an innate recognition of their predators, others require learning to recognize their predators. The specific characteristics of the predators that prey learn and whether prey can generalize this learning to similar predatory threats have been virtually ignored. Here, we investigated whether fathead minnows that learned to chemically recognize a specific predator species as a threat has the ability to generalize their recognition to closely related predators. We found that minnows trained to recognize the odour of a lake trout as a threat (the reference predator) generalized their responses to brook trout (same genus as lake trout) and rainbow trout (same family), but did not generalize to a distantly related predatory pike or non-predatory suckers. We also found that the intensity of antipredator responses to the other species was correlated with the phylogenetic distance to the reference predator; minnows responded with a higher intensity response to brook trout than rainbow trout. This is the first study showing that prey have the ability to exhibit generalization of predator odour recognition. We discuss these results and provide a theoretical framework for future studies of generalization of predator recognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".