Tadpole antipredator responses change over time: what is the role of learning and generalization?
Bibliographic record
Abstract
Many prey animals quickly learn the identity and risk level associated with unknown predators and this provides them with an immediate survival advantage. However, as time passes the information they learned about the risk level associated with the predator decreases in value. As the certainty of the information decreases, there should be a point in time when the prey should stop responding to the information. Here, we conditioned tadpoles to recognize a tiger salamander, fire belly newt, or goldfish and found that they responded to each of the 3 predators with an equal response both 1 day and 8 days postconditioning. Subsequently, we reconditioned each of the groups to recognize tiger salamanders alone and found that the duration of time for which the tadpoles responded to the tiger salamander cue was influenced by what the tadpoles learned in the past. Tadpoles conditioned twice to the tiger salamander retained their response the longest, whereas tadpoles taught goldfish and then tiger salamander responded to the tiger salamander for the least time. Those learning the newt and then the tiger salamander retained their response to the tiger salamander for an intermediate amount of time, indicating that information gained through predator generalization influences the retention of responses to predators. Our results highlight the complex algorithm used by animals to acquire, encode, and use information from their environment.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".