Linking predator risk and uncertainty to adaptive forgetting: a theoretical framework and empirical test using tadpoles
Bibliographic record
Abstract
Hundreds of studies have examined how prey animals assess their risk of predation. These studies work from the basic tennet that prey need to continually balance the conflicting demands of predator avoidance with activities such as foraging and reproduction. The information that animals gain regarding local predation risk is most often learned. Yet, the concept of 'memory' in the context of predation remains virtually unexplored. Here, our goal was (i) to determine if the memory window associated with predator recognition is fixed or flexible and, if it is flexible, (ii) to identify which factors affect the length of this window and in which ways. We performed an experiment on larval wood frogs, Rana sylvatica, to test whether the risk posed by, and the uncertainty associated with, the predator would affect the length of the tadpoles' memory window. We found that as the risk associated with the predator increases, tadpoles retained predator-related information for longer. Moreover, if the uncertainty about predator-related information increases, then prey use this information for a shorter period. We also present a theoretical framework aiming at highlighting both intrinsic and extrinsic factors that could affect the memory window of information use by prey individuals.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".