Time-dependent latent inhibition of predator-recognition learning
Bibliographic record
Abstract
To optimally manage an environment with predators, prey must correctly distinguish between cues that are risky and cues that are safe. Even a specific cue that is safe in one area or at a certain time may be dangerous in other situations, and vice versa. Latent inhibition is a cognitive mechanism by which animals fail to learn that a stimulus as risky because they have already learned it as non-threatening via previous encounters with the stimulus in the absence of negative consequences. Here, we demonstrate that latent inhibition of predator-recognition learning in wood frog tadpoles, Lithobates sylvaticus, depends on the timing of their learning opportunities. For 6 days, tadpoles were exposed daily to an initially novel stimulus (salamander odour), either in the morning (11.00-13.00 h) or evening (16.00-18.00 h). The following day, we conditioned tadpoles to recognize the salamander odour as a predator by pairing it with injured tadpole cues, either at the same time as their previous experience or at the opposite time. When tested the following day, latent inhibition occurred under each scenario where the timing of conditioning matched the timing of the pre-exposure. However, tadpoles tested in the morning showed learned fright responses when conditioned in the morning if their pre-exposure had occurred in the evening, whereas individuals tested in the evening showed learned fright responses when conditioned in the evening if their pre-exposure had occurred in the morning. This is the first report of time-dependent latent inhibition of predator-recognition learning, which is likely an important mechanism for correctly managing predation risk and safety.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".