Background Predation Risk and Learned Predator Recognition in Convict Cichlids: Does Risk Allocation Constrain Learning?
Bibliographic record
Abstract
Abstract Exposure to elevated levels of background predation risk is known to shape the behavioural response of prey organisms to known and unknown predation threats. However, less is known regarding the effects of background predation risk on predator recognition learning. Here, we test the potential effects of elevated background predation risk on the strength and retention of learned predator recognition in juvenile convict cichlids ( Amatitlania nigrofasciata ). In a series of laboratory trials, we exposed shoals of juvenile cichlids to conditions of elevated (vs. low) levels of background risk and then conditioned them to recognize a novel predator odour (rainbow trout, Oncorhynchus mykiss ). The results of our first experiment demonstrate that despite showing reduced response intensities during initial conditioning (due to risk allocation), conditioned cichlids from high vs. low background risk show similar intensities of learned recognition when tested 24 h post‐conditioning. Moreover, elevated levels of background risk induced a predator avoidance response among unconditioned cichlids (due to induced neophobia). Our second experiment demonstrates that while we find no difference in the strength of learning when tested 24 h post‐conditioning, retention of acquired recognition is enhanced among cichlids from the high background predation risk treatment. Together, our results highlight the complex interacting effects past experience plays in shaping the response to acute predation threats.
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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 | 0.000 |
| 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".