Can Convict Cichlids (Amatitlania siquia) Socially Learn the Degree of Predation Risk Associated with Novel Visual Cues in Their Environment?
Bibliographic record
Abstract
For many animals, the ability to distinguish cues indicative of predation risk from cues unrelated to predation risk is not entirely innate, but rather is learned and improved with experience. Two pathways to such learning are possible. First, an animal could initially express antipredator behaviour toward a wide range of cues and subsequently learn which of those cues are non-threatening. Alternatively, it could initially express no antipredator behaviour toward a wide range of cues and subsequently learn which of them are threatening. While the learned recognition of threatening cues may occur either through personal interaction with a cue (asocial learning) or through observation of the behaviour of social companions toward a cue (social learning), the learned recognition of non-threatening cues seems to occur exclusively through habituation, a form of asocial learning. Here, we tested whether convict cichlid fish (Amatitlaniasiquia) can socially learn to recognize visual cues in their environment as either threatening or non-threatening. We exposed juvenile convict cichlids simultaneously to a novel visual cue and one of three (visual) social cues: a social cue indicative of non-risk (the sight of conspecifics that had previously been habituated to the novel cue), a social cue indicative of predation risk (the sight of conspecifics trained to fear the novel cue), or a control treatment with no social cue. The subsequent response of focal fish, when presented with the novel cue alone, was not influenced by the social cue that they had previously witnessed. We therefore did not find evidence that convict cichlids in our study could use social learning to recognize novel visual cues as either threatening or non-threatening. We consider alternative explanations for our findings.
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 | 0.001 | 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".