Data from: Social learning in a high-risk environment: incomplete disregard for the ‘minnow that cried pike’ results in culturally transmitted neophobia
Bibliographic record
Abstract
Many prey species rely on conspecifics to gather information about unknown predation threats, but little is known about the role of varying environmental conditions on the efficacy of social learning. We examined predator-naive minnows that had the opportunity to learn about predators from experienced models that were raised in either a low- or high-risk environment. There were striking differences in behaviour among models; high-risk models showed a weaker response to the predator cue and became neophobic in response to the control cue (a novel odour, NO). Observers that were previously paired with low-risk models acquired a strong antipredator response only to the predator cue. However, observers that interacted with high-risk models, displayed a much weaker response to the predator odour and a weak neophobic response to the NO. This is the first study reporting such different outcomes of social learning under different environmental conditions, and suggests high-risk environments promote the cultural transmission of neophobia more so than social learning. If such a transfer can be considered similar to secondary traumatization in humans, culturally transmitted neophobia in minnows may provide a good model system for understanding more about the social ecology of fear disorders.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.019 |
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".