The socially mediated recovery of a fearful fish paired with periodically replaced calm models
Bibliographic record
Abstract
Social learning is an important mechanism for acquiring knowledge about environmental risk. However, little work has explored the learning of safety and how such learning outcomes are shaped by the social environment. Here, we exposed minnows,Pimephales promelas, to a high-risk environment to induce behavioural responses associated with fear (e.g. neophobia). We then used the presence of calm conspecific models (low-risk individuals) to weaken these responses. When observers (individuals from the high-risk environment) and models were paired consistently in a one-on-one setting, the observers showed no recovery (i.e. no weakening of the fear responses), and instead the models indirectly acquired those responses (i.e. a socially transmitted state of fear). However, observers paired with models that were periodically replaced with new calm models showed a significant recovery, and each new model showed diminished socially transmitted fear. We argue that our understanding of predation-related fear and social information transfer can prove fruitful in understanding problems with fear and stress across animal taxa, including among humans who experience post-traumatic stress and secondary trauma. Our findings indicate that the periodic replacement of models can promote fear recovery in observers and reduce socially transmitted fear in models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".