Bats learn about potential food sources from others: a review
Bibliographic record
Abstract
Compared to trial-and-error learning, learning from others is often a viable means of adding new adaptive behaviours to an individual’s repertoire. This is especially true in long-lived, group-living species that encounter moderate levels of environmental heterogeneity. Here we review the social learning literature that uses bats as models under the framework of Galef (2009) and Laland (2009) to examine when, where, and from whom bats are most likely to learn socially about food and other foraging behaviour. We conclude that evidence exists for bats learning about novel foods from other bats, learning how to handle such food from other bats, and that bats often learn these ways when uncertain about the quality of different foods available. There is also evidence that young bats learn about new foods from their mothers, and that adult bats learn from other adult bats, even other bat species. However, whether bats more likely to learn from familiar individuals or learn about specific foraging areas from others is less established and warrants further research. We also conclude that phyllostomid bats present the best evidence of social learning about food and suggest future research, including investigating the possibility of nonhuman culture, focus on this diverse group.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".