Social learning within and across species: information transfer in mouse-eared bats
Bibliographic record
Abstract
Social learning describes information transfer between individuals through observation or direct interaction. Bats can live and forage in large groups, sometimes comprising several species, and are thus well suited for investigations of both intraspecific and interspecific information transfer. Although social learning has been documented within several bat species, it has not been shown to occur between species. Furthermore, it is not fully understood what level of interaction between individuals is necessary for social learning in bats. We address these questions by comparing the efficiency of observation versus interaction in intraspecific social learning and by considering interspecific social learning in sympatric bat species. Observers learned from demonstrators to identify food sources using a light cue. We show that intraspecific social learning exists in the greater mouse-eared bat (Myotis myotis (Borkhausen, 1797)) and that direct interaction with a demonstrator more efficiently leads to information transfer than observational learning alone. We also found evidence for interspecific information transfer from M. myotis to the lesser mouse-eared bat (Myotis oxygnathus Monticelli, 1885). Additionally, we opportunistically retested one individual that we recaptured from the wild 1 year after initial learning and found long-term memory of the trained association. Our study adds to the understanding of learning, information transfer, and long-term memory in wild-living animals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".