Introductory gestures before songbird vocal displays are shaped by learning and biological predispositions
Bibliographic record
Abstract
Numerous animal displays begin with introductory gestures. For example, lizards start their head-bobbing displays with introductory push-ups, and many songbirds begin their vocal displays by repeating introductory notes (INs) before producing their learned song. Among songbirds, the acoustic structure and the number of INs produced before song vary considerably between individuals in a species. While similar variation in songs between individuals is a result of learning, whether variations in INs are also due to learning remains poorly understood. Here, using natural and experimental tutoring with male zebra finches, we show that mean IN number and IN acoustic structure are learned from a tutor. Interestingly, IN properties and how well INs were learned, were not correlated with the accuracy of song imitation and only weakly correlated with some features of songs that followed. Finally, birds artificially tutored with songs lacking INs still repeated vocalizations that resembled INs, before their songs, suggesting biological predispositions in IN production. These results demonstrate that INs, just like song elements, are shaped both by learning and biological predispositions. More generally, our results suggest mechanisms for generating variation in introductory gestures between individuals while still maintaining the species-specific structure of complex displays like birdsong.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".