Vocal learning and flexible rhythm pattern perception are linked: Evidence from songbirds
Bibliographic record
Abstract
Rhythm perception is fundamental to speech and music. Humans readily recognize a rhythmic pattern, such as that of a familiar song, independently of the tempo at which it occurs. This shows that our perception of auditory rhythms is flexible, relying on global relational patterns more than on the absolute durations of specific time intervals. Given that auditory rhythm perception in humans engages a complex auditory-motor cortical network even in the absence of movement and that the evolution of vocal learning is accompanied by strengthening of forebrain auditory-motor pathways, we hypothesize that vocal learning species share our perceptual facility for relational rhythm processing. We test this by asking whether the best-studied animal model for vocal learning, the zebra finch, can recognize a fundamental rhythmic pattern-equal timing between event onsets (isochrony)-based on temporal relations between intervals rather than on absolute durations. Prior work suggests that vocal nonlearners (pigeons and rats) are quite limited in this regard and are biased to attend to absolute durations when listening to rhythmic sequences. In contrast, using naturalistic sounds at multiple stimulus rates, we show that male zebra finches robustly recognize isochrony independent of absolute time intervals, even at rates distant from those used in training. Our findings highlight the importance of comparative studies of rhythmic processing and suggest that vocal learning species are promising animal models for key aspects of human rhythm perception. Such models are needed to understand the neural mechanisms behind the positive effect of rhythm on certain speech and movement 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".