The motor origins of human and avian song structure
Bibliographic record
Abstract
Human song exhibits great structural diversity, yet certain aspects of melodic shape (how pitch is patterned over time) are widespread. These include a predominance of arch-shaped and descending melodic contours in musical phrases, a tendency for phrase-final notes to be relatively long, and a bias toward small pitch movements between adjacent notes in a melody [Huron D (2006) Sweet Anticipation: Music and the Psychology of Expectation (MIT Press, Cambridge, MA)]. What is the origin of these features? We hypothesize that they stem from motor constraints on song production (i.e., the energetic efficiency of their underlying motor actions) rather than being innately specified. One prediction of this hypothesis is that any animals subject to similar motor constraints on song will exhibit similar melodic shapes, no matter how distantly related those animals are to humans. Conversely, animals who do not share similar motor constraints on song will not exhibit convergent melodic shapes. Birds provide an ideal case for testing these predictions, because their peripheral mechanisms of song production have both notable similarities and differences from human vocal mechanisms [Riede T, Goller F (2010) Brain Lang 115:69-80]. We use these similarities and differences to make specific predictions about shared and distinct features of human and avian song structure and find that these predictions are confirmed by empirical analysis of diverse human and avian song samples.
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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) | 0.001 | 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".