The origins of music in auditory scene analysis and the roles of evolution and culture in musical creation
Bibliographic record
Abstract
Whether music was an evolutionary adaptation that conferred survival advantages or a cultural creation has generated much debate. Consistent with an evolutionary hypothesis, music is unique to humans, emerges early in development and is universal across societies. However, the adaptive benefit of music is far from obvious. Music is highly flexible, generative and changes rapidly over time, consistent with a cultural creation hypothesis. In this paper, it is proposed that much of musical pitch and timing structure adapted to preexisting features of auditory processing that evolved for auditory scene analysis (ASA). Thus, music may have emerged initially as a cultural creation made possible by preexisting adaptations for ASA. However, some aspects of music, such as its emotional and social power, may have subsequently proved beneficial for survival and led to adaptations that enhanced musical behaviour. Ontogenetic and phylogenetic evidence is considered in this regard. In particular, enhanced auditory-motor pathways in humans that enable movement entrainment to music and consequent increases in social cohesion, and pathways enabling music to affect reward centres in the brain should be investigated as possible musical adaptations. It is concluded that the origins of music are complex and probably involved exaptation, cultural creation and evolutionary adaptation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".