Is music autonomous from language? A neuropsy chological appraisal
Bibliographic record
Abstract
Department of Organismic and Evolutionary Biology, Harvard University, USA Isabelle Peretz Département de Psychologie, Université de Montréal, Canada INTRODUCTION Music and language are universal among humans, and both employ richly structured auditory and motor patterns. Since music and language are the two primary acoustic communicative systems of our species, their similarities and differences as cognitive domains have long interested scholars. (e.g. Aiello, 1994; Albert, Sparks & Helm, 1973; Besson, Faïta, & Requin, 1994; Bernstein, 1976; Blacking, 1976; Clarke, 1989; Darwin, 1871; Handel, 1989; Judd, Gardner & Geschwind, 1983; Lerdahl & Jackendoff, 1983; Levman, 1992; Nettl, 1956; Rousseau, 1761; Selkirk, 1984; Sergent, 1993; Sloboda, 1985; Sundberg & Lindblom, 1976; Sundberg, Nord & Carlson 1991; Trehub & Trainor, 1993). These contributions highlight the diversity of fields which have addressed this issue, from philosophy to the social, psychological, and biological sciences.
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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 | 0.000 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".