Pitch chroma information is processed in addition to pitch height information with more than two pitch-range categories
Bibliographic record
Abstract
Octave equivalence describes the perception that notes separated by a doubling in frequency sound similar. While the octave is used cross-culturally as a basis of pitch perception, experimental demonstration of the phenomenon has proved to be difficult. In past work, members of our group developed a three-range generalization paradigm that reliably demonstrated octave equivalence. In this study we replicate and expand on this previous work trying to answer three questions that help us understand the origins and potential cross-cultural significance of octave equivalence: (1) whether training with three ranges is strictly necessary or whether an easier-to-learn two-range task would be sufficient, (2) whether the task could demonstrate octave equivalence beyond neighbouring octaves, and (3) whether language skills and musical education impact the use of octave equivalence in this task. We conducted a large-sample study using variations of the original paradigm to answer these questions. Results found here suggest that the three-range discrimination task is indeed vital to demonstrating octave equivalence. In a two-range task, pitch height appears to be dominant over octave equivalence. Octave equivalence has an effect only when pitch height alone is not sufficient. Results also suggest that effects of octave equivalence are strongest between neighbouring octaves, and that tonal language and musical training have a positive effect on learning of discriminations but not on perception of octave equivalence during testing. We discuss these results considering their relevance to future research and to ongoing debates about the basis of octave equivalence perception.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".