Perception of a tonal hierarchy derived from Korean music
Bibliographic record
Abstract
In two experiments, we assessed recovery of a tonal hierarchy in tone sequences. In Experiment 1, sequence tones were five tones of a Korean pentatonic scale plus seven nonscale tones located between scale tones. Sequences included all 12 tones, randomly ordered. Duration of scale tones in each sequence corresponded to the total duration of each tone in a piece of Korean music, as quantified by U. Nam (1998). Nonscale tones were shorter than scale tones. Listeners were either familiar or unfamiliar with the style of Korean music. Sequences were played 12 times, each time followed by 1 of 12 probe tones that had occurred in the sequence. Participants rated goodness-of-fit of the probe tone to the sequence. Ratings by both groups reflected the Korean tonal hierarchy including the relative salience of scale tones. Experiment 2 followed the same method and tones, but duration was assigned to tones quasi-randomly so that duration did not emphasize intervallic relationships in the Korean scale. Ratings differentiated long and short tones, but showed no other clear organization among long tones. Differences in results between experiments suggest that duration helps listeners organize pitch structure only when duration emphasizes intervallic relationships such as the near-perfect fifth.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".