Tracking the emergence of a pitch hierarchy using an artificial grammar requires extended exposure
Bibliographic record
Abstract
Introduction The tonal hierarchy is a perceived musical structure implicitly learned through exposure. Previous studies have demonstrated that new grammars, for example based on the Bohlen-Pierce scale, can be learned in as little as 20 minutes. Methods In this study, we created two grammars derived from the Bohlen-Pierce scale similar in complexity to the western tonal hierarchy. Participants rated the goodness-of-fit of all Bohlen-Pierce scale notes in a probe tone paradigm before and after 30 minutes of exposure to one of the two grammars. Participants were then asked about their experience in a short interview. Results Results do not support the learning of the artificial grammar: correlations between goodness-of-fit ratings and pitch frequency distribution of a grammar were no different before and after exposure to a grammar. Interviews suggest that participants are bad at identifying the strategy they used to complete the task. Testing the strategies reported on the data revealed that ratings decreased with increasing distance of the probe tone from the tonic. Discussion This is consistent with early brain responses to chromatic pitches of the tonal hierarchy. We suggest that longer exposure time is necessary to learn more complex grammars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".