Perception of Chord Sequences Modeled with Prediction by Partial Matching, Voice-Leading Distance, and Spectral Pitch-Class Similarity: A New Approach for Testing Individual Differences in Harmony Perception
Why this work is in the frame
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Bibliographic record
Abstract
The perception of harmony has been the subject of many studies in the research literature, though little is known regarding how individuals vary in their ability to discriminate between different chord sequences. The aim of the current study was to construct an individual-differences test for the processing of harmonic information. A stimulus database of 5076 harmonic sequences was constructed and several harmonic features were computed from these stimulus items. Participants were tasked with selecting which chord differed between two similar four-chord sequences, and their response data were modeled with explanatory item response models using the computational harmonic features as predictors. The final model suggests that participants’ responses can be modeled using transitional probabilities between chords, voice-leading distance, and spectral pitch-class distance cues, with participant ability correlated to three subscales from Goldsmiths Musical Sophistication Index. The item response model was used to create an adaptive test of harmonic progression discrimination ability (HPT) and validated in a second study showing substantial correlations with other tests of musical perception ability, self-reported musical abilities, and a working memory task. The HPT is a new free and open-source tool for assessing individual differences in harmonic sequence discrimination. Initial data suggest this harmonic discrimination ability relies heavily on transitional probabilities within harmonic progressions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it