Bibliographic record
Abstract
In Western tonal music, pitches are organized hierarchically based on their perceived fit in a specific tonal context. This hierarchy forms scales that are commonly used in Western tonal music. The hierarchical nature of tonal structure is well established behaviourally; however, the neural underpinnings are largely unknown. In this study, EEG data and goodness-of-fit ratings were collected from 34 participants who listened to an arpeggio followed by a probe tone, where the probe tone could be any chromatic scale degree and the context any of the major keys. Goodness-of-fit ratings corresponded to the classic tonal hierarchy. N1, P2 and the Early Right Anterior Negativity (ERAN) were significantly modulated by scale degree. Furthermore, neural marker amplitudes and latencies were significantly correlated with similar magnitude to both pitch height and goodness-of-fit ratings. This is different from the clearer divide between pitch height correlating with early neural markers (100-200 ms) and tonal hierarchy correlating with late neural markers (200-1000 ms) reported by Sankaran et al. (2020) and Quiroga-Martinez et al. (2019). Finally, individual differences were greater than any main effects detected when pooling participants and brain-behavior correlations vary widely (i.e. r = -0.8 to 0.8).
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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 | 0.001 |
| 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 | 0.000 |
| 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".