Temporally dissociable neural representations of pitch height and chroma
Bibliographic record
Abstract
The extraction and analysis of pitch underpin speech and music recognition, sound segregation, and other auditory tasks. Perceptually, pitch can be represented as a helix composed of two factors: height monotonically aligns with frequency, while chroma cyclically repeats at doubled frequencies. Although the early perceptual and neurophysiological mechanisms for extracting pitch from acoustic signals have been extensively investigated, the equally essential subsequent stages that bridge to high-level auditory cognition remain less well understood. How does the brain represent perceptual attributes of pitch at higher-order processing stages and how are the neural representations formed over time? We used a machine learning approach to decode time-resolved neural responses of human listeners (10 females and 7 males) measured by magnetoencephalography across different pitches, hypothesizing that different pitches sharing similar neural representations would result in reduced decoding performance. We show that pitch can be decoded from lower-frequency neural responses within auditory-frontal cortical regions. Specifically, linear mixed-effect modeling reveals that height and chroma explain decoding performance of delta band (0.5-4 Hz) neural activity at distinct latencies: a long-lasting height effect precedes a transient chroma effect, followed by a recurrence of height after chroma, indicating sequential processing stages associated with unique perceptual and neural characteristics. Furthermore, the localization analyses of the decoder demonstrate that height and chroma are associated with overlapping cortical regions, with differences observed in the right orbital and polar frontal cortex. The data provide a perspective motivating new hypotheses on the mechanisms of pitch representation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".