Individual differences in human frequency-following response predict pitch labeling ability
Bibliographic record
Abstract
The frequency-following response (FFR) provides a measure of phase-locked auditory encoding in humans and has been used to study subcortical processing in the auditory system. While effects of experience on the FFR have been reported, few studies have examined whether individual differences in early sensory encoding have measurable effects on human performance. Absolute pitch (AP), the rare ability to label musical notes without reference notes, provides an excellent model system for testing how early neural encoding supports specialized auditory skills. Results show that the FFR predicts pitch labelling performance better than traditional measures related to AP (age of music onset, tonal language experience, pitch adjustment and just-noticeable-difference scores). Moreover, the stimulus type used to elicit the FFR (tones or speech) impacts predictive performance in a manner that is consistent with prior research. Additionally, the FFR predicts labelling performance for piano tones better than unfamiliar sine tones. Taken together, the FFR reliably distinguishes individuals based on their explicit pitch labeling abilities, which highlights the complex dynamics between sensory processing and cognition.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".