The sensitivity of auditory-motor representations to subtle changes in auditory feedback while singing
Bibliographic record
Abstract
Singing requires accurate control of the fundamental frequency (F0) of the voice. This study examined trained singers' and untrained singers' (nonsingers') sensitivity to subtle manipulations in auditory feedback and the subsequent effect on the mapping between F0 feedback and vocal control. Participants produced the consonant-vowel /ta/ while receiving auditory feedback that was shifted up and down in frequency. Results showed that singers and nonsingers compensated to a similar degree when presented with frequency-altered feedback (FAF); however, singers' F0 values were consistently closer to the intended pitch target. Moreover, singers initiated their compensatory responses when auditory feedback was shifted up or down 6 cents or more, compared to nonsingers who began compensating when feedback was shifted up 26 cents and down 22 cents. Additionally, examination of the first 50 ms of vocalization indicated that participants commenced subsequent vocal utterances, during FAF, near the F0 value on previous shift trials. Interestingly, nonsingers commenced F0 productions below the pitch target and increased their F0 until they matched the note. Thus, singers and nonsingers rely on an internal model to regulate voice F0, but singers' models appear to be more sensitive in response to subtle discrepancies in auditory feedback.
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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 | 0.004 |
| 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) | 0.001 | 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".