Auditory-Motor Control of Vocal Production during Divided Attention: Behavioral and ERP Correlates
Bibliographic record
Abstract
When people hear unexpected perturbations in auditory feedback, they produce rapid compensatory adjustments of their vocal behavior. Recent evidence has shown enhanced vocal compensations and cortical event-related potentials (ERPs) in response to attended pitch feedback perturbations, suggesting that this reflex-like behavior is influenced by selective attention. Less is known, however, about auditory-motor integration for voice control during divided attention. The present cross-modal study investigated the behavioral and ERP correlates of auditory feedback control of vocal pitch production during divided attention. During the production of sustained vowels, 32 young adults were instructed to simultaneously attend to both pitch feedback perturbations they heard and flashing red lights they saw. The presentation rate of the visual stimuli was varied to produce a low, intermediate, and high attentional load. The behavioral results showed that the low-load condition elicited significantly smaller vocal compensations for pitch perturbations than the intermediate-load and high-load conditions. As well, the cortical processing of vocal pitch feedback was also modulated as a function of divided attention. When compared to the low-load and intermediate-load conditions, the high-load condition elicited significantly larger N1 responses and smaller P2 responses to pitch perturbations. These findings provide the first neurobehavioral evidence that divided attention can modulate auditory feedback control of vocal pitch production.
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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) | 0.001 | 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".