Attentional Demands Influence Vocal Compensations to Pitch Errors Heard in Auditory Feedback
Bibliographic record
Abstract
Auditory feedback is required to maintain fluent speech. At present, it is unclear how attention modulates auditory feedback processing during ongoing speech. In this event-related potential (ERP) study, participants vocalized/a/, while they heard their vocal pitch suddenly shifted downward a ½ semitone in both single and dual-task conditions. During the single-task condition participants passively viewed a visual stream for cues to start and stop vocalizing. In the dual-task condition, participants vocalized while they identified target stimuli in a visual stream of letters. The presentation rate of the visual stimuli was manipulated in the dual-task condition in order to produce a low, intermediate, and high attentional load. Visual target identification accuracy was lowest in the high attentional load condition, indicating that attentional load was successfully manipulated. Results further showed that participants who were exposed to the single-task condition, prior to the dual-task condition, produced larger vocal compensations during the single-task condition. Thus, when participants' attention was divided, less attention was available for the monitoring of their auditory feedback, resulting in smaller compensatory vocal responses. However, P1-N1-P2 ERP responses were not affected by divided attention, suggesting that the effect of attentional load was not on the auditory processing of pitch altered feedback, but instead it interfered with the integration of auditory and motor information, or motor control itself.
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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) | 0.002 | 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".