Selective and divided attention modulates auditory–vocal integration in the processing of pitch feedback errors
Bibliographic record
Abstract
Speakers rapidly adjust their ongoing vocal productions to compensate for errors they hear in their auditory feedback. It is currently unclear what role attention plays in these vocal compensations. This event-related potential (ERP) study examined the influence of selective and divided attention on the vocal and cortical responses to pitch errors heard in auditory feedback regarding ongoing vocalisations. During the production of a sustained vowel, participants briefly heard their vocal pitch shifted up two semitones while they actively attended to auditory or visual events (selective attention), or both auditory and visual events (divided attention), or were not told to attend to either modality (control condition). The behavioral results showed that attending to the pitch perturbations elicited larger vocal compensations than attending to the visual stimuli. Moreover, ERPs were likewise sensitive to the attentional manipulations: P2 responses to pitch perturbations were larger when participants attended to the auditory stimuli compared to when they attended to the visual stimuli, and compared to when they were not explicitly told to attend to either the visual or auditory stimuli. By contrast, dividing attention between the auditory and visual modalities caused suppressed P2 responses relative to all the other conditions and caused enhanced N1 responses relative to the control condition. These findings provide strong evidence for the influence of attention on the mechanisms underlying the auditory-vocal integration in the processing of pitch feedback errors. In addition, selective attention and divided attention appear to modulate the neurobehavioral processing of pitch feedback errors in different ways.
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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 | 0.001 |
| 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".