Does compensation in auditory feedback require attention?
Bibliographic record
Abstract
When speakers receive auditory feedback with a real-time perturbation of formant structure, they hear themselves produce a vowel slightly different form the one intended. In response, they spontaneously change the formant structure to make the feedback more consistent with the intended sound. This compensatory behavior was reported to be automatic [Munhall et al. (2009)] because speakers are not able to suppress it even when they are informed about the perturbation and are instructed not to change their articulation. However, whether and to which extent attentional resources are utilized for this behavior have not been directly investigated. In the current study, speakers performed a speech production task where they pronounced a monosyllable whose formant structure was perturbed, while concurrently performing another task (i.e., dual-task paradigm). The preliminary results showed that, when attention was diverted to an unrelated auditory detection task, the magnitude of compensation remained the same as in the single task condition. Follow-up experiments will manipulate the nature and difficulty of the concurrent task to examine whether compensation in speech production is affected, and if so, what levels of the error feedback system are more susceptible to attentional manipulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".