Recruitment of the speech motor system in adverse listening conditions
Bibliographic record
Abstract
Background noise is detrimental to speech comprehension. The decline-compensation hypothesis posits that deficits in sensory processing regions caused by background noise can be counteracted by compensatory recruitment of more general cognitive areas. We are exploring the role of the speech motor system as a compensatory mechanism during impoverished sensory representations in the auditory cortices. Prior studies using functional magnetic resonance imaging (fMRI) have revealed an increase in prefrontal activity when peripheral and central auditory systems cannot effectively process speech sounds. Our research using event-related fMRI revealed a negative correlation between brain activation and perceptual accuracy in speech motor regions (i.e., left ventral premotor cortex (PMv) and Broca’s area), suggesting a compensatory recruitment of the motor system during speech perception in noise. Moreover, multi-voxel pattern analysis revealed effective phoneme categorization in the PMv and Broca’s area, even in adverse listening conditions. This is in sharp contrast with phoneme discriminability in auditory cortices and left posterior superior temporal gyrus, which showed reliable phoneme classification only when the noise was extremely weak. Better discriminative activity in the speech motor system may compensate for the loss of specificity in the auditory system by forward sensorimotor mapping during adverse listening conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".