Noise differentially impacts phoneme representations in the auditory and speech motor systems
Bibliographic record
Abstract
Although it is well accepted that the speech motor system (SMS) is activated during speech perception, the functional role of this activation remains unclear. Here we test the hypothesis that the redundant motor activation contributes to categorical speech perception under adverse listening conditions. In this functional magnetic resonance imaging study, participants identified one of four phoneme tokens (/ba/, /ma/, /da/, or /ta/) under one of six signal-to-noise ratio (SNR) levels (-12, -9, -6, -2, 8 dB, and no noise). Univariate and multivariate pattern analyses were used to determine the role of the SMS during perception of noise-impoverished phonemes. Results revealed a negative correlation between neural activity and perceptual accuracy in the left ventral premotor cortex and Broca's area. More importantly, multivoxel patterns of activity in the left ventral premotor cortex and Broca's area exhibited effective phoneme categorization when SNR ≥ -6 dB. This is in sharp contrast with phoneme discriminability in bilateral auditory cortices and sensorimotor interface areas (e.g., left posterior superior temporal gyrus), which was reliable only when the noise was extremely weak (SNR > 8 dB). Our findings provide strong neuroimaging evidence for a greater robustness of the SMS than auditory regions for categorical speech perception in noise. Under adverse listening conditions, better discriminative activity in the SMS may compensate for loss of specificity in the auditory system via sensorimotor integration.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".