Left motor cortex contributes to auditory phonological discrimination
Bibliographic record
Abstract
Evidence suggests that the articulatory motor system contributes to speech perception in a context-dependent manner. This study tested 2 hypotheses using magnetoencephalography: (i) the motor cortex is involved in phonological processing, and (ii) it aids in compensating for speech-in-noise challenges. A total of 32 young adults performed a phonological discrimination task under 3 noise conditions while their brain activity was recorded using magnetoencephalography. We observed simultaneous activation in the left ventral primary motor cortex and bilateral posterior-superior temporal gyrus when participants correctly identified pairs of syllables. This activation was significantly more pronounced for phonologically different than identical syllable pairs. Notably, phonological differences were resolved more quickly in the left ventral primary motor cortex than in the left posterior-superior temporal gyrus. Conversely, the noise level did not modulate the activity in frontal motor regions and the involvement of the left ventral primary motor cortex in phonological discrimination was comparable across all noise conditions. Our results show that the ventral primary motor cortex is crucial for phonological processing but not for compensation in challenging listening conditions. Simultaneous activation of left ventral primary motor cortex and bilateral posterior-superior temporal gyrus supports an interactive model of speech perception, where auditory and motor regions shape perception. The ventral primary motor cortex may be involved in a predictive coding mechanism that influences auditory-phonetic processing.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".