Speech motor brain regions are differentially recruited during perception of native and foreign-accented phonemes for first and second language listeners
Bibliographic record
Abstract
Brain imaging studies indicate that speech motor areas are recruited for auditory speech perception, especially when intelligibility is low due to environmental noise or when speech is accented. The purpose of the present study was to determine the relative contribution of brain regions to the processing of speech containing phonetic categories from one's own language, speech with accented samples of one's native phonetic categories, and speech with unfamiliar phonetic categories. To that end, native English and Japanese speakers identified the speech sounds /r/ and /l/ that were produced by native English speakers (unaccented) and Japanese speakers (foreign-accented) while functional magnetic resonance imaging measured their brain activity. For native English speakers, the Japanese accented speech was more difficult to categorize than the unaccented English speech. In contrast, Japanese speakers have difficulty distinguishing between /r/ and /l/, so both the Japanese accented and English unaccented speech were difficult to categorize. Brain regions involved with listening to foreign-accented productions of a first language included primarily the right cerebellum, left ventral inferior premotor cortex PMvi, and Broca's area. Brain regions most involved with listening to a second-language phonetic contrast (foreign-accented and unaccented productions) also included the left PMvi and the right cerebellum. Additionally, increased activity was observed in the right PMvi, the left and right ventral superior premotor cortex PMvs, and the left cerebellum. These results support a role for speech motor regions during the perception of foreign-accented native speech and for perception of difficult second-language phonetic contrasts.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".