Language Experience Shapes Neural Grouping of Speech by Accent: EEG Evidence from Native, L2, and Heritage Listeners
Bibliographic record
Abstract
Accented speech contains talker-indexical cues that listeners can use to infer social group membership, yet it remains unclear how the auditory system categorizes accent variability and how this process depends on language experience. The current study used EEG and the MMN oddball paradigm to test pre-attentive neural sensitivity to accent changes of English words stopped produced by Canadian English or Mandarin Chinese accented English talkers. Three participant groups were tested: Native English listeners, L1-Mandarin listeners, and Heritage Mandarin listeners. In the Native English and L1-Mandarin groups, we observed MMNs to the Canadian accented English deviant, indicating that the brain can group speech by accent despite substantive inter-talker variation and is consistent with an experience-dependence sensitivity to accent. Exposure to Mandarin Chinese accented English modulated MMN magnitude. Time-frequency analyses suggested that α and low-β power during accent encoding varied with language background, with Native English listeners showing stronger activity when presented with Mandarin Chinese accented English. Finally, the neurophysiological response in the Heritage Mandarin group reflected a broader phonological space encompassing both Canadian English and Mandarin-accented English, and its magnitude was predicted by Chinese proficiency. These findings provide brain-based evidence that automatic accent categorization is not uniform across listeners but interacts with native phonology and second-language experience.
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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) | 0.002 | 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".