Fluency-dependent cortical activation associated with speech production and comprehension in second language learners
Bibliographic record
Abstract
This functional magnetic resonance imaging (fMRI) study investigated the brain regions underlying language task performance in adult second language (L2) learners. Specifically, we identified brain regions where the level of activation was associated with L2 fluency levels. Thirty Japanese-speaking adults participated in the study. All participants were L2 learners of English and had achieved varying levels of fluency, as determined by a standardized L2 English proficiency test, the Versant English Test (Pearson Education Inc., 2011). When participants performed the oral sentence building task from the production tasks administered, the dorsal part of the left inferior frontal gyrus (dIFG) showed activation patterns that differed depending on the L2 fluency levels: The more fluent the participants were, the more dIFG activation decreased. This decreased activation of the dIFG might reflect the increased automaticity of a syntactic building process. In contrast, when participants performed an oral story comprehension task, the left posterior superior temporal gyrus (pSTG) showed increased activation with higher fluency levels. This suggests that the learners with higher L2 fluency were actively engaged in post-syntactic integration processing supported by the left pSTG. These data imply that L2 fluency predicts neural resource allocation during language comprehension tasks as well as in production tasks. This study sheds light on the neural underpinnings of L2 learning by identifying the brain regions recruited during different language tasks across different modalities (production vs. comprehension).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".