Interhemispheric functional brain connectivity predicts new language learning success in adults
Bibliographic record
Abstract
Investigating interhemispheric interactions between homologous cortical regions during language processing is of interest. Despite prevalent left hemisphere lateralization of language, the right hemisphere also plays an important role and interhemispheric connectivity is influenced by language experience and is implicated in second language (L2) acquisition. Regions involved in language processing have differential connectivity to other cortical regions and to each other, and play specific roles in language. We examined the interhemispheric interactions of subregions of the inferior frontal gyrus (areas 44 and 45), the adjacent area 9/46v in the middle frontal gyrus, the superior temporal gyrus (STG), and the posterior inferior parietal lobule (pIPL) in relation to distinct and specific aspects of L2 learning success. The results indicated that the connectivity between left and right areas 44 and 9/46v predicted improvement in sentence repetition, connectivity between left and right area 45 and mid-STG predicted improvement in auditory comprehension, and connectivity between left and right pIPL predicted improvement in reading speed. We show interhemispheric interactions in the specific context of facilitating performance in adult L2 acquisition that follow an anterior to posterior gradient in the brain, and are consistent with the respective roles of these regions in language 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) | 0.002 | 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".