Functional MR imaging study of language-related differences in bilingual cerebellar activation.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Reports in the monolingual literature suggest that the cerebellum has an important role in language processing. The purpose of this study was to determine whether bilingual cerebellar functional MR imaging (fMRI) activation differs during the performance of comparable tasks in subjects' primary and secondary languages. METHODS: Eight bilingual, right-handed individuals underwent echo-planar fMRI at 1.5 T. They performed semantic (noun-verb association) and phonological (rhyming) tasks in Spanish (primary language) and English (secondary language). Individual and group functional datasets were analyzed using Statistical Parametric Mapping software (SPM99; P <.001 with a 10-voxel spatial extent threshold) and overlaid on T1-weighted anatomic images normalized to a standard (Montreal Neurologic Institute) space. Analysis of variance was performed on laterality indices derived from voxel counts in cerebellar regions of interest (ROIs). Subtraction of group-averaged normalized results from the combined Spanish tasks from the combined English tasks was also performed within SPM99 (P <.001 activation threshold). RESULTS: Significantly greater lateralilty indices were noted in the English tasks than in the Spanish tasks (mean Spanish LI, 0.3286; mean English LI, 0.5141 [P =.0143]). Overall, more robust activation was seen in the English tasks than in the Spanish tasks. Areas of significantly greater activation existed in the English tasks as compared with the Spanish tasks; these areas were more prominent in the left cerebellar hemisphere. CONCLUSION: Although both English and Spanish language tasks demonstrate left cerebellar dominance, English tasks demonstrate greater left hemispheric lateralization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".