Word and nonword repetition in bilingual subjects: A PET study
Bibliographic record
Abstract
Learning a specific skill during childhood may partly determine the functional organization of the adult brain. This hypothesis led us to study brain activation patterns using positron emission tomography (PET), in which we compared word and nonword repetition in 10 right-handed native English-speakers (L1) who were proficient in their second language, French (L2), which was learned after the age of 5 years. Regional cerebral blood flow (rCBF) was measured by the H2 15O intravenous bolus method with intersubject averaging and coregistration of magnetic resonance and PET images. A comparison of CBF changes when repeating words in L2 with those seen when repeating words in (L1) demonstrated that the pattern of CBF was similar across the two conditions, with several significant CBF differences in the vicinity of the left insular cortex, ventral premotor region, and in the striatum. We hypothesize that these regions are activated when subjects are required to repeat known words, showing increased activity when there are increased articulatory demands imposed by speaking L2. Comparisons of nonword repetition in L1 and L2 revealed increased activity for L2 in the left ventral premotor region and in the cerebellum; rCBF increases were also observed in these regions in both L1 and L2 with increased number of syllables and increased articulatory complexity, suggesting a role for these regions in the complex motor control needed for the production of novel sequences.
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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 | 0.001 | 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".