Investigating the contribution of ventral-lexical and dorsal-sublexical pathways during reading in bilinguals
Bibliographic record
Abstract
Several studies suggest the existence of ventral-lexical and dorsal-sublexical systems for reading. The relative contribution of these pathways can be manipulated by stimulus type and task demands. However, little is known about how bilinguals use these systems to read in their second language. In this study diffusion tensor imaging (DTI) was used to investigate the relationship between white matter (WM) integrity and reaction time in a group of 12 Chinese-English bilingual and 11 age-matched English monolingual adults. Considering a dual-route model of reading, the following four tracts were isolated in both the left and right hemispheres using a tractography measurement approach. Ventral tracts included the uncinate fasciculus (UF) and the inferior longitudinal fasciculus (ILF). The dorsal tracts of interest were the arcuate fasciculus (AF) and the superior longitudinal fasciculus (SLF). A significant correlation between the reaction time in a reading task and the mean diffusivity (MD) value was observed in the right UF in both bilingual and monolingual groups. Moreover, in the bilingual group we observed significantly more positive relationships between reaction time and MD in the right AF, and bilaterally in the SLF. We concluded that the relative contribution of the dorsal system for reading is greater in bilinguals than monolinguals. Further, these findings implicate a role of the right hemisphere in reading.
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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 | 0.001 | 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) | 0.001 | 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".