Phonological and morphological literacy skills in English and Chinese: A cross‐linguistic neuroimaging comparison of Chinese–English bilingual and monolingual English children
Bibliographic record
Abstract
Over the course of literacy development, children learn to recognize word sounds and meanings in print. Yet, they do so differently across alphabetic and character-based orthographies such as English and Chinese. To uncover cross-linguistic influences on children's literacy, we asked young Chinese-English simultaneous bilinguals and English monolinguals (N = 119, ages 5-10) to complete phonological and morphological awareness (MA) literacy tasks. Children completed the tasks in the auditory modality in each of their languages during functional near-infrared spectroscopy neuroimaging. Cross-linguistically, comparisons between bilinguals' two languages revealed that the task that was more central to reading in a given orthography, such as phonological awareness (PA) in English and MA in Chinese, elicited less activation in the left inferior frontal and parietal regions. Group comparisons between bilinguals and monolinguals in English, their shared language of academic instruction, revealed that the left inferior frontal was less active during phonology but more active during morphology in bilinguals relative to monolinguals. MA skills are generally considered to have greater language specificity than PA skills. Bilingual literacy training in a skill that is maximally similar across languages, such as PA, may therefore yield greater automaticity for this skill, as reflected in the lower activation in bilinguals relative to monolinguals. This interpretation is supported by negative correlations between proficiency and brain activation. Together, these findings suggest that both the structural characteristics and literacy experiences with a given language can exert specific influences on bilingual and monolingual children's emerging brain networks for learning to read.
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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".