Changes in executive function over time in bilingual and monolingual school-aged children.
Bibliographic record
Abstract
We examined the development of 3 executive function (EF) components-inhibition, updating, and task shifting-over time in monolingual and bilingual school-age children. We tested 41 monolingual and 41 simultaneous bilingual typically developing children (ages 8-12) on nonverbal tasks measuring inhibition (the Flanker task), updating (the Corsi blocks task), and task shifting (the Dimensional Change Card Sort task; DCCS) at 2 time points, 1 year apart. Three indexes of task shifting (shifting, switching, and mixing costs) were derived from the DCCS task. The 2 groups did not differ in their development of updating, but did demonstrate distinct patterns of development for inhibition. Specifically, while the bilingual group demonstrated a steep improvement in inhibition from Year 1 to Year 2, the monolingual group was characterized by stable inhibition performance over this time period. The 2 groups did not differ in their developmental patterns for shifting and switching costs, but for mixing costs, the bilingual children outperformed the monolingual children in both years. Together, the findings indicate that bilingual experience may modulate the developmental rates of some components of EF but not others, resulting in specific EF performance differences between bilinguals and monolinguals only at certain developmental time points. (PsycINFO Database Record
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".