Bidialectalism and Bilingualism: Exploring the Role of Language Similarity as a Link Between Linguistic Ability and Executive Control
Bibliographic record
Abstract
The notion of bilingual advantages in executive functions (EF) is based on the assumption that the demands posed by cross-language interference serve as EF training. These training effects should be more pronounced the more cross-language interference bilinguals have to overcome when managing their two languages. In the present study, we investigated the proposed link between linguistic and EF performance using the similarity between the two languages spoken since childhood as a proxy for different levels of cross-language interference. We assessed the effect of linearly increasing language dissimilarity on linguistic and EF performance in multiple tasks in four groups of young adults (aged 18-33): German monolinguals (n = 24), bidialectals (n = 25; German and Swiss German dialect), bilinguals speaking two languages of the same Indo-European ancestry (n = 24; e.g., German-English), or bilinguals speaking two languages of different ancestry (n = 24; e.g., German-Turkish). Bayesian linear-mixed effects modeling revealed substantial evidence for a linear effect of language similarity on linguistic accuracy, with better performance for participants with more similar languages and monolinguals. However, we did not obtain evidence for the presence of a similarity effect on EF performance. Furthermore, language experience did not modulate EF performance, even when testing the effect of continuous indicators of bilingualism (e.g., age of acquisition, proficiency, daily foreign language usage). These findings question the theoretical assumption that life-long experience in managing cross-language interference serves as EF training.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".