Chapter 8. Effects of cognitive control, lexical robustness, and frequency of codeswitching on language switching
Bibliographic record
Abstract
This study explores the effects of individual differences on the production of words when switching between a strong and significantly weaker language. Variables of interest included non-linguistic cognitive control, lexical robustness (i.e., the size and strength of the lexicon), and frequency of codeswitching in daily life. Seventy university students who were English (L1) speakers learning Spanish (L2) and French (L3) completed a language questionnaire and participated in: a Simon task; lexical robustness measures in all three languages; and a picture-naming task involving cued language switching between the L1 and L2. The results suggested that cognitive control and L2 lexical robustness had modulating effects on language switching, but only in limited cases. L3 lexical robustness did not affect L1-L2 language switching, however, both L1 and L2 lexical robustness had differential influences, with smaller differences between L1 and L2 switch costs being related to higher levels of L2. Counterintuitively, participants who reported more frequently codeswitching in daily life showed larger switch costs in both L1 and L2. We discuss the implications for these findings and emphasize the importance of examining a more comprehensive spectrum of variables that explain how multilingual experiences shape the networks that support cognition and language regulatory processes.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".