Engaging proactive control: Influences of diverse language experiences using insights from machine learning.
Bibliographic record
Abstract
We used insights from machine learning to address an important but contentious question: Is bilingual language experience associated with executive control abilities? Specifically, we assess proactive executive control for over 400 young adult bilinguals via reaction time (RT) on an AX continuous performance task (AX-CPT). We measured bilingual experience as a continuous, multidimensional spectrum (i.e., age of acquisition, language entropy, and sheer second language exposure). Linear mixed effects regression analyses indicated significant associations between bilingual language experience and proactive control, consistent with previous work. Information criteria (e.g., AIC) and cross-validation further suggested that these models are robust in predicting data from novel, unmodeled participants. These results were bolstered by cross-validated LASSO regression, a form of penalized regression. However, the results of both cross-validation procedures also indicated that similar predictive performance could be achieved through simpler models that only included information about the AX-CPT (i.e., trial type). Collectively, these results suggest that the effects of bilingual experience on proactive control, to the extent that they exist in younger adults, are likely small. Thus, future studies will require even larger or qualitatively different samples (e.g., older adults or children) in combination with valid, granular quantifications of language experience to reveal predictive effects on novel participants. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".