Bilingual Language Experience and Its Effect on Conflict Adaptation in Reactive Inhibitory Control Tasks
Bibliographic record
Abstract
We used machine-learning techniques to assess interactions between language and cognitive systems related to inhibitory control and conflict adaptation in reactive control tasks. We built theoretically driven candidate models of Simon and Number Stroop task data ( N = 777 adult bilinguals ages 18–43 years living in Montréal, Canada) that differed in whether bilingual experience interacted with inhibitory control, including two forms of conflict adaptation: shorter term sequential congruency effects and longer term trial order effects. Models with continuous aspects of bilingual experience provided signal in predicting new, unmodeled data. Specifically, mixed language usage predicted trial order adaptation to conflict. This effect was restricted to Number Stroop, which overtly involves linguistic or symbolic information and relatively higher language- and response-related uncertainty. These results suggest that bilingual experience adaptively tunes aspects of the control system and offers a novel integrative modeling approach that can be used to pursue other complex individual difference questions within the psychological sciences.
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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 | 0.008 |
| 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) | 0.002 | 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".