Serial memory error patterns in bilinguals and monolinguals
Bibliographic record
Abstract
Research shows that bilinguals tend to outperform monolinguals on certain cognitive tasks. While these advantages are typically attributed to differences in executive function, serial memory and syntactic processing may also help account for the differences observed between these populations. Bilinguals have been shown to outperform monolinguals on a variety of memory recall tasks, but it remains unclear whether these findings generalize to tasks involving serial memory. To answer this question, a digit recall task was administered to 11 monolingual and 11 bilingual participants. An algorithm was built to analyze the digit recall data and examine the mechanism underpinning the differences in memory performance in bilingual and monolingual participants. The algorithm found bilinguals making significantly fewer transpositional errors than monolinguals in the recall task (p < .005). One follow-up question pertained to the role of L1 syntactic processing as a possible predictor of serial memory error patterns. To test this question, a sentence recall task was administered to the same sample after completing the digit recall task. This task tested if bilingual exposure could predict syntactic memory error patterns. A Pearson's Chi-squared test found null results, leaving the transpositional error findings open for interpretation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".