Digit span error patterns in bilinguals and monolinguals
Bibliographic record
Abstract
Research shows that bilinguals tend to outperform monolinguals on certain cognitive and linguistic tasks. While the mechanism underlying these advantages remains unclear, it has been suggested that bilinguals have enhanced working memory, which may be responsible for some of the cognitive advantages observed in these populations. To examine the mechanism responsible for the bilingual advantage, serial working memory was compared between monolingual and bilingual undergraduate students using an adaptive digit span test (n = 77). The results of the test were algorithmically adjusted in order to reveal not only if each digit was correct, but also the existence of serial errors and digit scrambling. The results showed that bilinguals made significantly fewer transpositional errors compared to monolinguals (p < 0.02, d = 0.61). These results are thought to be caused by differences in attention to serial information between bilinguals and monolinguals. Another possible explanation for these results is that bilinguals implement more cognitive memory techniques (like chunking), and are therefore better at retaining serial information. The computational methods developed in this experiment will help guide paths for further research on the impact of syntactical co-activation on serial memory recall, and primacy/recency effects.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".