Digits Across Languages: The Role of Lexical and Numerical Characteristics in Digit Span Performance Across Fourteen Languages
Bibliographic record
Abstract
BACKGROUND: The digit span task, a measure of auditory verbal short-term and working memory, is widely used globally. Emerging research has revealed variations in digit span performance across languages among young adults; however, studies focusing on older populations are scarce and typically involving limited languages. This study investigates digit span performance among older adults (40-90 year-old) across fourteen languages and explored the influence of lexical and numerical properties on cognitive assessment. METHOD: We examined digit span performance among cognitively normal participants (CN), individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) across fourteen language cohorts totalling 3,681 participants: English (n = 446), Mandarin (n = 97), Cantonese (n = 65), Spanish (n = 218), Kannada (n = 69), Hindi (n = 72), Telugu (n = 69), Malayalam (n = 70), Bengali (n = 70), French (n = 299), Korean (n = 1098), Italian (n = 540), Arabic (n = 50), and Turkish (n = 518). First, we analyzed language differences in digit span performance among CN using ANOVA and general linear models. We then conducted Receiver operating characteristic (ROC) analyses to identify the optimal cutoff values for AD. Next, we computed the digit count, syllable count, and numerical magnitude (i.e. the average sum of the digits) of all digit stimuli in the English cohort and analyzed their effects via linear and ridge regression analyses. RESULT: The forward (FDS) and backward digit span (BDS) tests revealed significant differences among CN across the fourteen language cohorts even after adjusting for age and education (FDS:F=38.62, p <0.001; BDS:F=19.23, p <0.001). ROC analysis revealed varying optimal cutoff values across languages: English (FDS:6, BDS:4), Italian and Turkish (FDS:5, BDS:4), Mandarin (FDS:7, BDS:5), Cantonese (FDS:7, BDS:4), and French (FDS:6, BDS:3). Further analysis indicated that the interaction between digit and syllable counts significantly impacted FDS accuracy in English speakers (linear:p=0.00035; ridge:p< 0.000001), with no significant effect from digit count alone after adjusting for interaction. Conversely, BDS performance showed a significant negative influence from digit count (p = 0.00858), with numerical magnitude and syllable count nearing significance (p = 0.083 and p = 0.066, respectively). CONCLUSION: Variations in digit span performance across languages illustrate the role of linguistic and numerical factors in cognitive assessments, even with tests targeting non-language domains using digit stimuli. These findings underscore the critical value of language diversity in cognitive research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".