Cross-cultural and intra-cultural differences in finger-counting habits and number magnitude processing: Embodied numerosity in Canadian and Chinese university students
Bibliographic record
Abstract
Recent work in numerical cognition has shown-that number magnitude is not entirely abstract, and at least partly rooted in embodied and situated experiences, including finger-counting. The current study extends previous cross-cultural research to address within-culture individual differences in finger counting habits. Results indicated that Canadian participants demonstrated an additional cognitive load when comparing numbers that require more than one hand to represent, and this pattern of performance is further modulated by whether they typically start counting on their left hand or their right hand. Chinese students typically count on only one hand and so show no such effect, except for an increase in errors, similar to that seen in Canadians, for those whom self-identify as predominantly two-hand counters. Results suggest that the impact of finger counting habits extend beyond cultural experience and concord in predictable ways with differences in number magnitude processing for specific number-digits. We conclude that symbolic number magnitude processing is partially rooted in learned finger-counting habits, consistent with a motor simulation account of embodied numeracy and that argument is supported by both cross-cultural and within-culture differences in finger-counting habits.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".