Can developmental disorders provide evidence for two systems of number computation in humans?
Bibliographic record
Abstract
Typically developing 6-month-olds can discriminate between small and large numerosities. Discrimination between small numbers is only possible in young infants when variables continuous with number (e.g. area or circumference) are also present in the display. However, they succeed on large number discrimination even when such variables are controlled for. This points to the possible existence of two different number systems in infancy. Williams syndrome (WS), is a genetic disorder, for which numerical deficits have been identified in older children and adults. The current study examined whether such deficits originate in infancy. We tested small and large number discrimination in infants and toddlers with WS. While they succeeded in discriminating between 2 and 3 elements when numerosity was confounded with total area, the same infants failed to discriminate between 8 and 16 elements, when continuous variables were controlled for. These findings suggest that a system for tracking certain features of small numbers of objects may be functional in WS, while large number discrimination reveals deficits already in infancy. We conclude that individual differences in infancy in large number discrimination is probably a better predictor of subsequent development of numerical cognition than small number discrimination.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".