Language, visual working memory, and dot subtraction: What counts?
Bibliographic record
Abstract
To investigate cognitive factors affecting subtraction of visual objects, we adapted the dot subtraction task developed by Pica, Lemer, Izard, and Dehaene (2004), who used it to investigate calculation by the Mundurukú, an indigene group in Brazil that has a limited number word vocabulary. In the dot subtraction task, briefly displayed arrays of moving dots are used to represent the quantities for subtraction. We tested 40 Canadian university students' dot enumeration, Arabic digit subtraction, visual working memory, and performance on the dot subtraction task with dot display durations of 2, 1.5, 1, and .5 s. In the 2 s condition, error rates were uniformly low, whereas in the .5 s condition, error rates increased sharply as the minuend increased from 4 to 8, as was observed with the Mundurukú. Individual differences in dot subtraction accuracy were predicted by dot enumeration skill with longer dot display durations but were predicted by visual working memory efficiency with shorter durations. Pica et al. (2004) attributed the Mundurukú participants' very poor subtraction to the absence of counting words, but our results show that a shift to reliance on visual working memory is a nonlinguistic factor that comes into play in the dot subtraction task when time to encode the dot arrays is limited.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".