Mutual influences between numerical and non-numerical quantities in comparison tasks
Bibliographic record
Abstract
Humans possess a numerical intuition that allows them to manipulate large non-symbolic quantities. This ability has been broadly assessed with the help of number comparison tasks involving simultaneously displayed arrays. Many authors pointed out that the manipulation (or the lack thereof) of non-numerical features deeply impacts performance in these tasks, but the specific nature of this influence is not clear. The current study investigates the interaction between numerical and non-numerical quantity judgment tasks. Adult participants performed five distinct comparison tasks, each based on a target dimension: numerosity, total area, dot size, convex hull, and mean occupancy. We manipulated the relation between the target and the other dimensions to measure their respective influence on task performance. Results showed that total area and convex hull substantially affected numerosity comparisons. The number of dots conversely acted as an informative dimension when participants had to make a decision based on the total area or the convex hull. Our results illustrate that adults flexibly use non-target dimensions as visual cues to perform comparison judgments. Overall, this suggests that the influence found in numerical comparison tasks is explicit and deliberate rather than due to implicit visual integration processes.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".