Evidence for rapid extraction of average numeric value
Bibliographic record
Abstract
The visual system may represent general physical properties of objects through parallel extraction of statistical descriptors such as mean size, mean orientation, mean direction of motion, and mean speed (Ariely, 2001; Ariely & Burbeck, 1995; Chong &Treisman, 2003). We examined whether semantic properties, particularly average numeric value, are similarly represented. We asked observers to determine which side of a briefly presented (200 ms) display had the larger average numeric value, the greater occurrence of a given letter, or the greater occurrence of a given shape. We measured RT and error rate for each judgment. In Experiment 1, we asked participants to compare displays of block-character 2′s and 5′s, either upright or rotated sideways 90o. In the upright digits condition, participants indicated which side of the display had the larger mean numeric value. In the sideways digits condition, we asked them which side of the display had more of a given shape, (a “5” rotated 90o to the left). In Experiment 2, observers compared displays of either p's and q's, or displays of these letters rotated 90o. In the upright letters condition, we asked subjects which side of the display had more q's. In the sideways letters condition, participants indicated which side of the display contained more of a given shape (this time a “q” rotated 90o to the left). We find that participants are faster and more accurate in the upright digits condition, while participants are equally slow and inaccurate in all other conditions. Our results suggest that subjects make numeric value judgments faster than judgments based on familiarity or shape. Specifically, our results suggest that average numeric value may be represented in a manner similar to physical statistical descriptors. Based on this initial evidence, we propose that rapid perceptual processing may also extract semantic descriptors of information, such as average numeric value.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".