The Rapid Extraction of Statistical Properties in Visual Search
Bibliographic record
Abstract
Duncan and Humphreys (1989) proposed that search efficiency decreases as target-distractor similarity increases. The items that best resemble the target are grouped together, whereas the items that do not resemble the target are grouped together, and discarded. Search is then based only on the items that received the most activation. According to Ariely (2001) perceptual averaging (i.e., the ability of observers to represent sets of similar objects by their overall statistical properties, rather than their individual properties) could possibly facilitate this grouping process (see also Rosenholtz, 1999). In the present set of studies we used a series of conjunction search tasks to demonstrate that size averaging operates to improve the efficiency of search among items varying in size when size is both a) relevant to the search task (localize a target circle, defined by a color/size conjunction, Experiments 1 and 2) and b) irrelevant to the search task (localize a target line, defined by a color/orientation conjunction, Experiment 3 and 4). Results showed that search for a target was slower when target size corresponded to the average size of the distractors than when it did not (Experiment 1); that search was slower when target size corresponded to the average size of distractors appearing in the same color as the target than when it did not (Experiment 2); and that search was less efficient when target size corresponded to the average size of distractors appearing in the same color as the target than when it did not, even though target size was not a relevant search criterion (Experiments 3 and 4). These results emphasize the role of perceptual averaging in visual search among items varying in size, suggesting that targets are located first by segregating items into perceptual groups (by color) and then by isolating possible targets from distractors by size.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".