Bibliographic record
Abstract
How do we find a target embedded in a scene? Within the framework of signal detection theory, this task is carried out by comparing each region of the scene with a "template," i.e., an internal representation of the search target. Here we ask what form this representation takes when the search target is a complex image with uncertain orientation. We examine three possible representations. The first is the matched filter. Such a representation cannot account for the ease with which humans can find a complex search target that is rotated relative to the template. A second representation attempts to deal with this by estimating the relative orientation of target and match and rotating the intensity-based template. No intensity-based template, however, can account for the ability to easily locate targets that are defined categorically and not in terms of a specific arrangement of pixels. Thus, we define a third template that represents the target in terms of image statistics rather than pixel intensities. Subjects performed a two-alternative, forced-choice search task in which they had to localize an image that matched a previously viewed target. Target images were texture patches. In one condition, match images were the same image as the target and distractors were a different image of the same textured material. In the second condition, the match image was of the same texture as the target (but different pixels) and the distractor was an image of a different texture. Match and distractor stimuli were randomly rotated relative to the target. We compared human performance to pixel-based, pixel-based with rotation, and statistic-based search models. The statistic-based search model was most successful at matching human performance. We conclude that humans use summary statistics to search for complex visual targets.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".