Interactions between search mechanisms in conjunction search.
Bibliographic record
Abstract
We report the results of a technique designed to measure interactions between different visual search processes. We interrupted pop-out search before it produced a detection response, by adding extra distractors to the display so that a target initially defined by a single feature difference (e.g., a yellow horizontal line among yellow vertical lines) could then only be found on the basis of the conjunction of two features (a yellow horizontal line among yellow vertical lines and pink horizontal lines; difficult search). This technique has been used to measure the duration of the perceptual components of pop-out search, independent of over-all response time, for targets presented among different sets of distractors. In addition, when pop-out failed because it was interrupted, past work has shown that it nevertheless provided useful information to the processes responsible for difficult search. That is, partial pop-out assisted difficult search, when extra distractors made search difficult because the target was between the two types of distractors in the relevant feature space (Olds, Cowan, & Jolicoeur, 2000a,b,c). The present results demonstrate that partial pop-out also assists difficult search when difficult search is a conjunction search, and therefore these interactions may occur at a stage where information from different feature dimensions is combined.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".