Sclodnick, Sun, & Milliken; A Novel Demonstration of Preparation in Pop-out Search
Bibliographic record
Abstract
There is ongoing debate among visual attention researchers about whether top-down processes contribute to pop-out search. In the present study we describe a new method to orthogonally manipulate top-down preparation and feature priming in a pop-out search task. On each trial, participants viewed a single item (randomly blue or orange) followed by a pop-out search display (randomly blue target with orange distractors, or vice versa). Preparation was induced by instructing participants to respond to the single item only if it was a particular colour, and to ignore it otherwise. Participants then responded on all trials to the odd-coloured item in the following pop-out search display. This method allowed us to examine whether top-down preparation for the single item influenced subsequent pop-out search. Our results revealed a large effect of preparation for the single item on subsequent search RTs. We discuss this result in relation to the interplay between top-down control and selection history effects in pop-out visual search.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".