A novel demonstration of preparation in pop-out search.
Bibliographic record
Abstract
There is an 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 if it was a particular colour and to ignore it otherwise-but to respond to the odd-coloured target in all following pop-out search displays. 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 response times. We discuss this result in relation to the interplay between top-down control and selection history effects in pop-out visual search. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".