How do relative features guide attention in visual search?
Bibliographic record
Abstract
Research has shown that attention is often biased to the relative feature of the target in visual search (e.g., redder / larger / darker) rather than its absolute feature values, in line with a Relational Account of Attention (Becker, 2010). However, it is currently unknown how tuning to relative features is achieved. If we know the feature value of the target (e.g., orange), can the visual system rapidly assess the dominant feature in the visual scene (e.g., red or yellow) and compute the relative feature of the target prior to selection (e.g., yellower or redder)? Or do we need on-task experience to learn how the target differs from the context? Another important question is how search progresses through the search items when there are multiple distractors. When an orange target is redder than the majority of other items, do we first select the reddest item, then the next-reddest item and so forth, until we find the target? The present study tested these questions in a 36-item search display with multiple differently coloured distractors and variable target and non-target colours. The first fixations on a trial showed that these displays still reliably evoked relational search, even when observers had no knowledge of the context or relative feature of the target. This indicates that information about the relative target feature can be rapidly extracted and guide attention prior to the first eye movement. Moreover, the first five fixations within a trial revealed that we tend to select the most extreme items first (e.g., red), followed by the next-extreme (e.g., red-orange), etc., until the target is found. This shows that attention is first guided by relative features and only hones in on the exact target colour after multiple fixations on relatively more extreme distractors.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".