Searching for Visual Singletons Without A Feature to Guide Attention
Bibliographic record
Abstract
RT studies have provided evidence for a singleton-detection strategy that is used to search for salient targets when there is no additional featural knowledge that would help guide attention. Despite this behavioral evidence, there have been few ERP studies of singleton detection mode because it was reported early on that the ERP signature of attentional selection (the N2pc) is absent without feature guidance. Recently, however, it was discovered that a small and relatively late N2pc occurs in singleton detection mode along with a previously unreported component called the singleton detection positivity (SDP). Here, we show that both components are influenced by the number of items in the display, as one might expect in a salience-based search mode. Specifically, the N2pc and SDP were larger when the set size was increased to make the singleton "pop out" more easily, when participants responded more quickly regardless of set size, and when RT search slopes were negative (Experiment 1). The latency of the SDP also depended on set size. In Experiment 2, EEG was recorded with a higher density electrode array to better characterize the scalp topography of the components and to estimate their neural sources. Regional sources near the ventral surface of extrastriate cortex in the occipital lobe explained over 96% of N2pc and SDP activities. These results indicate that searching in singleton detection mode selectively modulates processing within perceptual regions of visual cortex.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".