Electrophysiological evidence of low salience distractor interference during visual search
Bibliographic record
Abstract
Visual search displays often include distractors of lesser salience in addition to a target and one or more salient distractors. We investigated low salience distractor effects on the N2pc, an ERP component indexing the deployment of attention, and the Ptc, a component purported to reflect attentional disengagement. We hypothesized that salient distractors pull the attentional focus away from the target, which could lead to increased attentional processing of low salience distractors close to the target and salient distractor. Participants looked for a colored inverted T during a visual search task while ignoring an L of the same color at a fixed distance on an imaginary circle around fixation. There were four conditions: no additional gray (low salience) distractors, two additional gray distractors between color items, two additional gray distractors just outside the area delimited by the colored items, and four additional gray distractors inside and outside the attended region. The gray distractors impacted N2pc and Ptc amplitude and latency, indicating an effect of gray distractors on attentional processing. Also, additional gray distractors led to increasingly more deviation of the N2pc and Ptc waveforms from the baseline offered by the condition with no additional gray distractors. When we increased the difficulty to individuate the target, we observed more displacement of lateralized activity from the N2pc to the Ptc time window. We argue that distractor-related modulations likely result from increased variance in the latency of attentional engagement activity to the target instead of distractor inhibition or attentional disengagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".