Task-irrelevant filler items alter the dynamics of electrical brain activity during visual search
Bibliographic record
Abstract
In electroencephalography (EEG) studies of visual search, task-irrelevant fillers are included in displays to balance bottom-up stimulation across the visual field and generally considered as inconsequential for performance or EEG results. We examined the impact of fillers on target and distractor processing using lateralised event-related potentials (ERPs). Two task-relevant items (TRIs) were presented, with or without fillers. One TRI (target or target-colour distractor) was on the vertical midline and the other in a lateral position (left or right visual field) on an imaginary circle around fixation. An N2pc was elicited by lateral targets and task-relevant distractors, suggesting that attention was allocated to the lateral TRI because it possessed a target defining feature (colour). A Ptc was only elicited by lateral task-relevant distractors, in line with previous research suggesting that this component is associated with distractor processing. When fillers were also in the circular arrangement, alterations in performance and neural activity occurred. Fillers enhanced and delayed attentional deployment (N2pc) and delayed distractor processing (Ptc). Critically, we observed no difference in Ptc amplitude according to filler presence. Thus, if the Ptc reflects active suppression (or attentional disengagement), it was not required for fillers. ERPs were also modulated by the distance between TRIs (which could be separated by one or four filler positions), but differently according to the colour scheme (blue TRIs with grey fillers or vice versa). Our results suggest that fillers affect lateralised electrophysiological activity at multiple loci during visual search and should not be considered inconsequential.
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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 | 0.002 |
| 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) | 0.002 | 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".