Effects of task‐irrelevant or filler items on brain mechanisms of visual spatial attention
Bibliographic record
Abstract
Many visual search paradigms use color to distinguish task-relevant items from those considered fillers (e.g., blue task-relevant items and grey fillers). Hilimire and colleagues suggested that the N2pc, a lateralized electrophysiological component typically observed in visual attention, is a neural correlate for localized attentional interference, which postulates that target selection is degraded by nearby competing stimuli. In their study, N2pc amplitude decreased with decreasing distance between task-relevant items presented among fillers. With an increase in distance, however, there was also an increase in the number of fillers between task-relevant items. We tested whether this distance effect could be explained by the presence of fillers near task-relevant items rather than their proximity per se. We manipulated the distance between task-relevant items (adjacent, separated by two, or by four positions) and the presence/absence of fillers orthogonally. We used two color schemes: blue task-relevant items and grey fillers or grey task-relevant items and blue fillers (manipulated between-subjects) to control for color interactions. N2pc amplitude increased with increasing distance, but only when fillers were present, suggesting that the results of Hilimire et al. may be due to increasing fillers interference. Exploratory analyses also suggested that the colors selected to be task-relevant and task-irrelevant could play a role in our ability to filter task-irrelevant information. Our results suggest that fillers are not as inconsequential as sometimes assumed and generally support the Ambiguity Resolution Theory, where nearby items increase N2pc amplitude because of a greater need for focused attention.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".