Item-specific control of attention capture: An eye movement study
Bibliographic record
Abstract
Measures of attentional capture are sensitive to attentional control settings. Recent research suggests that such control settings can be linked associatively to specific items. Rapid item-specific retrieval of these control settings can then modulate measures of attentional capture. However, the processes that produce this item-specific control of attentional capture are unclear. The current study addressed this issue by examining eye-movement patterns associated with the item-specific proportion congruency effect (ISPC). Participants searched for a shape singleton target in search displays that also contained a colour singleton-the colour singleton was either the same item as the shape singleton (congruent trials) or a different item (incongruent trials). The relative proportions of congruent and incongruent trials were manipulated separately for two distinct item types that were randomly intermixed. Response times (RTs) were faster on congruent than incongruent trials, and this congruency effect was larger for high-proportion congruent (HPC) than low-proportion congruent (LPC) items. Eye movement data revealed a higher proportion of saccades towards the distractor and longer dwell times on the distractor in the HPC condition. These results suggest that item-specific associative learning can influence the strength of representation of the task goal (e.g., find the odd shape), a form of selection history effect in visual search.
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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 | 0.004 |
| 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".