Attentional control settings and visual working memory (preprint)
Bibliographic record
Abstract
In the present study, we examined whether visual working memory (VWM) can support attentional control settings (ACSs) by maintaining representations of the visual properties that should capture attention. Beyond enhancing capture by memory-matching stimuli, can VWM representations suppress capture by non-matching stimuli? In Experiments 1a/b, participants maintained a colour in VWM that changed every trial while completing a Posner cueing task with memory matching and memory non-matching colour cues. We replicated the conventional finding that the colour in VWM modulated distractor costs, indicating that the colour was represented in the active state. Yet, this colour had no effect on the capture of visual spatial attention measured via cueing effects, suggesting that merely remembering a colour in VWM did not define participants’ ACSs. When participants searched for the colour in VWM, it did support an ACS that eliminated cueing effects by non-matching colours (Experiment 2), though not if participants searched for two colours stored in VWM (Experiment 3). These findings demonstrate that one active representation in VWM can support ACSs, though active representation alone is insufficient. These findings also speak to the ongoing debate about the automaticity of attentional capture by contributing additional evidence that distractor costs and cueing effects are dissociable measures of attentional capture.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".