Goals matter: Only searched-for visual working memory representations form an attentional control set.
Bibliographic record
Abstract
Attentional control settings (ACSs) guide attention in our complex visual environments by determining which objects capture our spatial attention. Both episodic long-term memory and semantic memory can support ACSs, but the role of visual working memory (VWM) remains unclear. Here, we assessed whether objects represented in VWM form an ACS and control attentional capture. In Experiment 1, participants maintained a colour in memory while completing a modified Posner cueing task that was designed to measure both singleton distractor costs and spatial cueing effects. The memory colour changed on each trial to limit the contribution of long-term memory. In Experiment 1, we replicated the typical finding of greater singleton capture by cues that matched the memory colour, indicating that the colour was represented in active VWM and produced an attentional bias. There was, however, no effect on spatial cueing; all cues produced comparable spatial cueing effects, even when they did not match the colour maintained in memory, indicating that the memory colour did not form an ACS. In Experiment 2, we adjusted the Posner cueing task so that participants had to search for the colour held in VWM. We again found enhanced singleton distractor costs by memory matching cues. Critically, the searched-for colour maintained in VWM formed an ACS; only memory matching cues, but not non-matching cues, produced a spatial cueing effect. These experiments contribute two important findings: 1) merely representing an object in active VWM is not sufficient for the representation of that object to form an ACS (Experiment 1), and 2) participants can form an ACS even when the searched-for colour changes from trial to trial, suggesting that—like episodic and semantic long-term memory—VWM can support ACSs (Experiment 2).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".