Visual working memory supports the inhibition of previously processed information: Evidence from preview search.
Bibliographic record
Abstract
In four experiments we assessed whether visual working memory (VWM) maintains a record of previously processed visual information, allowing old information to be inhibited, and new information to be prioritized. Specifically, we evaluated whether VWM contributes to the inhibition (i.e., visual marking) of previewed distractors in a preview search. We evaluated this proposal by testing three predictions. First, Experiments 1 and 2 demonstrate that preview inhibition is more effective when the number of previewed distractors is below VWM capacity than above; an effect that can only be observed at small preview set sizes (Experiment 2A) and when observers are allowed to move their eyes freely (Experiment 2B). Second, Experiment 3 shows that, when quantified as the number of inhibited distractors, the magnitude of the preview effect is stable across different search difficulties. Third, Experiment 4 demonstrates that individual differences in preview inhibition are correlated with individual differences in VWM capacity. These findings provide converging evidence that VWM supports the inhibition of previewed distractors. More generally, these findings demonstrate how VWM contributes to the efficiency of human visual information processing--VWM prioritizes new information by inhibiting old information from being reselected for attention.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".