Beyond Distraction: Exploring the Conditions of Task-Irrelevant Feature Reinstatement and Working Memory-Based Capture during Visual Search
Bibliographic record
Abstract
Objects actively stored in visual working memory (VWM) for a recall task, produce VWM-based attentional capture. However, capture often only occurs for the object’s task relevant features. For example, when shown a red circle and asked to later report its shape, in an intervening singleton-search task, circle distractors (task-relevant feature) preferentially capture attention, and red distractors (task-irrelevant feature) do not. Interestingly, for an object’s features to be stored in VWM, they do not need to be shown, but can also be reinstated from long-term memory (LTM); for example, if the shape was previously associated with a colour. Unlike shown features, reinstated features in VWM appear to preferentially capture attention even when task irrelevant. To investigate this difference, we examined how capture by reinstated features is influenced by memory goal (remember vs. search), and search goal (singleton vs. object). Participants memorized a set of objects with specific colours, and then completed a dual task in which they were instructed to either remember or search for one of the objects’ shapes in any colour. On each trial, one memorized object was brought into VWM, and participants had to complete a shape-singleton search task followed by either an object recall, or an object search task. We found that, regardless of whether participants were instructed to remember or search for the object shape, the irrelevant reinstated colour did not capture attention more than any other colour in the shape-singleton search task. However, in both object tasks, participants were quicker to locate and report the object when it appeared in the irrelevant memorized colour. These results suggest that under both remember and search conditions, the task-irrelevant feature was reinstated, actively maintained in VWM, and could bias attention. But merely actively maintaining the irrelevant reinstated feature did not mean it always captured 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 | 0.001 | 0.011 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".