Prioritization of new objects during visual search is limited by the capacity of visual short-term memory
Bibliographic record
Abstract
When new items are presented in a visual scene, these items are typically given attentional priority over old ones. While some theories posit that this prioritization of new items is related only to the automatic capture of attention by luminance changes, other evidence suggests that the visual system may mark or inhibit the old items, thereby giving these old items less priority. If the visual system can in fact inhibit old items, the ability to do so may be limited by visual memory capacity (about 4 items). Accordingly, we tested whether the number of old items in a visual scene that could be given reduced priority was limited by the capacity of visual short-term memory (VSTM). We presented participants with a visual-search task in which 0–7 distractors were previewed for one second prior to the presentation of the target. The results demonstrate that the search time is not affected by the number of old items when the old items can be held in VSTM (i.e., when there are fewer than 4 old items). Furthermore, this prioritization occurs even in the absence of luminance changes. We also demonstrate that when the number of old items is greater than memory capacity, performance is benefited by the preview of old items, as search times are equivalent to the removal of roughly 4 distractors. These results provide compelling evidence that the number of old items that can be given reduced attentional priority relative to new items is limited by the capacity of visual short-term memory, suggesting a role for VSTM in the prioritization of new items in a visual scene.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".