Attentional Contributions to the Memorability Benefit in Visual Working Memory
Bibliographic record
Abstract
Some visual stimuli are consistently better remembered than others across individuals, due to variations in memorability (the stimulus-intrinsic property that determines ease of encoding into visual long-term memory (VLTM)). We found that this memorability benefit also exists in visual working memory (VWM). Within VWM, memorable stimuli enjoy a dual benefit: 1) they are stored more efficiently in VWM and 2) are also more competitive at attracting VWM resources than forgettable stimuli. In this study, we examined the hypothesis that this competitive advantage of memorable stimuli reflects preferential allocation of spatial attention toward memorable stimuli. To test this, we had participants perform a face VWM task where they had to remember an array composed of both memorable and forgettable faces. Critically, on 23% of trials, letters replaced the face stimuli at differing stimulus onset asynchronies. Participants then reported the letters they remembered seeing. This enabled us to examine where spatial attention was allocated at different times during viewing. We found that participants did not report more letters superimposed on memorable faces than on forgettable faces until 450ms after the onset of face stimuli, suggesting that spatial attention is not immediately biased toward memorable stimuli. Next, we examined when the competitive benefit emerged in relation to changes in attentional allocation. Does the competitive benefit lead to differences in attentional allocation or is attention responsible for the competitive benefit? We had participants perform the same VWM task and we manipulated the stimulus duration. We found that the competitive benefit was not present until after there were differences in attentional allocation. We speculate that this may be due to memory guided attention where memorable stimuli are efficiently encoded into VWM, which then attracts attentional resources ultimately leading to the competitive benefit.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".