Not all objects are created equal: the object benefit in visual working memory is supported by greater recollection-like memory, but only for memorable objects
Bibliographic record
Abstract
Visual working memory is thought to have a fixed capacity limit. However, recent evidence suggests that a greater number of real-world objects than simple features (i.e., colors) can be maintained, an effect termed the object benefit. Here, we examined whether this object benefit in visual working memory is due to qualitatively different memory processes employed for meaningful stimuli compared to simple features. In online samples of young adults, real-world objects were better remembered than colors, had higher measures of recollection, and showed a greater proportion of high-confidence responses (Exp. 1). Objects were also remembered better than their scrambled counterparts (Exp. 2), suggesting that this benefit is related to semantic information, rather than visual complexity. Critically, the specific objects that were likely to be remembered with high confidence were highly correlated across experiments, consistent with the idea that some objects are more memorable than others. Visual working memory performance for the least-memorable objects was worse than that of colors and scrambled objects. These findings suggest that real-world objects give rise to recollective, or at least high-confidence, responses at retrieval that may depend on activation of semantic features, but that this effect is limited to certain objects.
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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 | 0.003 |
| 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".