The nationality benefit: Long-term memory associations enhance visual working memory for color-shape conjunctions
Bibliographic record
Abstract
Visual working memory (VWM) is typically found to be severely limited in capacity, but this limitation may be ameliorated by providing familiar objects that are associated with knowledge stored in long-term memory. However, comparing meaningful and meaningless stimuli usually entails a confound, because different types of objects also tend to vary in terms of their inherent perceptual complexity. The current study therefore aimed to dissociate stimulus complexity from object meaning in VWM. To this end, identical stimuli - namely, simple color-shape conjunctions - were presented, which either resembled meaningful configurations ("real" European flags), or which were rearranged to form perceptually identical but meaningless ("fake") flags. The results revealed complexity estimates for "real" and "fake" flags to be higher than for unicolor baseline stimuli. However, VWM capacity for real flags was comparable to the unicolor baseline stimuli (and substantially higher than for fake flags). This shows that relatively complex, yet meaningful "real" flags reveal a VWM capacity that is comparable to rather simple, unicolored memory items. Moreover, this "nationality" benefit was related to individual flag recognition performance, thus showing that VWM depends on object knowledge.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".