Effective distribution of VWM resources does not depend on VWM capacity.
Bibliographic record
Abstract
Attention serves as a filter to capacity-limited visual working memory (VWM), ensuring that irrelevant information is not encoded. Dube et al. (2017) suggested that this attentional filter also regulates the distribution of VWM resources, ensuring the most relevant items are encoded with the greatest precision (the Filter and Distribute account). There are individual differences in VWM capacity, and high- and low-capacity individuals differ in their ability to filter distraction (Vogel et al., 2005). Here we examine whether high- and low-capacity individuals also differ in their ability to flexibly distribute VWM resources. We first used a change localization task to measure VWM capacity: participants viewed an array of colored squares and identified the item that changed color when the array reappeared. We separated participants into high- and low-capacity groups using a median split on capacity estimates. Next, participants viewed four colored shapes (two circles/two squares) before reporting the color of a probed shape in a continuous report task. We manipulated the likelihood that a square (or circle) would be probed (target shape counterbalanced across participants), such that the probed item was 60%, 70%, 80%, or 90% likely to be the pre-designated target shape (blocked conditions). We observed flexible resource distribution in both VWM groups: the precision of color report increased with increasing probe probability. Unlike the ability to filter out distraction, our results suggest that low-capacity VWM individuals do not show reduced ability to flexibly distribute resources in VWM. Thus, counter to the suggestion made by the Filter and Distribute account, the ability to filter information in/out of VWM and the ability to flexibly distribute resources among encoded information may be supported by distinct mechanisms.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".