Low-priority items are held in visual working memory: Evidence from flexible allocation in a two-alternative forced-choice (2AFC) paradigm
Bibliographic record
Abstract
Visual working memory (VWM) is characterized as extremely capacity limited. This finding is supported by the dramatic decline in change detection performance beyond a small number of items, as well as the observation of flat error distributions in delayed-estimation tasks. However, continuous resource models predict that small amounts of memory resources can be distributed to items at the expense of memory resolution (resulting in low response precision). These low-resolution memory representations should have nearly flat error distributions that could appear indistinguishable from uniform guessing distributions. In the current study, memory resource allocation was manipulated by varying the probability of an item being probed at recall. Responses were intermixed between continuous response and two-alternative forced-choice (2AFC) trials to examine whether these low-probability items could produce above-chance performance, consistent with them being held in memory. For comparison with the distribution of continuous responses, the magnitude of the discrimination between the target and lure colors was manipulated. Accuracy on the 2AFC trials was sensitive to both discrimination difficulty and probe probability manipulations. Critically, above-chance performance was found in the lowest probe probability condition (10% probe probability, equivalent to an item load of 10), suggesting that this condition had low-resolution memory representations rather than no memory representations. These findings are consistent with the predictions of continuous resource models and applications of signal detection models of VWM.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".