Updating visual working memory in the change detection paradigm
Bibliographic record
Abstract
An updating version of a visual change detection paradigm was used to investigate the behavioral outcomes and event-related potential (ERP) correlates of visual working memory updating. In each trial, participants were either presented with a memory array followed by a test probe, or with two successive memory arrays. Participants were instructed to update their working memory with the information in the second array. The second array differed from the first one in all, some, or none of the items. When a subset of the items was updated, the probe could appear in the location of a repeated item or of an updated item. Two experiments are reported, using set-sizes of six and two items, respectively. Both experiments show a benefit for probing a repeated item compared to an updated item. This result is consistent with an item-specific updating process. Experiment 2 also revealed two distinct updating-related ERP components, observed in both contralateral and ipsilateral visual hemifields. Frontal electrodes were sensitive to the number of changed items in the array. This ERP component was interpreted as reflecting the modification of information in working memory. Lateral-posterior electrodes only showed a difference between a full repetition of the array and updating, regardless of the number of updated items. This component was interpreted as reflecting attention to task-relevant information rather than the updating process per se. The finding of item-specific updating supports discrete-item architecture models of working memory.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".