The fate of visual working memory items after their job is done
Bibliographic record
Abstract
Visual working memory is a competitive, capacity-limited system for the storage of feature and object-based information. In change-detection tasks, items are encoded into memory and, after a retention period, are compared against a test set. Loss of information can occur from attentional interference or prioritizing some items over others. But what happens to the memory representations after the change-detection task is completed? The current article examines the fate of a memory item after its behavioral purpose has been fulfilled. Participants encoded a single item in memory for a difficult change-detection task. Visual search trials were presented both before and after the memory test was completed. Singleton distractors were present in these search trials that could match or not the memory item. In Experiment 1, memory-driven capture (the memory-matching distractors led to longer search response times than the unrelated distractor) was observed in the pre-memory test and, in a weaker form, the post-test search trials. In Experiment 2, we introduced cues that indicated the memory test would not occur on a subset of trials, controlling for re-exposure to the memory stimulus. Memory-driven capture was again observed for these post-cue search trials, but only at a short time interval, at a longer interval this effect was attenuated. These results suggest that the memory representations only linger briefly in the visual system.
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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 | 0.005 |
| 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 | 0.000 |
| 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".