Rapid forgetting prevented by retrospective attention cues.
Bibliographic record
Abstract
Recent studies have demonstrated that memory performance can be enhanced by a cue which indicates the item most likely to be subsequently probed, even when that cue is delivered seconds after a stimulus array is extinguished. Although such retro-cuing has attracted considerable interest, the mechanisms underlying it remain unclear. Here, we tested the hypothesis that retro-cues might protect an item from degradation over time. We employed two techniques that previously have not been deployed in retro-cuing tasks. First, we used a sensitive, continuous scale for reporting the orientation of a memorized item, rather than binary measures (change or no change) typically used in previous studies. Second, to investigate the stability of memory across time, we also systematically varied the duration between the retro-cue and report. Although accuracy of reporting uncued objects rapidly declined over short intervals, retro-cued items were significantly more stable, showing negligible decline in accuracy across time and protection from forgetting. Retro-cuing an object's color was just as advantageous as spatial retro-cues. These findings demonstrate that during maintenance, even when items are no longer visible, attention resources can be selectively redeployed to protect the accuracy with which a cued item can be recalled over time, but with a corresponding cost in recall for uncued items.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".