Retrospective Cue Benefits in Visual Working Memory are Limited to a Single Item at a Time
Bibliographic record
Abstract
Working memory (WM) performance can be improved by an informative cue presented during storage. This effect, termed a retrocue benefit, can be used to study limits on how human observers select and prioritize information stored in WM for behavioral output. There is disagreement about whether retrocue benefits extend to multiple WM items. One possibility is that relative to no- or neutral-cue trials multiple retrocues improve some aspects of memory performance (e.g., a reduction in random guessing) while worsening others (e.g., an increase in the probability of reporting a nonprobed item). We tested this possibility in three experiments. Participants remembered arrays of four orientations or colors over a brief delay. One, two, or all four of these items were retrospectively cued, and at the end of the trial a single item was probed for recall. Participants’ recall errors were lower during cue-one relative to cue-two and cue-four trials, and this benefit was driven primarily by a reduction in random guessing during cue-one trials. Moreover, recall precision, swap errors (i.e., reporting a nonprobed item), and guessing rates were statistically indistinguishable across cue-two and cue-four trials. Thus, multiple simultaneously presented retrospective cues led to no performance improvement relative to an uninformative cue, providing further evidence that retrocue benefits in WM performance are limited to a single item at a time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 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".