Working memory inefficiency: Minimal information is utilized in visual recognition tasks.
Bibliographic record
Abstract
Can people make perfect use of task-relevant information in working memory (WM)? Specifically, when questioned about an item in an array that does not happen to be in WM, can participants take into account other items that are in WM, eliminating them as response candidates? To address this question, an ideal-responder model that assumes perfect use of items in a capacity-limited WM was tested against a minimal-responder model that assumes use of only information about the queried item. Three different WM tasks were adopted: change detection, identity recognition, and location recognition. The change-detection task produced benchmark WM results. The 2 novel tasks showed that only the minimal responder model provided convergence with this benchmark. This finding was replicable even when the change-detection task was replaced by a feature-switch detection task. Thus, it appears that people do not make full use of information in WM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".