Item and associative recognition with precuing and postcuing.
Bibliographic record
Abstract
M. Duncan & B. B. Murdock (2000) compared precued and postcued item recognition and serial recall showing precued-postcued differences for item recognition but not for serial recall. Precuing and postcuing refer to 2 conditions in which the instructions as to the type of recall test following the presentation of short lists of items is given before or after the list presentation. This methodology was extended here to a paired-associate task. In 2 experiments, short lists of paired associates were presented followed by single-item, old-new, or intact-rearranged pair recognition tests; test type was precued or postcued. A fast or slow presentation rate was used to discourage or encourage mediators. TODAM2 (a theory of distributed associative memory) predicts that there should be little or no cuing differences regardless of whether subjects use mediators to remember the pairs. As predicted the recognition data were essentially identical for the precued and postcued conditions.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".