Metamemory judgments and the benefits of repeated study: Improving recall predictions through the activation of appropriate knowledge.
Bibliographic record
Abstract
Correspondence between judgments of learning (JOLs) and actual recall tends to be poor when the same items are studied and recalled multiple times (e.g., A. Koriat, L. Sheffer, & H. Ma'ayan, 2002). The authors investigated whether making relevant metamemory knowledge more salient would improve the association between actual and predicted recall as a function of repeated exposure to the same study list. In 2 experiments, participants completed 4 study-recall phases involving the same list of items. In addition to having participants make item-by-item JOLs during each study phase, after the 1st study-recall phase participants also generated change-in-recall estimates as to how many more or fewer words they would recall given another exposure to the same study list. This estimation procedure was designed to highlight repeated study as a factor that can contribute to recall performance. Activating metamemory knowledge about the benefits of repeated study for recall in this way allowed participants to accurately express this knowledge in a free-recall context (Experiment 2), but less so when the memory test was cued recall (Experiment 1).
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".