On the cost and benefits of restudying: exploring the list strength effect in self-guided learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Across five experiments we examined whether restudying a self-selected subset of items impairs memory for the remaining non-restudied items, and enhances memory for the restudied items. This question was inspired by research on the list strength effect, in which re-presentation of only a subset of items from a list impairs recall for items presented only once, and enhances memory for items presented twice. We found that following initial encoding of all items, honouring participants’ restudy selections did indeed impair recall for the non-restudied items relative to when no items were restudied. Additionally, we found that memory for the subset of restudied items was enhanced relative to when all items were restudied. These findings expand previous research on the LSE to self-regulated learning and provide important new insights on how some learning strategies may in part be detrimental, but also beneficial, to future memory performance.
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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 | 0.001 |
| 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 it