Examining the influence of list composition on the mnemonic benefit of errorful generation
Bibliographic record
Abstract
Despite literature showing that errorful generation with corrective feedback enhances retention better than mere studying, it is unclear if this benefit depends on the composition of the learning list (pure error generation/read versus mixed). Here, we investigated whether the mnemonic advantage and metamnemonic evaluation of errorful generation generalise beyond mixed-list designs. Experiment 1 used a free-recall test, while Experiments 2 and 3 used a cued-recall test, with Experiment 3 also including a judgment of learning (JOL) assessment. Only when memory was tested via free recall did the benefit of errorful generation depend on experimental design, with the effect being most robust in mixed lists. Replicating past research, we too found that despite a clear mnemonic benefit for error generation in cued-recall tests, participants predicted better memory following read-only trials, and that this effect was not contingent on list composition. At the practical level, these findings demonstrate instances in which errorful generation is beneficial for memory and learning. At the theoretical level, the results fit nicely within the item-order framework in accounting for commonly observed design effects in free recall.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
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".