Examining the influence of list composition on the mnemonic benefit of errorful generation
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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