Errorless learning and elaborative self-generation in healthy older adults and individuals with amnestic mild cognitive impairment: Mnemonic benefits and mechanisms
Bibliographic record
Abstract
Errorless learning is an intervention that benefits memory performance in healthy older adults and a variety of clinical populations. A limitation of the errorless learning technique is that it is passive and does not involve elaborative processing. We report two studies investigating the added benefits of elaborative, self-generated learning to the errorless learning advantage. We also explored the mnemonic mechanisms of the errorless learning advantage. In both studies, older adults and individuals with amnestic mild cognitive impairment (aMCI) completed four encoding conditions representing the crossing of errorless/errorful learning and self-generated/experimenter-provided learning. Self-generation enhanced the errorless learning benefit in cued recall and cued recognition, but not in free recall or item recognition. An errorless learning advantage was observed for priming of target words, and this effect was amplified for participants with aMCI after self-generated learning. Moreover, the aMCI group showed significant priming of prior self-generated errors. These results demonstrate that self-generation enhances the errorless learning advantage when study and test conditions match. The data also support the argument that errorless learning eliminates the misleading implicit influence of prior errors, as well as the need for explicit memory processes to distinguish targets from errors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".