Self-generation amplifies the errorless learning effect in healthy older adults when transfer appropriate processing conditions are met
Bibliographic record
Abstract
Errorless learning improves memory for older adults by providing individuals with correct information from the onset, thereby minimizing the misleading influence of errors. Our previous research demonstrated that self-generation enhanced the errorless learning effect among older adults in cued recall when encoding encouraged processing of cue–target relationships, suggesting that transfer appropriate processing is necessary for this interactive effect (Lubinsky, Rich, & Anderson, 2009 Lubinsky, T., Rich, J. B. and Anderson, N. D. 2009. Errorless learning and elaborative self-generation in healthy older adults and individuals with amnestic mild cognitive impairment: Mnemonic benefits and mechanisms. Journal of the International Neuropsychological Society, 15(05): 704–716. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar], Journal of the International Neuropsychological Society, 15, 704). The current study further tests this notion by investigating whether the interaction of errorless learning and self-generated learning is observed in free recall when study conditions foster encoding of inter-item associations. Healthy older adult participants studied related or unrelated words (manipulated between-subjects) under four within-subjects learning conditions representing the crossing of errorless/errorful learning and self-generated/experimenter-provided information. As predicted, self-generation enhanced the errorless learning advantage in free recall for related word lists but not unrelated word lists. The results are discussed in relation to the transfer appropriate processing view of generation effects.
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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 | 0.000 | 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) | 0.002 | 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".