Mistakes as stepping stones: Effects of errors on episodic memory among younger and older adults.
Bibliographic record
Abstract
The memorial costs and benefits of trial-and-error learning have clear pedagogical implications for students, and increasing evidence shows that generating errors during episodic learning can improve memory among younger adults. Conversely, the aging literature has found that errors impair memory among healthy older adults and has advocated for the use of errorless learning to rehabilitate memory. However, there is evidence that errors are not always beneficial for younger adults, nor always harmful for older adults. We propose that differences in the learning paradigms used in the younger and older adult literatures may account for these conflicting recommendations, namely that they typically engender conceptual and nonconceptual processing, respectively. In this study, we had younger and older adults study words under errorless and trial-and-error learning instructions and based either on conceptual (a flower--tulip) or lexical (ho___--house) cues. We found that relative to errorless learning, trial-and-error learning increased target memory in the conceptual condition but decreased it in the lexical condition. Critically, both age groups showed this pattern, implying that aging does not influence how we learn from mistakes. We suggest that conceptual guesses act as "stepping stones" toward the target whereas lexical guesses simply create retrieval noise. This suggestion was supported by the fact that participants of both ages remembered their prior guesses better in the conceptual than lexical condition and that memory for guesses mediated differences in target performance. These findings are discussed within the framework of current theories on the effects of error generation on episodic memory.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".