Trial-and-error learning improves source memory among young and older adults.
Bibliographic record
Abstract
Trial-and-error learning, relative to errorless learning, has been shown to impair memory among older adults, despite evidence from young adults that errors may afford memorial benefits through richer encoding. However, previous studies on the effects of errorless versus trial-and-error learning in older adults has required production of errors based on perceptual cues. We hypothesized that producing errors conceptually associated with targets would boost memory for the encoding context in which information was studied, especially for older adults who do not spontaneously elaborate on targets at encoding. We report two studies examining the impact of generating errors during learning on source memory among young and older adults, with a process dissociation procedure employed in Study 1, and source memory assessed directly in Study 2. In both studies, participants were shown semantic category cues and generated an exemplar either with or without errors. In Study 1, for both age groups trial-and-error learning was associated with lower familiarity-based memory and higher recollection-based memory relative to errorless learning, and the latter effect was more marked for older than younger adults. Similarly, in Study 2, trial-and-error learning was associated with better source memory relative to errorless learning, particularly for the older adults. We argue that trial-and-error learning can enhance source memory and confer memorial benefits when making such errors facilitates semantic elaboration, especially for older adults who do not spontaneously engage in strategic encoding.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".