Training recollection in healthy older adults: clear improvements on the training task, but little evidence of transfer
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Normal aging holds negative consequences for memory, in particular for the ability to recollect the precise details of an experience. With this in mind, Jennings and Jacoby (2003) developed a recollection training method using a single-probe recognition memory paradigm in which new items (i.e., foils) were repeated during the test phase at increasingly long intervals. In previous reports, this method has appeared to improve older adults' performance on several non-trained cognitive tasks. We aimed to further examine potential transfer effects of this training paradigm and to determine which cognitive functions might predict training gains. Fifty-one older adults were assigned to either recollection training (n = 30) or an active control condition (n = 21) for six sessions over 2 weeks. Afterward, the recollection training group showed a greatly enhanced ability to reject the repeated foils. Surprisingly, however, the training and the control groups improved to the same degree in recognition accuracy (d') on their respective training tasks. Further, despite the recollection group's significant improvement in rejecting the repeated foils, we observed little evidence of transfer to non-trained tasks (including a temporal source memory test). Younger age and higher baseline scores on a measure of global cognitive function (as measured by the Montreal Cognitive Assessment tool) and working memory (as measured by Digit Span Backward) predicted gains made by the recollection training group members.
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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 | 0.001 |
| 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".