An Incidental Learning Method to Improve Face-Name Memory in Older Adults With Amnestic Mild Cognitive Impairment
Bibliographic record
Abstract
OBJECTIVE: Forgetting names is a common memory concern for people with amnestic mild cognitive impairment (aMCI) and is related to explicit memory deficits and pathological changes in the medial temporal lobes at the early stages of Alzheimer's disease (AD). In the current experiment, we tested a unique method to improve memory for face-name associations in people with aMCI involving incidental rehearsal of face-name pairs. METHOD: Older adults with aMCI and age- and education-matched controls learned 24 face-name pairs and were tested via immediate cued recall with faces as cues for associated names. During a 25- to 30-min retention interval, 10 of the face-name pairs reappeared as a quarter of the items on a seemingly unrelated 1-back task on faces, with the superimposed names irrelevant to the task. After the delay, surprise delayed cued recall and forced-choice associative recognition tests were administered for the face-name pairs. RESULTS: Both groups showed reduced forgetting of the names that repeated as distraction and enhanced recollection of these pairs. CONCLUSIONS: The results demonstrate that passive methods to prompt automatic retrieval of associations may hold promise as interventions for people with early signs of AD.
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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".