Face–name association learning in early Alzheimer's disease: A comparison of learning methods and their underlying mechanisms
Bibliographic record
Abstract
This study compared the efficacy of five learning methods in the acquisition of face-name associations in early dementia of Alzheimer type (AD). The contribution of error production and implicit memory to the efficacy of each method was also examined. Fifteen participants with early AD and 15 matched controls were exposed to five learning methods: spaced retrieval, vanishing cues, errorless, and two trial-and-error methods, one with explicit and one with implicit memory task instructions. Under each method, participants had to learn a list of five face-name associations, followed by free recall, cued recall and recognition. Delayed recall was also assessed. For AD, results showed that all methods were efficient but there were no significant differences between them. The number of errors produced during the learning phases varied between the five methods but did not influence learning. There were no significant differences between implicit and explicit memory task instructions on test performances. For the control group, there were no differences between the five methods. Finally, no significant correlations were found between the performance of the AD participants in free recall and their cognitive profile, but generally, the best performers had better remaining episodic memory. Also, case study analyses showed that spaced retrieval was the method for which the greatest number of participants (four) obtained results as good as the controls. This study suggests that the five methods are effective for new learning of face-name associations in AD. It appears that early AD patients can learn, even in the context of error production and explicit memory conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| 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".