Memory for Public Events in Mild Cognitive Impairment and Alzheimer’s Disease: The Importance of Rehearsal
Bibliographic record
Abstract
Ribot's law refers to the better preservation of remote memories compared with recent ones that presumably characterizes retrograde amnesia. Even if Ribot-type temporal gradient has been extensively studied in retrograde amnesia, particularly in Alzheimer's disease (AD), this pattern has not been consistently found. One explanation for these results may be that rehearsal frequency rather than remoteness accounts for the better preservation of these memories. Thus, the aim of present study was to address this question by studying retrograde semantic memory in subjects with amnestic mild cognitive impairment (aMCI) (n = 20), mild AD (n = 20) and in healthy older controls (HC; n = 19). In order to evaluate the impact of repetition as well as the impact of remoteness, we used a test assessing memory for enduring and transient public events that occurred in the recent and remote past. Results show no clear temporal gradient across time periods (1960-1975; 1976-1990; 1991-2005; 2006-2011), but a better performance was observed in all three groups for enduring compared with transient events. Moreover, although deficits were globally found in both patients groups compared with HC, more specific analyses revealed that aMCI patients were only impaired on transient events while AD patients were impaired on both transient and enduring events. Exploratory analyses also revealed a tendency suggesting preservation of remote transient events in aMCI. These findings are discussed with regards to memory consolidation models.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".