Effects of Healthy and Neuropathological Aging on Autobiographical Memory: A Meta-Analysis of Studies Using the Autobiographical Interview
Bibliographic record
Abstract
OBJECTIVES: A meta-analytic review was conducted to assess the effects of healthy aging, amnestic mild cognitive impairment (MCI), and Alzheimer's disease (AD) on naturalistic autobiographical memory using the Autobiographical Interview, a widely used, standardized assessment that derives measures of internal (episodic) and external (nonepisodic) details from freely recalled autobiographical narratives. METHODS: A comprehensive literature search identified 21 aging, 6 MCI, and 7 AD studies (total N = 1,556 participants). Summary statistics for internal and external details for each comparison (younger vs older or MCI/AD vs age-matched comparison groups) and effect size statistics were extracted and summarized using Hedges' g (random effects model) and adjusted for the presence of publication bias. RESULTS: The pattern of reduced internal and elevated external details in aging was robust and consistent across nearly all 21 studies. MCI and-to a greater extent-AD were associated with reduced internal details, whereas the external detail elevation faded with MCI and AD. Although there was evidence of publication bias on reporting of internal detail effects, these effects remained robust after correction. DISCUSSION: The canonical changes to episodic memory observed in aging and neurodegenerative disease are mirrored in the free recall of real-life events. Our findings indicate that the onset of neuropathology overwhelms the capacity of older adults to draw upon distributed neural systems to elaborate on past experiences, including both episodic details specific to identified events and nonepisodic content characteristic of healthy older adults' autobiographical narratives.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".