Differences in the content and coherence of autobiographical memories between younger and older adults: Insights from text analysis.
Bibliographic record
Abstract
Several studies have shown that older adults generate autobiographical memories with fewer specific details than younger adults, a pattern typically attributed to age-relate declines in episodic memory. A relatively unexplored question is how aging affects the content used to represent and recall these memories. We recently proposed that older adults may predominately represent and recall autobiographical memories at the gist level. Emerging from this proposal is the hypothesis that older adults represent memories with a wider array of content topics and recall memories with a distinct narrative style when compared to younger adults. We tested this hypothesis by applying natural language processing approaches to a data set of autobiographical memories described by healthy younger and older adults. We used topic modeling to estimate the distribution (i.e., diversity) of content topics used to represent a memory, and sentence embedding to derive an internal similarity score to estimate the shifts in content when narrating a memory. First, we found that older adults referenced a wider array of content topics (higher content diversity) than younger adults when recalling their autobiographical memories. Second, we found older adults were included more content shifts when narrating their memories than younger adults, suggesting a reduced reliance on choronology to form a coherent memory. Third, we found that the content diversity measures were positively related to specific detail generation for older adults, potentially reflecting age-related compensation for episodic memory difficulties. We discuss how our results shed light on how younger and older adults differ in the way they remember and describe the past. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".