Aging impairs memory for perceptual, but not narrative event details
Bibliographic record
Abstract
Memory declines over the course of healthy aging. However, memory is not a monolith, and draws from different kinds of representations. Historically, much of our understanding of age-related memory decline arise from recognition of items that tend to focus on isolated studied items. In contrast, real-life events are remembered as narratives, and this kind of information is often missed in typical memory recognition studies. Here, we designed a task to tax mnemonic discrimination of event details, directly contrasting perceptual and narrative memory. Older and younger adults watched an episode of a television show and later completed an Old/New recognition test featuring targets, novel foils, and similar lures in narrative and perceptual domains. While we observed no age-related differences on basic recognition of repeated targets and novel foils, older adults showed a deficit in correctly rejecting perceptual, but not narrative lures. Further analyses split the older adult group into low versus high scorers on the Montreal Cognitive Assessment (MoCA), revealing that age-related deficits in discrimination performance of perceptual lures were largely driven by older adults with low MoCA scores. These findings provide insights into the vulnerability of different memory domains in aging, and may be useful in characterizing individuals at risk for pathological cognitive decline.
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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) | 0.002 | 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".