Memory Reactivation in Healthy Aging: Evidence of Stimulus-Specific Dedifferentiation
Bibliographic record
Abstract
We investigated how aging affects the neural specificity of mental replay, the act of conjuring up past experiences in one's mind. We used functional magnetic resonance imaging (fMRI) and multivariate pattern analysis to quantify the similarity between brain activity elicited by the perception and memory of complex multimodal stimuli. Young and older human adults viewed and mentally replayed short videos from long-term memory while undergoing fMRI. We identified a wide array of cortical regions involved in visual, auditory, and spatial processing that supported stimulus-specific representation at perception as well as during mental replay. Evidence of age-related dedifferentiation was subtle at perception but more salient during mental replay, and age differences at perception could not account for older adults' reduced neural reactivation specificity. Performance on a post-scan recognition task for video details correlated with neural reactivation in young but not in older adults, indicating that in-scan reactivation benefited post-scan recognition in young adults, but that some older adults may have benefited from alternative rehearsal strategies. Although young adults recalled more details about the video stimuli than older adults on a post-scan recall task, patterns of neural reactivation correlated with post-scan recall in both age groups. These results demonstrate that the mechanisms supporting recall and recollection are linked to accurate neural reactivation in both young and older adults, but that age affects how efficiently these mechanisms can support memory's representational specificity in a way that cannot simply be accounted for by degraded sensory processes.
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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) | 0.001 | 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".