How Multiple Retrievals Affect Neural Reactivation in Young and Older Adults
Bibliographic record
Abstract
OBJECTIVES: Aging can reduce the specificity with which memory episodes are represented as distributed patterns of brain activity. It remains unclear, however, whether repeated encoding and retrieval of stimuli modulate this decline. Memory repetition is thought to promote semanticization, a transformative process during which episodic memory becomes gradually decontextualized and abstracted. Because semantic memory is considered more resilient to aging than context-rich episodic memory, we hypothesized that repeated retrieval would affect cortical reinstatement differently in young versus older adults. METHODS: We reanalyzed data from young and older adults undergoing functional magnetic resonance imaging while repeatedly viewing and recalling short videos. We derived trial-unique multivariate measures of similarity between video-specific brain activity patterns elicited at perception and at recall, which we compared between age groups at each repetition. RESULTS: With repetition, memory representation became gradually more distinct from perception in young adults, as reinstatement specificity converged downward toward levels observed in the older group. In older adults, alternative representations that were item-specific but orthogonal to patterns elicited at perception became more salient with repetition. DISCUSSION: Repetition transformed dominant patterns of memory representation away and orthogonally from perception in young and older adults, respectively. Although distinct, both changes are consistent with repetition-induced semanticization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".