Multielement Episodic Encoding in Young and Older Adults
Bibliographic record
Abstract
Previous research on age-related associative memory deficits has generally focused on memory for single associations. However, our real-world experiences contain a multitude of details that must be effectively integrated and encoded into coherent representations to facilitate subsequent retrieval of the event as a whole. How aging interferes with the processes necessary for multielement encoding is still unknown. We investigated this issue in the current fMRI study. While undergoing scanning, young and older adults were presented with an occupation and an object and were asked to judge how likely the two were to interact, either in general or within the context of a given scene. After scanning, participants completed recognition tasks for the occupation-object pairs and the sources/contexts with which the pairs were studied. Using multivariate behavioral partial least squares analyses, we identified a set of regions including anterior pFC and medial-temporal lobes whose activity was beneficial to subsequent memory for the pairs and sources in young adults but detrimental in older adults. An additional behavioral partial least squares analysis found that, although both groups recruited anterior pFC areas to support context memory performance, only in the young did this activity appear to reflect integration of the occupation, object, and scene features. This was also consistent with behavioral results, which found that young adults showed greater conditional dependence between pair and context memory compared with older adults. Together, these findings suggest that binding and/or retrieving multiple details as an integrated whole becomes increasingly difficult with age.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".