How does Encoding Context Affect Memory in Younger and Older Adults?
Bibliographic record
Abstract
How does encoding context affect memory? Participants studied visually presented words viewed concurrently with a rich (intact face) or weak (scrambled face) image as context and subsequently made "Remember", "Know", or "New" judgements to words presented alone. In Experiment 1a, younger, but not older, adults showed higher recollection accuracy to words from rich- than from weak-context encoding trials. The age-related deficit in recollection occurred, in Experiment 1b, even when encoding and retrieval time was doubled in older adults, suggesting that insufficient processing time cannot account for this age-related deficit. In Experiment 1c, dividing attention in young, during encoding, reduced overall memory, though the recollection boost from rich encoding contexts remained, suggesting that reduced attention resources cannot explain this age-related deficit. Experiment 2 showed that an own-age bias, to face images as context, could not explain the age-related differences either. Results suggest that age deficits in recollection stem from a lack of spontaneous binding, or elaboration, of context to target information during encoding.
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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 | 0.003 |
| 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 | 0.001 | 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".