Familiar real-world spatial cues provide memory benefits in older and younger adults.
Bibliographic record
Abstract
Episodic memory, future thinking, and memory for scenes have all been proposed to rely on the hippocampus, and evidence suggests that these all decline in healthy aging. Despite this age-related memory decline, studies examining the effects of context reinstatement on episodic memory have demonstrated that reinstating elements of the encoding context of an event leads to better memory retrieval in both younger and older adults. The current study was designed to test whether more familiar, real-world contexts, such as locations that participants visited often, would improve the detail richness and vividness of memory for scenes, autobiographical events, and imagination of future events in young and older adults. The predicted age-related decline in internal details across all 3 conditions was accompanied by persistent effects of contextual familiarity, in which a more familiar spatial context led to increased detail and vividness of remembered scenes, autobiographical events, and, to some extent, imagined future events. This study demonstrates that autobiographical memory, imagination of the future, and scene memory are similarly affected by aging, and all benefit from being associated with more familiar (real-world) contexts, illustrating the stability of contextual reinstatement effects on memory throughout the life span. (PsycINFO Database Record
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".