Spatial context scaffolds long-term episodic richness of weaker real-world autobiographical memories in both older and younger adults
Bibliographic record
Abstract
Remembering life experiences involves recalling not only what occurred (episodic details), but also where an event took place (spatial context), both of which decline with age. Although spatial context can cue episodic detail recollection, it is unknown whether initially recalling an event alongside greater reinstatement of spatial context protects memory for episodic details in the long term, and whether this is affected by age. Here, we analysed 1079 personally-experienced, real-world events from 29 older adults and 12 younger adults. Events were recalled first on average 6 weeks after they occurred and then again on average 24 weeks after they occurred. We developed a novel scoring protocol to quantify spatial contextual details and used the established Autobiographical Interview to quantify episodic details. We found improved recall of episodic details after a delay if those details had initially been recalled situated in greater spatial context. Notably, for both older and younger adults, this preservation was observed for memories initially recalled with low, but not high, numbers of episodic details, suggesting that spatial context aided episodic retrieval for memories that required more support. This work supports the notion that spatial context scaffolds detail-rich event recollection and inspires memory interventions that leverage this spatial scaffold.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".