Older adults show a self-reference effect for narrative information
Bibliographic record
Abstract
The self-reference effect (SRE), enhanced memory for information encoded through self-related processing, has been established in younger and older adults using single trait adjective words. We sought to examine the generality of this phenomenon by studying narrative information in these populations. Additionally, we investigated retrieval experience at recognition and whether valence of stimuli influences memory differently in young and older adults. Participants encoded trait adjectives and narratives in self-reference, semantic, or structural processing conditions, followed by tests of recall and recognition. Experiment 1 revealed an SRE for trait adjective recognition and narrative cued recall in both age groups, although the existence of an SRE for narrative recognition was unclear due to ceiling effects. Experiment 2 revealed an SRE on an adapted test of narrative recognition. Self-referential encoding was shown to enhance recollection for both trait adjectives and narrative material in Experiment 1, whereas similar estimates of recollection for self-reference and semantic conditions were found in Experiment 2. Valence effects were inconsistent but generally similar in young and older adults when they were found. Results demonstrate that the self-reference technique extends to narrative information in young and older adults and may provide a valuable intervention tool for those experiencing age-related memory decline.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".