The Role of Schematic Support and Emotional Valence in Associative Memory in Young and Older Adults
Bibliographic record
Abstract
The purpose of this study was to investigate the combined effect of positive facial expressions and prior knowledge on the associative memory of young and older adults. When asked to remember the association between paired items (i.e., associative memory), older adults tend to perform significantly worse than young adults. However, older adults’ associative memory performance can be ameliorated by high levels of schematic support from previous knowledge. Additionally, older adults tend to show a bias in attention and memory towards positive information, a phenomenon named the positivity effect. However, there is a gap in the literature about whether valence, such as the emotional expression of facial stimuli, will interact with or moderate the beneficial effect of schematic support on older adults’ associative memory. The current study aimed to examine this question by testing young and older adults on their memory for low schematic name-face and high schematic occupation-face associations of difference valences (happy vs. angry emotional facial expression). Associative memory performance was indexed by recognition discrimination (i.e., Hit rate-False alarm rate). This study found that occupations were significantly better recognized than names, for both happy and angry pairs. However, the valence effect was present only for occupation-face pairs, with a better recognition for positive pairs, in both age groups. The findings suggest that schematic support is an effective associative memory booster strategy that facilitates the valence memory advantage for positive over negative pairs.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".