Disentangling Aging and Mood Effects on Emotional Memory
Bibliographic record
Abstract
Older adults tend to be in a more positive mood than young adults, and tend to remember positive information more often than negative information, yet the link between their positive mood and their positive memory bias has not often been explored. In this study, we manipulated young and older adults’ moods prior to their completing an emotional memory task. For mood manipulation, young (n= 147) and older (n= 111) adults viewed a positive, negative, or neutral video lasting 3 min. To validate the mood induction, we collected self-reported ratings of valence and arousal (affective slider; Betella and Verschure, 2016 ) at baseline, after the video, and after the memory task. The memory task consisted of incidental encoding of 30 intermixed pictures (10 positive, 10 negative, 10 neutral valence), followed by free recall. The mood manipulation changed people’s self-reported valence, yet it did not influence self-reported arousal. The memory task revealed a consistent negativity bias in young adults. Older adults recalled negative and positive pictures equally well in all conditions. After viewing a negative video, they recalled positive pictures more often than neutral pictures, but did not show the same advantage for negative pictures over neutral pictures. This positive memory advantage was weaker in the positive mood condition. Therefore, mood manipulation influenced in part older adults’ emotional memory bias, showing some signs of mood incongruence which we discuss in terms of emotion regulation. This shows the importance of accounting for mood differences in studies on aging and memory. The robust age group differences support the view that the positivity effect in aging is the result of a negativity bias that fades with age.
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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 | 0.002 |
| 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 | 0.001 |
| 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".