Preparing for the worst: Evidence that older adults proactively downregulate negative affect
Bibliographic record
Abstract
Abstract Age-related differences in processing emotional stimuli are well established. However, previous studies have only assessed the impact of age on emotional processing and encoding in response to, not in anticipation of, emotional stimuli. In the current study, we investigated age-related differences in the impact of emotional anticipation on affective responses and episodic memory for emotional images. Young and older were scanned while encoding negative and neutral images preceded by cues that were either valid or invalid predictors of image valence. Participants were asked to rate the emotional intensity of the images and to complete an episodic recognition task immediately after scanning. Using multivariate behavioral partial least squares (PLS) analysis, we found that young and older adults recruit the same set of brain regions to differentially support emotional processing during the anticipation of emotional images. Specifically, anticipatory recruitment of the amygdala, ventromedial PFC, and hippocampus in older adults predicts reduced memory for negative than neutral images for older adults and the opposite for young adults. Seed PLS analyses further show inverse coupling between the amygdala and ventromedial PFC activation following negative cues, consistent with the top-down spontaneous suppression of negative affect. To the best of our knowledge, this is the first study to provide evidence that the “positivity effect” seen in older adults’ memory performance is related to the spontaneous suppression of negative affect in anticipation of, not just in response to, negative stimuli.
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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 | 0.001 |
| 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 | 0.000 |
| 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".