Exploring the Relationship between Emotional Valence and Prospective Memory Metamemory in Younger and Older Adults
Bibliographic record
Abstract
Prospective memory (PM) plays a crucial role in daily autonomy. Metamemory and emotional valence have both been shown to influence PM performance in younger and older adults. However, when considered together, the relationship between emotional valence, metamemory, and PM has not been examined yet, especially whether metamemory PM representations develop with task experience (i.e., before versus after performing a task). We collected data from 25 younger and 19 older adults using an event-based PM task with emotional cues (positive, negative, or neutral). Results revealed that younger adults’ predictions underestimated performance for neutral and negative cues. After performing the task, they showed more accurate representations for neutral cues, indicating that they monitored their representations. Older adults’ predictions overestimated performance for negative PM cues, and they did not modify representations after performing the task. Thus, we do not find evidence that older adults are able to coherently monitor their PM representations. These findings highlight the importance of understanding PM representations, especially in older adults, as they may lead to less strategy use and more impaired PM performance in negative everyday situations.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".