Developmental associations between short-term variability and long-term changes: Intraindividual correlation of positive and negative affect in daily life and cognitive aging.
Bibliographic record
Abstract
Conceptual notions and empirical evidence suggest that the intraindividual correlation (iCorr) of positive affect (PA) and negative affect (NA) is a meaningful characteristic of affective functioning. PA and NA are typically negatively correlated within-person. Previous research has found that the iCorr of PA and NA is relatively stable over time within individuals, that it differs across individuals, and that a less negative iCorr is associated with better resilience and less vulnerability. However, little is known about how the iCorr of PA and NA relates to cognitive aging. This project examined how the association between PA and NA in everyday life is associated with long-term cognitive aging trajectories. To do so, we linked microlongitudinal data on PA and NA obtained on up to 33 occasions over 6 consecutive days with macrolongitudinal data on fluid and crystallized cognitive abilities obtained over 15 years from a subsample of Berlin Aging Study participants (N = 81, mean age at the microlongitudinal study = 81 years, range 73-98; 41% women). Over and above age, gender, education, overall levels of PA and NA, and number of health conditions, a less negative iCorr of PA and NA was associated with lower levels of cognitive ability and steeper cognitive declines, particularly for fluency and knowledge abilities. We discuss possible mechanisms for this finding and argue that a less negative iCorr of PA and NA may be indicative of deficits in emotional integration that are tied to changes in crystallized aspects of cognitive abilities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".