Longitudinal mediation of processing speed on age-related change in memory and fluid intelligence.
Bibliographic record
Abstract
Age-related decline in processing speed has long been considered a key driver of cognitive aging. While the majority of empirical evidence for the processing speed hypothesis has been obtained from analyses of between-person age differences, longitudinal studies provide a direct test of within-person change. Using recent developments in longitudinal mediation analysis, we examine the speed-mediation hypothesis at both the within-and between-person levels in two longitudinal studies, Longitudinal Aging Study Amsterdam (LASA) and Origins of Variance in the Oldest-Old (OCTO-Twin). We found significant within-person indirect effects of change in age, such that increasing age was related to lower speed, which in turn relates to lower performance across repeated measures on other cognitive outcomes. Although between-person indirect effects were also significant in LASA, they were not in OCTO-Twin which is not unexpected given the age homogeneous nature of the OCTO-Twin data. A more in-depth examination through measures of effect size suggests that, for the LASA study, the within-person indirect effects were small and between-person indirect effects were consistently larger. These differing magnitudes of direct and indirect effects across levels demonstrate the importance of separating between- and within-person effects in evaluating theoretical models of age-related change.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".