Cognitive Reserve Capacity: Exploring and Validating a Theoretical Model in Healthy Ageing
Bibliographic record
Abstract
OBJECTIVE: Cognitive reserve (CR) capacity can be viewed as the maximum processing potential of neural systems that support adaptive cognitive performance in age-related cognitive decline. CR is a complex construct that can only be measured indirectly. Proxy measures (e.g., psychosocial/lifestyle variables) are traditionally used to reflect CR. However, strong relationships have been observed between these measures and cognitive functions (e.g., executive function [EF], processing resources [PR], fluid/crystallized abilities); therefore, the organizational structure of indicators implicated in CR remains unclear. The objective of this study was to test a hypothetical, theoretical model of CR capacity that includes both traditional CR proxy indicators and measures of cognitive function [Satz et al. (2011). Journal of Clinical and Experimental Neuropsychology, 33(1), 121-130], which remain, as yet, untested. METHOD: Construct validity of the model was investigated in healthy older adults through exploratory and confirmatory factor analysis (EFA and CFA) using data from the Maastricht Ageing Study (MAAS). A secondary CFA was conducted to validate the model using data from the Irish Longitudinal Study on Ageing (TILDA). RESULTS: EFA and CFA in MAAS established a two-factor model comprising EF/PR and cumulative cognitive enrichment (CCE), which was further validated in a secondary analysis in TILDA. Convergent and discriminant validity was supported in MAAS (range of R2 = .228-.635; factor correlation confidence interval (CI) = .622, .740) and TILDA (range of R2 = .172-.899; factor correlation CI = .559, .624). CONCLUSIONS: A dual model of CR elucidated the relationships between hypothesized indicators of CR capacity and revealed a two-factor structure suggesting that both control (EF/PR) and representational processes (CCE) are involved in CR capacity.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".