Effect of cognitive reserve on physiological measures of cognitive workload in older adults with cognitive impairments
Bibliographic record
Abstract
Abstract Background Cognitive reserve may protect against cognitive decline. However, its effect on physiological measures of cognitive workload in adults with cognitive impairments is unclear. Objective The aim was to determine the association between cognitive reserve and physiological measures of cognitive workload in older adults with and without cognitive impairments. Methods 29 older adults with cognitive impairment (age: 75±6, 11 (38%) women, MOCA scores 20±7) and 19 with normal cognition (age: 74±6; 11 (58%) women; MOCA 28±2) completed a working memory test of increasing task demand (0-, 1-, 2-back). Cognitive workload was indexed using amplitude and latency of the P3 event-related potential (ERP) at electrode sites Fz, Cz, and Pz, and changes in pupillary size, converted to an index of cognitive activity (ICA). The Cognitive Reserve Index questionnaire (CRIq) evaluated Education, Work Activity, and Leisure Time as a proxy of cognitive reserve. Results Higher CRIq total scores were associated with larger P3 ERP amplitude (p=0.048), independent of cognitive status (p=0.80), task demand (p=0.003), and electrode site (p<0.0001). This relationship was mainly driven by Work Activity (p=0.0005). Higher CRIq total scores also correlated with higher mean ICA (p = 0.002), regardless of cognitive status (p=0.29) and task demand (p=0.12). Both Work Activity (p=0.0002) and Leisure Time (p=0.045) impacted ICA. No relationship was found between CRIq and P3 latency. Conclusion Cognitive reserve affects cognitive workload and neural efficiency, regardless of cognitive status. Future longitudinal studies should investigate the causal relationship between cognitive reserve and physiological processes of neural efficiency across cognitive aging.
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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 | 0.000 |
| 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) | 0.001 | 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".