Characterizing executive functioning in older special populations: From cognitively elite to cognitively impaired.
Bibliographic record
Abstract
The authors examined the structure and invariance of executive functions (EF) across (a) a continuum of cognitive status in 3 groups of older adults (cognitively elite [CE], cognitively normal [CN], and cognitively impaired [CI]) and (b) a 3-year longitudinal interval. Using latent variable analyses (LISREL 8.80), the authors tested 3-factor models ("Inhibition": Hayling [Burgess & Shallice, 1997], Stroop [Regard, 1981]; "Shifting": Brixton [Burgess & Shallice, 1997], Color Trails [D'Elia et al., 1996]; and "Updating": Reading and Computational Span [Salthouse & Babcock, 1991]) and 1-factor models within each group. Participants (initial N = 570; 53-90 years) were from the Victoria Longitudinal Study (Sample 3, Waves 1 and 2). Cross-sectionally, the authors observed a 3-factor EF structure especially for the CE group and 1-factor solutions for all 3 groups. Longitudinally, temporal invariance was supported for the 3-factor model (CE and CN groups) and the 1-factor model (CI and CN groups). Subgroups with higher cognitive status and greater 3-year stability performed better on EF factors than corresponding groups with lower cognitive status and less stability. Studies of EF structure, performance, dedifferentiation, and dysfunction will benefit from considering initial cognitive status and longitudinal stability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".