Cognitive Structure and Real Life Implementation of Scripts in Late Adulthood
Bibliographic record
Abstract
Previous research has demonstrated that healthy senescent cohorts manifest marked impairment in cognitive performance, particularly on tests of executive functions. Studies directly investigating ADL have found mild and tardive impairment in senescence, and a relation with certain executive functions, but the targeted ADL were very simple tasks such as memorizing a telephone number or walking a few meters and have always been strictly limited to the accuracy domain–excluding any speed factor. The purpose of the present study was (1) to investigate performance, in an experimentally controlled manner, in normal senescent cohorts, on one of the most complex ADL (planning and preparing a meal under time pressure), more indicative of true quality of life of senior citizens, and (2) to scrutinize its cognitive structure. A large battery of tests of executive function, including a script generation task were also administered. It was found that despite numerous marked impairments on tests of executive function, this particular ADL was not globally impaired even in advanced senescence. This finding suggests that the combination of deep proceduralization over a lifetime and continued daily practice suffice to maintain complex ADL, i.e., quality of life, well into late senescence, despite important decline in cognitive resources.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".