Measuring Cognitive Function and Cognitive Decline With Response Time Data in the National Social Life, Health, and Aging Project
Bibliographic record
Abstract
OBJECTIVES: Scholarly, clinical, and policy interest in cognitive function has grown over the last several decades in part due to large increases in Alzheimer's disease and related dementias as populations age. However, adequate measures of cognitive function have not been available in many research data sets. We argue that a wealth of previously unexploited survey data exists to model cognition and cognitive decline. METHODS: We use metadata of the time it takes older respondents in the National Social Life, Health, and Aging Survey, which we label response times (RTs), to answer questions in a standard cognitive assessment. We compare several measures of RT to a survey-adapted form of the Montreal Cognitive Assessment (MoCA). RESULTS: We show that RTs predict both concurrent and future MoCA scores. Our results show that longer and more varied RT at baseline predict lower MoCA scores 5 years later, net of baseline scores and controls. We also show that the effect of RT measures on predicting current MoCA differs for individuals of different races and ages, but are not different by gender. DISCUSSION: Our paper demonstrates that RTs constitute a separate powerful measure of cognitive functioning. RTs may be remarkably useful both to clinicians and social scientists because they can increase the accuracy of cognitive assessment without increasing the time it takes to administer the assessment.
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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 | 0.032 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".