Longitudinal changes in response time mean and inconsistency exhibit predictive dissociations for risk of cognitive impairment.
Bibliographic record
Abstract
OBJECTIVE: Although mean response time (RT) is a commonly used indicator of cognitive function, trial-to-trial variability (RT inconsistency [RTI]) represents a dissociable dimension of performance arguably more sensitive for characterizing cognitive status. The present study explores whether (a) RT mean or inconsistency reflects a more dispositional characteristic of an individual, particularly with increased cognitive impairment; (b) RT mean and inconsistency exhibit comparable patterns of change across a 4-year period; and (c) these rates of change differentially predict cognitive status. METHOD: A sample of 304 adults (64-92 years) at baseline completed a choice RT task weekly for 4-5 weeks, repeating this protocol and a basic neuropsychological assessment annually for 4 years. Three cognitive status subgroups were identified at baseline and Year 4: healthy controls (HCs), as well as cognitively impaired-not-demented (CIND) status based upon single (CIND-S) and multiple (CIND-M) domains. RESULTS: < .01) were linked to increased likelihood of CIND-M classification at Year 4, independent of age, education, chronic health conditions, and mean RT. CONCLUSIONS: RT mean and RTI confer distinct sources of information about cognitive function and status. Overall, RTI holds promise as an early indicator of normal and pathological cognitive aging. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".