Associations between Intra-Individual Variability of Reaction Time and Cognitive Function in Cognitively Normal Senior Adults: Still beyond Good or Bad?
Bibliographic record
Abstract
Background: Intra-individual (IIV) of reaction time (RT), as the short-term fluctuations of RT-based performance, has been reported to be susceptible to cognitive ageing. The current study aimed to examine IIV of RT and its relationships with cognitive performance in cognitively normal senior adults. Methods: We evaluated 137 community-dwelling elderly (mean age: 72.41 ± 3.99) with a comprehensive neuropsychological battery and a RT-based test. Intraindividual coefficient of variation of reaction time (ICV-RT) was used to evaluate the IIV. Pearson’s correlation and hierarchical multiple regression analyses were employed to assess the relationships between IIV and the scores of cognitive function. Results: Advancing age was accompanied with declined cognitive function and increased IIV. ICV-RT was negatively correlated with the score of Montreal Cognitive Assessment Hong Kong version (HK MoCA) across three types of flanker. Hierarchical multiple regression showed that ICV-RT was a significant predictor of HK MoCA (β = −0.294, p = 0.001). Conclusion: Increased IIV is consistently associated with and contributed to the age-related decline of cognitive performance in senior adults. The utility of IIV in predicting further deterioration should be carefully postulated with prospective studies.
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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 | 0.001 | 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".