Staying on Task: Age-Related Changes in the Relationship Between Executive Functioning and Response Time Consistency
Bibliographic record
Abstract
OBJECTIVE: Little is known about the relationship of executive functioning with age-related increases in response time (RT) distribution indices (intraindividual standard deviation [ISD], and ex-Gaussian parameters mu, sigma, tau). The goals of this study were to (a) replicate findings of age-related changes in response time distribution indices during an engaging touch-screen RT task and (b) investigate age-related changes in the relationship between executive functioning and RT distribution indices. METHOD: Healthy adults (24 young [aged 18-30], 24 young-old [aged 65-74], and 24 old-old [aged 75-85]) completed a touch-screen attention task and a battery of neuropsychological tests. The relationships between RT performance and executive functions were examined with structural equation modeling (SEM). RESULTS: ISD, mu, and tau, but not sigma, increased with age. SEM revealed tau as the most salient RT index associated with neuropsychological measures of executive functioning. Further analysis demonstrated that correlations between tau and a weighted executive function composite were significant only in the old-old group. DISCUSSION: Our results replicate findings of greater RT inconsistency in older adults and reveal that executive functioning is related to tau in adults aged 75-85. These results support literature identifying tau as a marker of cognitive control, which deteriorates in old age.
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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 | 0.005 |
| 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".