Response Time Consistency Is an Indicator of Executive Control Rather than Global Cognitive Ability
Bibliographic record
Abstract
OBJECTIVES: Intraindividual variability increases with age, but the relative strength of association with cognitive domains is still unclear. The objective of this study was to examine the relation between cognitive domains and the shape and spread of response time (RT) distributions as indexed by intraindividual standard deviation (ISD), and ex-Gaussian parameters (μ, σ, τ). METHODS: Healthy adults (40 young [aged 18-30 years], 40 young-old [aged 65-74 years], and 41 old-old [aged 75-85 years]) completed neuropsychological testing and a touch-screen attention task from which ISD and ex-Gaussian parameters were derived. The relation between RT performance and cognitive domains (memory, processing speed, executive functioning) was examined with structural equation modeling (SEM), and the predictive power of RT distribution indices over age was investigated with linear regression. RESULTS: ISD, μ, and τ, but not σ, showed a linear increase with age group. An SEM showed that independent of age, τ was most strongly associated with executive functioning, while μ exhibited less critical associations. Linear regression indicated that μ and τ explained a significant portion of variance in processing speed and executive ability in addition to age group. Memory was more parsimoniously predicted by age, without any significant contribution of ex-Gaussian parameters. CONCLUSIONS: The findings suggest that exceptionally slow responses convey attention lapses through wavering of cognitive control, which strongly correspond to executive functioning tests. General slowing and extremely slow responses predicted processing speed and executive performance beyond age group, indicating that RT metrics are sensitive to differences in cognitive ability. (JINS, 2018, 24, 456-465).
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".