A Comprehensive Comparison of Quantifications of Intraindividual Variability in Response Times: A Measurement Burst Approach
Bibliographic record
Abstract
OBJECTIVES: To formally identify and contrast the most commonly-employed quantifications of response time inconsistency (RTI) and elucidate their utility for understanding within-person (WP) and between-person (BP) variation in cognitive function with increasing age. METHOD: Using two measurement burst studies of cognitive aging, we systematically identified and computed five RTI quantifications from select disciplines to examine: (a) correlations among RTI quantifications; (b) the distribution of BP and WP variation in RTI; and (c) the comparability of RTI quantifications for predicting attention switching. RESULTS: Comparable patterns were observed across studies. There was significant variation in RTI BP as well as WP across sessions and bursts. Correlations among RTI quantifications were generally strong and positive both WP and BP, except for the coefficient of variation. Independent prediction models indicated that slower mean response time (RT) and greater RTI were associated with slower attention switching both WP and BP. For selecting simultaneous prediction models, collinearity resulted in inflated standard errors and unstable model estimates. DISCUSSION: RTI reflects a novel dimension of performance that is a robust and theoretically informative predictor of BP and WP variation in cognitive function. Among the plenitude of RTI quantifications, not all are interchangeable, nor of comparable predictive utility.
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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 | 0.030 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".