Methods for validating chronometry of computerized tests
Bibliographic record
Abstract
Determining the speed at which a task is performed (i.e., reaction time) can be a valuable tool in both research and clinical assessments. However, standard computer hardware employed for measuring reaction times (e.g., computer monitor, keyboard, or mouse) can add nonrepresentative noise to the data, potentially compromising the accuracy of measurements and the conclusions drawn from the data. Therefore, an assessment of the accuracy and precision of measurement should be included along with the development of computerized tests and assessment batteries that rely on reaction times as the dependent variable. This manuscript outlines three methods for assessing the temporal accuracy of reaction time data (one employing external chronometry). Using example data collected from the Dalhousie Computerized Attention Battery (DalCAB) we discuss the detection, measurement, and correction of nonrepresentative noise in reaction time measurement. The details presented in this manuscript should act as a cautionary tale to any researchers or clinicians gathering reaction time data, but who have not yet considered methods for verifying the internal chronometry of the software and or hardware being used.
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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 | 0.070 | 0.206 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".