Measuring Executive Function Using Eye Movements on a Computerized Trail Making Test: A Pilot Study
Bibliographic record
Abstract
Objective: The current study investigated the validity of a novel computerized version of the Trail Making Test, and tested whether the integration of eye-tracking increased specificity and predictive power with other tests of executive function. We were specifically interested in whether eye movements, recorded during the completion of a computerized version of the Trail Making Test, served as a predictor of executive function as measured by the computerized Wisconsin Card Sorting Test. Methods: Forty participants completed the pencil-and-paper Trail Making Test, the computerized Wisconsin Card Sorting Test and the computerized Trail Making Test. Eye movements were recorded during the completion of the computerized Trail Making Test. Results: Eye-tracking measures for part B of the computerized Trail Making Test were correlated with T-scores for perseverative and non-perseverative responses/errors on the computerized Wisconsin Card Sorting Test. Hierarchical linear regression revealed that eye-tracking measures predicted variance for perseverative and non-perseverative errors/responses on the computerized Wisconsin Card Sorting Test, above and beyond Trail Making Test completion time. Conclusions: The current pilot study supported the use of computerized versions of the Trail Making Test and provided preliminary evidence that eye movements may significantly add to the specificity in assessing executive function using the Trail Making Test.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".