Trail Making Test, Part B as a Measure of Executive Control: Validation Using a Set-Switching Paradigm
Bibliographic record
Abstract
Recent controversy surrounds the use of the Trail Making Test as a measure of cognitive flexibility, given that the Trail Making Test, Part B (TMT-B) also differs from Part A (TMT-A) in factors of motor control and perceptual complexity. The present study compared performance in the TMT and a set-switching task in order to test the assumption that cognitive flexibility is captured in TMT-B performance. Set-switching tasks have low motor and perceptual selection demands, and therefore provide a clearer index of executive function. In this study, participants made category judgments for digits, letters, or symbols across a series of trials, and performance for consecutive same-task trials was compared with task-switch trials. Results of the set-switching task indicated significant switch cost, but only for the situation of task alternation (e.g., an ABA series), suggesting that task-set inhibition may play a role in this effect. Alternating-switch cost was significantly correlated with TMT-B performance, especially with the TMT-B to TMT-A ratio (B/A). Cost for alternating switches was especially large for participants with B/A ratio > 3. These results provide direct evidence that the B/A ratio of performance in the TMT provides an index of executive function.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".