Cognitive Abilities Predict Safety Performance: A Study Examining High-Speed Railway Dispatchers
Bibliographic record
Abstract
Cognitive abilities are good predictors of safety performance in many occupations. However, this correlation has not been studied from the perspective of high-speed railway (HSR) dispatchers who play a vital role in ensuring the safety and punctuality of HSR transportation system. Therefore, studying factors affecting HSR dispatchers’ safety performance is not only of great importance in filling the theoretical gap, but also conducive to the selection and training of dispatchers, contributing to the reduction of human errors and the prevention of railway accidents. In this study, a total of 118 HSR dispatchers from a branch of China Railway were recruited to complete the tests that examined their cognitive abilities related to the dispatching job, including logical reasoning, visual multiobject tracking, working memory, task switching, and cognitive flexibility. Safety performance, including both the safety evaluation score obtained from the dispatchers’ monthly safety performance record of the Railway Bureau and the emergency disposal performance indicated by train delay time, was evaluated with a dispatch simulator. The results suggested that better abilities in visual multiobject tracking, working memory, task switching, and cognitive flexibility were correlated with higher safety evaluation score (reflecting daily safety performance) and shorter train delay time (reflecting safety and efficiency in emergency disposal). No significant correlation was found in logical reasoning. These findings support the recommendation that cognitive abilities investigated as predictors of safety performance could be useful for the selection and training of HSR dispatchers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".