Minds at Work: A Design for an Experimental Approach to Assessing Cognitive Abilities in Construction
Bibliographic record
Abstract
The construction industry relies heavily on the cognitive abilities of its workforce to manage intricate tasks and ensure project success. Research indicates that when the cognitive demands of tasks outweigh workers’ cognitive capabilities, this imbalance can significantly compromise worker performance and safety. Consequently, it is crucial to understand workers’ cognitive capabilities and assign tasks accordingly. However, existing cognitive assessment techniques might be limited when applied in the construction context due to dynamic construction site conditions and the absence of task complexity considerations. To address these gaps, this research designed standardized experimental procedures for assessing construction professionals’ cognitive abilities and calibrating construction task complexity using a design of experiments approach. Four key cognitive abilities were assessed through a combination of cognitive tests and wearable sensors. Pilot testing demonstrated that the designed experimental procedures successfully captured participants’ cognitive states, stress levels, and performance across varying complexity levels. Performance scores peaked at moderate complexity, consistent with the inverted U hypothesis, while stress and cognitive load increased with task complexity. National Aeronautics and Space Administration Task Load Index (NASA-TLX) survey results supported this trend, with participants reporting increased mental and temporal demands, frustration, and effort as task complexity increased. These findings confirm the feasibility and effectiveness of the designed dual-method procedure in construction-related cognitive assessment. This study advances knowledge by introducing the first construction-specific experimental protocol that integrates cognitive testing and sensor-based monitoring while accounting for task complexity. The proposed approach enables objective evaluation of cognitive fitness for construction roles, offering a foundation for practical applications in workforce planning, training, and safety management. Future work will involve deeper analysis of sensor-collected data to develop predictive models and scalable field applications.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".