A human-centered framework for assessing task complexity in construction: a cognitive load perspective
Bibliographic record
Abstract
Purpose Construction workers process complex information, make decisions and coordinate tasks under deadlines. These cognitive demands can overwhelm workers, leading to errors and inefficiencies. While task complexity (TC) influences construction performance, prior research lacks a structured approach to assessing and managing cognitive load. This study introduces a scalable framework integrating cognitive load theory (CLT), Lean thinking and physiological metrics to evaluate TC and its impact on worker performance. Design/methodology/approach A design science research approach was used to assess TC and cognitive load in construction. Through literature reviews and expert consultations, a structured framework integrating cognitive load metrics and TC indicators was developed. The framework was validated through a controlled experiment simulating visual complexity using Object Speed (OS). A structural equation modeling (SEM) was developed to model TC as a latent construct using OS and cognitive load metrics while predicting performance errors. Findings The SEM model demonstrated relationships between TC, cognitive load and performance, confirming OS as a key determinant. The results support the framework’s ability to capture complexity-performance dynamics with high model fit indices and validate its use for interpreting cognitive responses to visual task variation. Research limitations/implications It supports human-centered task design to enhance productivity, safety and worker well-being. Future research should incorporate other complexity metrics and validate it in real-world construction. Originality/value This study applies CLT to construction and integrates TC concepts from behavioral science to provide structured TC assessment.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".