A human-centered framework for assessing task complexity in construction: a cognitive load perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it