Exploring Cognitive Load and Task Complexity in Dynamic Tracking Tasks: Insights for Construction Workflows
Bibliographic record
Abstract
Construction tasks involve dynamic and visually demanding activities that require continuous monitoring, tracking, and decision-making.These demands can overwhelm workers' cognitive capacity and increase mental strain and reduce efficiency.This study combines two cognitive load assessment methods: objective cognitive load assessment using wearable eye tracking and task-based performance analysis by calculating performance error.It examines the impact of complexity in dynamic tracking tasks on performance and cognitive load.The metrics include blink rate, fixation rate, saccadic amplitude, saccadic peak velocity, saccadic mean velocity, and tracking error.Saccadic amplitude showed a strong positive correlation (r = 0.891) which reflect the need to scan broader areas for higher visual complexity.In contrast, saccadic peak velocity (r = -0.96),blink rate (r = -0.967),and fixation rate (r = -0.671)demonstrated negative correlations with task complexity, which suggests increased cognitive demands and a prioritization of accuracy over speed.Saccadic mean velocity showed minimal correlation (r = -0.07),which suggests it might not be a sensitive metric for evaluating the impact of task complexity.Performance error was measured as the Euclidean distance between gaze and target, and it revealed a strong positive correlation with task complexity (r = 0.967).It indicates reduced performance as complexity increased.These findings highlight the significant impact of complexity on cognitive and visual performance.This is particularly relevant in construction tasks that require continuous monitoring, tracking, and visual processing.Understanding these relationships helps optimize construction workflows, reducing cognitive strain, improving efficiency, and enhancing safety in visually demanding tasks.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".