1328. Beyond taskload: Constructing a Multidimensional Cognitive Overload Indicator Using Efficiency Dominance
Invited abstract in session WB-60: DEA applications in Transportation, stream Data Envelopment Analysis and its applications.
Wednesday, 10:30-12:00Room: Western LT
Authors (first author is the speaker)
| 1. | Ahmed-Youssef Oukassou
|
| Economics & Quantitative Methods, IESEG SCHOOL OF MANAGEMENT | |
| 2. | Marijn Verschelde
|
| Department of Economics and Quantitative Methods, IÉSEG School of Management |
Abstract
Cognitive workload is a critical determinant of human performance in digital control rooms, yet its measurement remains challenging. Traditional approaches rely on one-dimensional indicators, such as taskload, which fail to capture the complexity of cognitive strain. More advanced methods, including physiological measures like heart rate variability and electroencephalography, offer deeper insights but are costly and impractical for large-scale implementation. This study introduces alpha-efficiency, a multidimensional, data-driven composite indicator that moves beyond simple taskload measures to assess both fatigue accumulation and mental effort. Rather than relying on intrusive physiological monitoring, alpha-efficiency constructs workload indicators from operational data, incorporating active signal clearances, taskload diversity, fatigue accumulation, and workstation unfamiliarity to distinguish between cognitive demands and resources. Using real-world railway digital control room data, we apply nonparametric efficiency analysis to develop a multi-dimensional cognitive overload indicator. Regression results confirm that alpha-efficiency is strongly associated with human error probability, with significant effects observed across different estimation techniques. Interaction effects further highlight that cognitive overload risk is particularly pronounced at both the lower and upper quantiles of cognitive load intensity. These findings demonstrate the effectiveness of alph
Keywords
- Data Envelopment Analysis
- Efficiency Analysis
- Railway Applications
Status: accepted
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