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Abstract:Human performance in digital control rooms is strongly influenced by interface characteristics, which shape visual search, cognitive processing, and motor execution. Accurate prediction of operator action time is therefore essential for ergonomic evaluation, interface design, and performance optimization in safety critical systems. However, existing approaches typically rely on extensive experimental data or black box models, limiting their interpretability and practical applicability. This study proposes ATIM (ACTR based Task Interface Model), a theory guided and data calibrated modeling framework that predicts operator action time directly from interface features. The model decomposes total action time into visual, cognitive, motor, and interaction components, integrating principles from visual search theory, Fitts law, and cognitive architecture modeling. Interface characteristics, including target salience and semantic interference, are used as direct inputs, enabling prediction without additional user experiments. A dataset from a digital nuclear control room environment was used for calibration and validation. Task types were identified through data driven clustering, and separate parameter sets were estimated for novice and experienced users. The proposed framework achieved a mean absolute error of 3.17 s and demonstrated strong correlation with observed performance (r = 0.664) on a held-out validation set. The results show that ATIM generalizes well to unseen data while maintaining interpretability, offering a novel tool for ergonomic assessment and interface design in complex human machine systems.

Submission history

From: Xingyu Xiao [view email]
[v1] Mon, 4 May 2026 08:22:06 UTC (13,707 KB)