Objective Display and control console human-machine interface (HMI) in complex task environments commonly exhibit issues such as redundant information hierarchies and low visual search efficiency, leading to cognitive overload for operators and seriously affecting task performance and operational safety. To address this situation, this study proposes a comprehensive evaluation method based on cognitive load theory and the fusion of subjective and objective weights. It aims to reduce external cognitive load by optimizing interface layout and information presentation, improve interaction efficiency and task execution performance, and provide a universal methodological reference for complex HMI design.
Method To achieve the above goal, a multidimensional evaluation system was constructed based on cognitive load theory and ergonomic principles, incorporating interface complexity, visual search efficiency, and reach envelope coverage. The BWM (best-worst method) was used to determine subjective weights, while the entropy method was applied to calculate objective weights. A fusion weighting model was employed to integrate subjective and objective indicators, and fuzzy comprehensive evaluation was conducted to assess interface performance. Jack software was used to simulate the reach and field of view of the console. Eye-tracking experiments, NASA-TLX scale, and task performance were used to validate the optimization effects with multidimensional data. Twelve male participants were recruited to perform 11 simulated tasks, and the interface performance before and after optimization was compared.
Result The optimized interface significantly improved task efficiency and accuracy. The primary task completion time decreased to (122.42 ± 17.23) s, and accuracy increased to (95.45 ± 8.23)%. Eye-tracking data showed a reduction in fixation count from 69.44 ± 12.35 to 56.24 ± 12.71 and a decrease in pupil dilation from (4.03 ± 0.23) px to (3.81 ± 0.28) px. The NASA-TLX score dropped markedly to 42.61 ± 4.54, and the fuzzy comprehensive score rose from 62.51 to 78.10. These results clearly demonstrated the effectiveness of the optimized design in reducing cognitive load and enhancing task performance.
Conclusion This study experimentally verifies the effectiveness of subjective-objective fusion weighting method. By integrating subjective and objective weights and applying fuzzy logic analysis, the method effectively reduces external cognitive load and significantly improves task performance. It provides a scientific and adaptable methodological approach for optimizing HMIs in high-load operational scenarios.
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