A multimodal analytical framework to estimate human error probability in cognitive-oriented tasks

In the Industry 5.0 landscape, placing humans at the center of production requires robust models to manage Human Error Probability (HEP) amid increasing cognitive complexity. Despite the availability of separate evaluation methods, there is a lack of predictive models that explicitly link HEP to the simultaneous interplay of Task Complexity, Operator Characterization, and Work Environment. This research bridges the gap by proposing a novel multimodal analytical framework that uses Multi-Attribute Utility Theory (MAUT) to synthesize structural task complexity (TACOM), operators' physical/cognitive traits, and environmental information. The framework was evaluated through a controlled laboratory study involving 22 operators. Multiple regression analysis explored the distinct statistical contributions of the macro-domains (p < 0.05), providing a preliminary structural evaluation of the model's architecture. Furthermore, a comparative ROC analysis against traditional baselines yielded low discriminative metrics (AUC 0.48 vs. 0.39), indicating that while the findings support conceptual plausibility and preliminary internal structural assessment, static weight configurations are insufficient for robust field predictions. Notably, the framework modeled a pattern consistent with non-linear cognitive-load effects, offering a plausible interpretation, requiring direct future validation, of increased error probability among expert operators under low-stimulation (underload) conditions, alongside vulnerabilities among novice operators due to skill gaps. Quantitative analysis yielded Mean Absolute Error (MAE) values of 0.1897 and 0.1646. At the same time, this overestimation provides a functional safety margin by signaling latent risks; it also highlights the future need for calibrated alert thresholds to prevent alarm fatigue in real-world applications. This study offers a reproducible, context-aware assessment framework to support proactive human risk management in cognitively demanding industrial tasks.