A Cognitive-Mechanistic Human Reliability Analysis Framework: A Nuclear Power Plant Case Study

Traditional human reliability analysis (HRA) methods, such as IDHEAS-ECA, rely on expert judgment and empirical rules that often overlook the cognitive underpinnings of human error. Moreover, conducting human-in-the-loop experiments for advanced nuclear power plants is increasingly impractical due to novel interfaces and limited operational data. This study proposes a cognitive-mechanistic framework (COGMIF) that enhances the IDHEAS-ECA methodology by integrating an ACT-R-based human digital twin (HDT) with TimeGAN-augmented simulation. The ACT-R model simulates operator cognition, including memory retrieval, goal-directed procedural reasoning, and perceptual-motor execution--under high-fidelity scenarios derived from a high-temperature gas-cooled reactor (HTGR) simulator. To overcome the resource constraints of large-scale cognitive modeling, TimeGAN is trained on ACT-R-generated time-series data to produce high-fidelity synthetic operator behavior datasets. These simulations are then used to drive IDHEAS-ECA assessments, enabling scalable, mechanism-informed estimation of human error probabilities (HEPs). Comparative analyses with SPAR-H and sensitivity assessments demonstrate the robustness and practical advantages of the proposed COGMIF. This work offers a credible and computationally efficient pathway to integrate cognitive theory into industrial HRA practices. Keywords: Human Reliability, Human Digital Twins, IDHEAS-ECA, TimeGAN, Bayesian 1 Introduction Human reliability analysis (HRA) plays a pivotal role in the safety assessment of complex socio-technical systems, particularly in high-risk domains such as nuclear power generation [1]. As a fundamental component of probabilistic risk assessment (PRA), HRA aims to estimate the likelihood of human error under specific operational contexts, thereby supporting risk-informed decision-making and the design of resilient safety systems. Over the past decades, a range of structured methodologies, such as the standardized plant analysis risk-human reliability analysis (SPAR-H) [2], the technique for human error rate prediction (THERP) [3], and more recently, the integrated human event analysis system for event and condition assessment (IDHEAS-ECA) [4], have been developed to quantify human error probabilities (HEPs). While these frameworks offer operational utility, they are primarily grounded in expert judgment, predefined performance shaping factors (PSFs), and empirically derived databases, often lacking a mechanistic understanding of the cognitive processes that drive operator actions and errors. Furthermore, traditional HRA approaches are highly dependent on two major data sources: (1) retrospective analysis of operational events, and (2) human-in-the-loop (HITL) simulation experiments conducted in controlled environments.