Operator training is critical to ensure safe operation in safety-critical domains such as chemical process industries. Training enhances the operator's understanding of the process, which is then encapsulated as mental models. Typically, the operator's learning in traditional training programs is assessed using expert judgment or in terms of process- and operator action-based metrics. These assessment schemes, however, ignore the cognitive aspects of learning, such as mental model development and cognitive workload. The HMM-based model proposed in Part 1 offers a systematic way to quantify operators' cognition during abnormalities. In this Part 2, we show that the cognitive behaviors displayed by expert operators can be represented as target values on the HMM's state transitions and emission probability distributions. Further, we propose two axioms of learning that can capture the evolution of the operator's mental models as they learn the causal relationships in the process and gain expertise in handling abnormal situations. We validate the proposed axioms by conducting training experiments involving 10 participants performing 486 tasks. Our results reveal that the axioms can accurately assess the progress of operators' learning. © 2022 Elsevier Ltd