Process industries rely on effective decision-making by human operators to ensure safety and product quality. As essential decision-makers, their choices maintain the crucial balance between operational safety and potential risks. These decisions are strongly influenced by their training, where they cultivate mental models to comprehend and navigate complex processes. However, this understanding isn't solely technical; it also delves into cognitive behaviors, highlighting how information is acquired, interpreted, and acted upon. Assessing operator performance is imperative, but traditional methods present limitations: they often bring in subjectivity and overlook the cognitive elements that can be vital in accident causation. Therefore, understanding these cognitive aspects is vital for ensuring industry safety. Moreover, most of the existing approaches do not provide quantitative metrics to assess the operator’s performance. Generally, there are two types of industrial operators: control room operators and field operators. Control room operators, often considered the nerve center of industrial operations, are stationed in front of panels. They sift through a deluge of data, making decisions that steer the larger process. To understand their cognitive dynamics, we employed screen-based eye tracking, designing Areas of Interest (AOIs) based on expert input. This allowed us to delve deep into their attention patterns. Quantitative measures were developed using Fixation Transition Entropy (FTE), Correspondence Analysis (CA), and Hidden Markov Model (HMM) to objectively evaluate operator performance. The FTE provides quantitative information about the operator’s eye gaze transitions on the Human-Machine Interface (HMI). Metrics developed using Correspondence Analysis (CA) – association and salience metrics – went a step further, pinpointing the specific process variables that captivated their attention, especially during process abnormalities. With the intent of understanding the underlying cognitive frameworks operators rely on, the Hidden Markov Model (HMM) provided a mathematical blueprint of their evolving thought processes during operations. Two axioms of learning are derived from the proposed HMM-based mental models. These axioms help evaluate operator training in terms of understanding causal relationships and proactive monitoring strategies. Yet, industries aren't solely steered by those behind the panels. Field operators, often the eyes and ears on the ground, interact directly with the processes. Their dynamic work environment, marked by constant movement, renders traditional screen-based eye tracking inadequate. Recognizing this, our study forayed into the use of eye-tracking glasses tailored for field settings. Using the bounding boxes indicative of their gaze, we harnessed the power of Self- Organizing Maps (SOM). This approach clustered similar gaze patterns, illuminating areas of interest and subsequently highlighting key attention zones in a shifting environment. We conducted tests on both simulators and pilot plants, which validated the efficiency of these metrics for both operator types. In essence, this research offers a holistic view of operator performance, from panel- based decision-making to on-ground actions, ensuring that their choices are informed, precise, and prioritize safety. The quantitative nature of these metrics sidesteps the pitfalls of subjectivity, championing a consistent, repeatable, and neutral assessment methodology.