Eye-tracking makes it possible to track the position and direction of a person s gaze on some stimulus (e.g., images or videos). This technology allows us to identify events inherent to human vision, containing implicit information capable of revealing essential aspects of one s behavior during a given task. However, identifying these pieces of information is a complex task that requires a set of skills to interpret the eye-tracking data and relate it to domain-specific knowledge. In this context, one can use intelligent systems to couple the knowledge and experience of specialists with the responses from the eye-tracking device. Thus, the main objective of this work is to propose a methodology to create eye-tracking-based systems to improve the assessment of subjects during specific tasks, resulting in a model that can represent the specialist s knowledge over subjective aspects to automate this process. Therefore, the present work s use case is the evaluation of the relationship between visual behavior and efficiency in solving tests inspired by Raven s Progressive Matrices. Those tests are commonly used in psychology to measure intelligence and abstract reasoning through image visualization. We chose an approach based on fuzzy rules, as it allows us to represent knowledge in a more readable way to end-users. The model s rules were developed and validated alongside a specialist in psychology. The system was tested with data extracted from users and showed promising results. The findings and models obtained in this research may be used as a foundation for the development of more robust systems.