A Model Development to Differentiate Among Brain Activity and Cognitive Ability

Although the phrase “process model” is frequently used in literature, its exact definition is still unclear. In this study, we provide a new paradigm for explaining and building models of cognitive processes. Process models go beyond traditional models, which concentrate mostly on inputoutput interactions, and capture the dynamic information transformations that take place at different levels of abstraction. Our thesis depends on a number of process model defining characteristics. First of all, they cover a wide range of topics and several layers of abstraction. Second, they provide insights into the underlying cognitive processes at work by articulating a theoretical mental information transformation. Most importantly, these models shed light on the underlying mechanisms that underlie the behavior of interest in addition to predicting it. Notably, these forecasts are produced directly from input data, eliminating the requirement for output data to be reverse-inferred. Additionally, our methodology guarantees that the assumed information transformation procedures are consistent with what is already understood about human cognitive capacity, which adds to their validity and reliability. When it comes to the information that enters the mind, the suggested mental events, and the behavior that is being examined, process models require a well-defined conceptual scope. Across modeling paradigms, this approach is a useful tool for improving process models before to and following empirical testing. As a thorough guide for the creation of models of cognitive processes, it can contribute to the current discussions on how to classify various models in this field.