The insights from this study on Generalized Additive Models (GAMs) can be extended to other interpretable machine learning models by applying the same framework of quantifying visual properties and their impact on cognitive load. For instance, models such as decision trees, linear regression, and rule-based classifiers can benefit from similar analyses. By identifying key visual properties—such as the number of splits in decision trees or the complexity of rules in rule-based models—researchers can develop metrics that predict cognitive load and interpretability.
Moreover, the methodology of conducting user studies to assess perceived cognitive load can be adapted to evaluate other models. For example, using metrics like graph length or visual chunks can help in understanding how users interpret the outputs of models like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations). By establishing a common set of metrics across different models, researchers can create a standardized approach to assess interpretability, facilitating comparisons and improvements in model design.