Prior results demonstrated that the general visualization abstraction (GVA) algorithm can perform information abstraction (i.e., selection and grouping) and determine how information items should be presented (i.e., size) while lowering workload and improving situational awareness and task performance. This paper presents results from a within-subject evaluation to ascertain the relative strengths and weaknesses of the GVA algorithm's components and associated learning effects. The results corroborate the previous results and demonstrate that the GVA algorithm's underlying subcomponent structural composition is beneficial. Furthermore, these results indicate that usage of the GVA algorithm requires some learning before the benefits are achieved.