Tensions between Preference and Performance: Designing for Visual Exploration of Multi-frequency Medical Network Data

The analysis of complex high-dimensional data is a common task in many domains, resulting in bespoke visual exploration tools. Expectations and practices of domain experts as users do not always align with visualization theory. In this paper, we report on a design study in the medical domain where we developed two high-fidelity prototypes encoding EEG-derived brain network data with different types of visualizations. We evaluate these prototypes regarding effectiveness, efficiency, and preference with two groups: participants with domain knowledge (domain experts in medical research) and those without domain knowledge, both groups having little or no visualization experience. A requirement analysis and study of low-fidelity prototypes revealed a strong preference for a novel and aesthetically pleasing visualization design, as opposed to a design that is considered more optimal based on visualization theory. Our study highlights the pros and cons of both approaches, discussing trade-offs between task-specific measurements and subjective preference. While the aesthetically pleasing and novel low-fidelity prototype was favored, the results of our evaluation show that, in most cases, this was not reflected in participants' performance or subjective preference for the high-fidelity prototypes.