Does Interacting Help Users Better Understand the Structure of Probabilistic Models?

TL;DR

This study investigates whether interactive visualizations enhance users' understanding of probabilistic models' structure, especially for complex models, and finds that interaction improves comprehension and confidence without significantly increasing response time.

Contribution

The paper presents an evaluation of an interactive visualization tool for probabilistic models, demonstrating its benefits over static visualizations in understanding complex structures.

Findings

Interaction improves understanding of complex models.

Interaction increases user confidence.

No significant increase in response time with interaction.

Abstract

Despite growing interest in probabilistic modeling approaches and availability of learning tools, people with no or less statistical background feel hesitant to use them. There is need for tools for communicating probabilistic models to less experienced users more intuitively to help them build, validate, use effectively or trust probabilistic models. Users' comprehension of probabilistic models is vital in these cases and interactive visualizations could enhance it. Although there are various studies evaluating interactivity in Bayesian reasoning and available tools for visualizing the sample-based distributions, we focus specifically on evaluating the effect of interaction on users' comprehension of probabilistic models' structure. We conducted a user study based on our Interactive Pair Plot for visualizing models' distribution and conditioning the sample space graphically. Our results suggest that improvements in the understanding of the interaction group are most pronounced for more exotic structures, such as hierarchical models or unfamiliar parameterizations in comparison to the static group. As the detail of the inferred information increases, interaction does not lead to considerably longer response times. Finally, interaction improves users' confidence.