Does Interacting Help Users Better Understand the Structure of Probabilistic Models?
This addresses the need for more intuitive tools to communicate probabilistic models to less experienced users, though it is incremental as it builds on existing work in interactive visualizations.
The study tackled the problem of helping users with limited statistical background understand the structure of probabilistic models by evaluating the effect of interactive visualizations, finding that interaction improved comprehension for complex structures like hierarchical models and boosted user confidence without significantly increasing response times.
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.