Does interactive conditioning help users better understand the structure of probabilistic models?

Evdoxia Taka, Sebastian Stein, John H. Williamson

Abstract:

Despite growing interest in probabilistic modeling approaches and availability of learning tools, people are hesitant to use
them. There is a need for tools to communicate probabilistic models more intuitively and help users build, validate, use effectively or
trust probabilistic models. We focus on visual representations of probabilistic models and introduce the Interactive Pair Plot (IPP) for
visualization of a model’s uncertainty, a scatter plot matrix of a probabilistic model allowing interactive conditioning on the model’s
variables. We investigate whether the use of interactive conditioning in a scatter plot matrix of a model helps users better understand
variables’ relations. We conducted a user study and the findings suggest that improvements in the understanding of the interaction
group are the most pronounced for more exotic structures, such as hierarchical models or unfamiliar parameterizations, in comparison
to the understanding of the static group. As the detail of the inferred information increases, interactive conditioning does not lead to
considerably longer response times. Finally, interactive conditioning improves participants’ confidence about their responses.

Index Terms—Brushing-and-linking, empirical study, interactive conditioning, prior distribution, probabilistic models, scatter plot matrix