My name is Ab Mosca.
Today I'll be presenting
our work: Does Interaction
Improve Bayesian Reasoning
with Visualization.
I collaborated on this project
with Alvitta Ottley
from Washington University
in St. Louis, and
Remco Chang from
Tufts University.
This project focuses on better supporting Bayesian reasoning;
which has been shown to be a difficult reasoning task for physicians and patients alike.
In the visualization community, there's been a lot of prior work
investigating the best visualization design to support Bayesian reasoning. However,
despite all of this work, there is no clear consensus on the best visualization to use.
In this project, we ask the question: What if we add interaction? In the visualization
community, we know there are certain cases where interaction is indisputably beneficial. In
particular, when we're trying to visualize large amounts of data on a small screen, interactions
such as zooming or panning are essential to reveal otherwise occluded or missing data.
But Bayesian reasoning visualizations are a fundamentally different context.
A Bayesian reasoning visualization only needs to communicate four values:
true positive, true negative, false positive, and false negative. As long as the visualization shows
these four values, a user has all the information they need to perform Bayesian inference.
In other words, adding interaction to a visualization like this will not reveal,
or add, any otherwise missing data that is essential to performing Bayesian reasoning.
To see if interaction is useful in this unique context, we ran two human-subject experiments.
Each of these experiments utilized three base visualization designs that range in the amount
of perceptual grouping they use to communicate sub-populations of interest in the Bayesian
reasoning problem. In experiment 1 we test each of these base visualization designs in a static and
interactive setting. The interaction we implement is called cbAll. It consists of adding checkboxes
next to each legend label of the visualization that allow participants to show, or not show,
different subpopulations of interest to the Bayesian reasoning problem.
We call this interaction cbAll, because the visualization loads on the page with
all checkboxes checked off. In experiment 2 we test a slight variation of cbAll.
In addition, we test an interaction we call cbNone which performs identically to cbAll,
but the visualization loads on the page with all checkboxes unchecked.
Additionally, we test a drag interaction which performs identically to cbNone. Finally,
we implement a hover and tooltip interaction. These are designed to draw a stronger link between
the textual and visual descriptions of the Bayesian reasoning problem.
The experimental task we give participants is a canonical Bayesian reasoning problem.
We analyze proportions of correct answers across the different visualizations.
Our findings are surprising. Despite our best efforts to implement interactions that would
aid in Bayesian reasoning, we find no situations in which participants assigned to interactive
visualizations performed with higher accuracy than participants assigned to static visualizations.
In addition, in experiment 2 we stratified our analyses based on participants' spatial ability
and find that for participants with high spatial ability, those assigned to the hover visualization
performed with significantly worse accuracy than those assigned to a static visualization. All this
is to say our main takeaway from this project is that, in the case of Bayesian reasoning,
a well-designed static visualization may be as, if not more, effective than an interactive one.
I'd like to recognize again my co-authors as well as our funding sources.
Thank you for listening I look forward to taking your questions.