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

Taka, E. , Stein, S. and Williamson, J. H. (2023) Does interactive conditioning help users better understand the structure of probabilistic models? IEEE Transactions on Visualization and Computer Graphics, (doi: 10.1109/TVCG.2022.3231967) (PMID:37018342) (Early Online Publication)

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.

• Item Type: Articles
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Stein, Dr Sebastian and Taka, Dr Evdoxia and Williamson, Dr John
• Authors: Taka, E., Stein, S., and Williamson, J. H.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: IEEE Transactions on Visualization and Computer Graphics
• Publisher: IEEE
• ISSN: 1077-2626
• ISSN (Online): 1941-0506
• Published Online: 05 January 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Transactions on Visualization and Computer Graphics 2023
• Publisher Policy: Reproduced in accordance with the publisher copyright policy
• Data DOI: 10.5525/gla.researchdata.1248

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
300982		Exploiting Closed-Loop Aspects in Computationally and Data Intensive Analytics	Roderick Murray-Smith	Engineering and Physical Sciences Research Council (EPSRC)	EP/R018634/1	Computing Science