A Bayesian cognition approach to improve data visualization

Yea Seul Kim, Logan A. Walls, Peter Krafft, Jessica Ruth Hullman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

People naturally bring their prior beliefs to bear on how they interpret the new information, yet few formal models exist for accounting for the influence of users’ prior beliefs in interactions with data presentations like visualizations. We demonstrate a Bayesian cognitive model for understanding how people interpret visualizations in light of prior beliefs and show how this model provides a guide for improving visualization evaluation. In a first study, we show how applying a Bayesian cognition model to a simple visualization scenario indicates that people’s judgments are consistent with a hypothesis that they are doing approximate Bayesian inference. In a second study, we evaluate how sensitive our observations of Bayesian behavior are to different techniques for eliciting people subjective distributions, and to different datasets. We find that people don’t behave consistently with Bayesian predictions for large sample size datasets, and this difference cannot be explained by elicitation technique. In a final study, we show how normative Bayesian inference can be used as an evaluation framework for visualizations, including of uncertainty.

Original languageEnglish (US)
Title of host publicationCHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450359702
DOIs
StatePublished - May 2 2019
Event2019 CHI Conference on Human Factors in Computing Systems, CHI 2019 - Glasgow, United Kingdom
Duration: May 4 2019May 9 2019

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2019 CHI Conference on Human Factors in Computing Systems, CHI 2019
CountryUnited Kingdom
CityGlasgow
Period5/4/195/9/19

Fingerprint

Data visualization
Visualization

Keywords

  • Bayesian cognition
  • Uncertainty elicitation
  • Visualization

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

Cite this

Kim, Y. S., Walls, L. A., Krafft, P., & Hullman, J. R. (2019). A Bayesian cognition approach to improve data visualization. In CHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3290605.3300912
Kim, Yea Seul ; Walls, Logan A. ; Krafft, Peter ; Hullman, Jessica Ruth. / A Bayesian cognition approach to improve data visualization. CHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2019. (Conference on Human Factors in Computing Systems - Proceedings).
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Kim, YS, Walls, LA, Krafft, P & Hullman, JR 2019, A Bayesian cognition approach to improve data visualization. in CHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery, 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, United Kingdom, 5/4/19. https://doi.org/10.1145/3290605.3300912

A Bayesian cognition approach to improve data visualization. / Kim, Yea Seul; Walls, Logan A.; Krafft, Peter; Hullman, Jessica Ruth.

CHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2019. (Conference on Human Factors in Computing Systems - Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Kim YS, Walls LA, Krafft P, Hullman JR. A Bayesian cognition approach to improve data visualization. In CHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. 2019. (Conference on Human Factors in Computing Systems - Proceedings). https://doi.org/10.1145/3290605.3300912