A Bayesian Cognition Approach to Improve Data Visualization
This work addresses the challenge of incorporating users' prior beliefs into visualization design, offering a formal model that could improve evaluation methods, though it is incremental in applying existing Bayesian concepts to a new domain.
The paper tackled the problem of how people's prior beliefs affect their interpretation of data visualizations by proposing a Bayesian cognitive model, finding that people's judgments align with approximate Bayesian inference in simple scenarios but deviate for large datasets, and demonstrating the model's utility for evaluating visualizations, including uncertainty representations.
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.