# A Bayesian Cognition Approach to Improve Data Visualization

## TL;DR

This paper introduces a Bayesian cognitive model to understand how prior beliefs influence data visualization interpretation and demonstrates its application in evaluating and improving visualizations, especially under uncertainty.

## Contribution

It presents a formal Bayesian model of user interpretation of visualizations and explores its implications for visualization evaluation and design improvements.

## Key findings

- People's judgments align with approximate Bayesian inference in simple scenarios.
- Bayesian behavior varies with dataset size and elicitation methods.
- Bayesian inference serves as an effective framework for visualization evaluation.

## 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.