Using Resource-Rational Analysis to Understand Cognitive Biases in Interactive Data Visualizations

TL;DR

This paper proposes integrating resource-rational analysis with Bayesian cognitive modeling to better understand cognitive biases in interactive data visualizations, emphasizing bounded rationality and a feedback-driven research approach.

Contribution

It introduces a novel framework combining resource-rational analysis and Bayesian modeling to study cognitive biases in data visualization users.

Findings

Highlights the role of bounded rationality in visualization decision-making

Proposes a research roadmap for experimental and theoretical studies

Connects cognitive biases with resource constraints in visualization contexts

Abstract

Cognitive biases are systematic errors in judgment. Researchers in data visualizations have explored whether cognitive biases transfer to decision-making tasks with interactive data visualizations. At the same time, cognitive scientists have reinterpreted cognitive biases as the product of resource-rational strategies under finite time and computational costs. In this paper, we argue for the integration of resource-rational analysis through constrained Bayesian cognitive modeling to understand cognitive biases in data visualizations. The benefit would be a more realistic "bounded rationality" representation of data visualization users and provides a research roadmap for studying cognitive biases in data visualizations through a feedback loop between future experiments and theory