Using Resource-Rational Analysis to Understand Cognitive Biases in Interactive Data Visualizations
This work addresses the issue of cognitive biases in decision-making for data visualization users, but it is incremental as it builds on existing theories without presenting new experimental results or concrete improvements.
The paper tackles the problem of understanding cognitive biases in interactive data visualizations by proposing the integration of resource-rational analysis through constrained Bayesian cognitive modeling, aiming to provide a more realistic bounded rationality representation of users and a research roadmap for future studies.
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