Behavioral Science of Data Visualization
I am a behavioral scientist whose research examines how visual information influences beliefs and behavior. I study data visualizations not only as tools for communicating information, but also as behavioral stimuli whose features shape how people attend to, interpret, and respond to evidence.
My work investigates the mechanisms linking data visualization features—such as design choices, visual complexities, static and interactive element, and framing—to cognitive, affective, and behavioral outcomes. Overall, my research seeks to understand when data visualizations help people make better judgments and when they may unintentionally hinder or bias them.
Gruttad’Auria, G., Puntiroli, M., Bangerter, A., Holzer, A., & Bezençon, V. (2025). Does Uncertain Data Prevent Belief Update? Displaying Uncertainty in Data Visualizations Decreases Belief Update via Lower Processing Fluency but Increases It Through Trust Among the Highly Numerate. Advances in Consumer Research, 53 (Association for Consumer Research Annual Conference, Washington, DC, USA).
Gruttad’Auria, G., Puntiroli, M., Bangerter, A., Holzer, A., & Bezençon, V. (2024). More Statistically Accurate, Yet Harder to Process? The Impact of Uncertainty in Data Visualizations on Consumer Responses. Advances in Consumer Research, 52 (Association for Consumer Research Annual Conference, Paris, France).
Gruttad'Auria, G. (2022). Public policies to contain the Covid-19 pandemic and air pollution: the impact of lockdowns in Northern Italy and Switzerland. View thesis record