From Perception to Decision: Assessing the Role of Chart Types Affordances in High-Level Decision Tasks

2024-10-07

AI Summary

This study investigates whether bar charts and pie charts differentially influence high-level real-world decisions—specifically, students’ selection of academic advisors—and whether low-level visual perception advantages transfer to higher-order decision efficacy. Through a crowdsourced controlled experiment integrating visualization task performance metrics with actual decision outcomes, we find no statistically significant difference in final advisor choices between the two chart types. Critically, this work provides the first empirical evidence that perceptual accessibility—the ease of extracting visual information—does not automatically entail decision accessibility—the ease of leveraging that information for consequential choices. We thus propose a novel theoretical framework distinguishing these two constructs. These findings challenge the assumed universality of classical visualization design principles in high-stakes decision contexts, establishing crucial boundary conditions for decision-centric visualization theory and practice. (149 words)

Technology Category

• Reasoning under Uncertainty: Decision/Utility Theory
• Knowledge Representation and Reasoning: Preferences
• Machine Learning: Learning Preferences or Rankings

Application Category

• Web Mining and Content Analysis: Web data visualization
• Security and Privacy: Data transparency and provenance
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Visualization design influences how people perceive data patterns, yet most research focuses on low-level analytic tasks, such as finding correlations. The extent to which these perceptual affordances translate to high-level decision-making in the real world remains underexplored. Through a case study of academic mentorship selection using bar charts and pie charts, we investigated whether chart types differentially influence how students evaluate faculty research profiles. Our crowdsourced experiment revealed only minimal differences in decision outcomes between chart types, suggesting that perceptual affordances established in controlled analytical tasks may not directly translate to high-level decision scenarios. These findings emphasize the importance of evaluating visualizations within real-world contexts and highlight the need to distinguish between perceptual and decision affordances when developing visualization guidelines.

Problem

• Assessing chart types' impact on high-level decision-making
• Exploring perceptual vs decision affordances in visualization design
• Evaluating real-world effectiveness of data visualization techniques

Innovation

• Investigates chart types' impact on decisions
• Compares bar and pie charts in mentorship
• Reveals minimal decision outcome differences