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

TL;DR

This study asks whether perceptual affordances of chart types translate to high-level decision-making. Using a CSRankings-derived mentor-profile task, participants evaluated bar vs pie representations and reported willingness to work with faculty. Factor analysis revealed two latent constructs—Interdisciplinarity and Productivity—with productivity driving decisions, while chart type exerted only a small effect. Regression and distributional analyses show perceptual affordances have limited influence in real-world-style decisions, underscoring the need to evaluate visualizations in context and to separate perceptual from decision affordances when developing guidelines.

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.

Figures (5)

• Figure 1: Bar Chart and Pie Chart Stimuli for the four conditions: 6L (faculty with Large publication size in 6 areas), 2L (faculty with Large publication size in 2 areas), 6S (faculty with Small publication size in 6 areas), 2S (faculty with Small publication size in 2 areas).
• Figure 2: The hypothesized model (H), where Bar chart leads to stronger correlation for Productivity Factor, while Pie chart leads to stronger correlation for Interdisciplinarity Factor, and the actual result (R), while Productivity always has a stronger correlation with Willingness to Work, regardless of chart types.
• Figure 3: The empirical BIC and root mean residuals of factor models consisting of 1 to 6 factors.
• Figure 4: An illustration demonstrating that when the normalized Earth Mover's Distance value (Norm.EMD) is equal to 0.01, 0.1, 0.5, and 1, with a fixed Distribution 1 on the left side, what Distribution 2 would look like on the right side.
• Figure 5: Comparison of participant responses using bar charts (orange) and pie charts (blue) across four conditions (6L, 2L, 6S, 2S) for the two factors, with the red dash-line representing the average value of these factors. The diagrams show the Mean Difference (MD), normalized Earth Mover's Distance (Norm.EMD), and KS statistics, highlighting the impact of chart type on participant perceptions for each condition.* represents the significance of the KS test, with * representing $p-value<0.05$, ** representing $p-value<0.01$, and *** representing $p-value<0.001$.