PatternSight: A Perceptual Grouping Effectiveness Assessment Approach for Graphical Patterns in Charts

TL;DR

PatternSight is a perceptual assessment tool for charts that predicts viewer noticeability of patterns, helping authors optimize design based on perceptual effectiveness.

Contribution

The paper introduces a perception simulation model that predicts perceptual grouping in charts, integrating perceptual theory for interpretability and practical assessment.

Findings

Model accurately simulates viewer perceptual grouping behaviors.

PatternSight effectively helps authors optimize chart designs.

User experiments confirm the tool's usefulness in pattern representation.

Abstract

The boom in visualization generation tools has significantly lowered the threshold for chart authoring. Nevertheless, chart authors with an insufficient understanding of perceptual theories may encounter difficulties in evaluating the effectiveness of chart representations, thereby struggling to identify the appropriate chart design to convey the intended data patterns. To address this issue, we propose a perception simulation model that can assess the perceptual effectiveness of charts by predicting graphical patterns that chart viewers are likely to notice. The perception simulation model integrates perceptual theory into visual feature extraction of chart elements to provide interpretable model outcomes. Human perceptual results proved that the outcome of our model can simulate the perceptual grouping behaviors of most chart viewers and cover diverse perceptual results. We also embed the model into a prototype interface called PatternSight to facilitate chart authors in assessing whether the chart design can satisfy their pattern representation requirements as expected and determining feasible improvements of visual design. According to the results of a user experiment, PatternSight can effectively assist chart authors in optimizing chart design for representing data patterns.