Eye of the Beholder: Towards Measuring Visualization Complexity

TL;DR

This study investigates how humans perceive visualization complexity and evaluates methods to automatically estimate it, finding that large language models like GPT-4 can effectively predict perceived complexity with minimal effort.

Contribution

The paper introduces a crowdsourced dataset of human complexity ratings and demonstrates that zero-shot LLMs can accurately predict perceived visualization complexity.

Findings

Image complexity metrics do not correlate with human perception.

Manual coding yields reasonable predictions but is effort-intensive.

GPT-4 effectively predicts perceived complexity using zero-shot prompting.

Abstract

Constructing expressive and legible visualizations is a key activity for visualization designers. While numerous design guidelines exist, research on how specific graphical features affect perceived visual complexity remains limited. In this paper, we report on a crowdsourced study to collect human ratings of perceived complexity for diverse visualizations. Using these ratings as ground truth, we then evaluated three methods to estimate this perceived complexity: image analysis metrics, multilinear regression using manually coded visualization features, and automated feature extraction using a large language model (LLM). Image complexity metrics showed no correlation with human-perceived visualization complexity. Manual feature coding produced a reasonable predictive model but required substantial effort. In contrast, a zero-shot LLM (GPT-4o mini) demonstrated strong capabilities in both rating complexity and extracting relevant features. Our findings suggest that visualization complexity is truly in the eye of the beholder, yet can be effectively approximated using zero-shot LLM prompting, offering a scalable approach for evaluating the complexity of visualizations. The dataset and code for the study and data analysis can be found at https://osf.io/w85a4/