Perceptual Pat: A Virtual Human System for Iterative Visualization Design
Sungbok Shin, Sanghyun Hong, Niklas Elmqvist

TL;DR
Perceptual Pat is an AI-driven virtual system that analyzes visualization snapshots to support iterative design by providing critique reports, enabling designers to improve visualizations efficiently without external feedback.
Contribution
This paper introduces Perceptual Pat, a novel extensible AI system that automates visualization critique and supports iterative design through analysis and version tracking.
Findings
Validated through a longitudinal study with professional designers
Enabled effective iterative visualization improvement
Provided actionable critique reports
Abstract
Designing a visualization is often a process of iterative refinement where the designer improves a chart over time by adding features, improving encodings, and fixing mistakes. However, effective design requires external critique and evaluation. Unfortunately, such critique is not always available on short notice and evaluation can be costly. To address this need, we present Perceptual Pat, an extensible suite of AI and computer vision techniques that forms a virtual human visual system for supporting iterative visualization design. The system analyzes snapshots of a visualization using an extensible set of filters - including gaze maps, text recognition, color analysis, etc - and generates a report summarizing the findings. The web-based Pat Design Lab provides a version tracking system that enables the designer to track improvements over time. We validate Perceptual Pat using a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Visualization and Analytics
