  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 longitudinal
qualitative study involving 4 professional visualization designers that used
the tool over a few days to design a new visualization.
