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A Stevens's Power Law Check-up of GPT-5.5's Image-Based Visualization Reading

2026-10-06 13:52:34
Kaichun Yang, Jian Chen
arXiv_CV
     
Abstract

We adapt Stevens's power law to measure the innate ability of AI models to read visualizations, which can reveal the built-in perceptual mechanisms of algorithmic models. In our pilot study, models see no legend. A model first views a reference visual representation and estimates its magnitude, then estimates the magnitude of each subsequent image of the same representation relative to that reference. Our evaluation of twelve visual variables makes how algorithmic models read visual encodings measurable, comparable with human perception, and more interpretable to humans.

Abstract (translated)

URL

https://arxiv.org/abs/2610.08365

PDF

https://arxiv.org/pdf/2610.08365.pdf

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