Graphical Perception Across Encoding Channels in Multimodal LLMs

Abstract

Visual encoding channels are ranked for human observers by how accurately people decode quantities from them, and that ranking underpins decades of design guidance. We ask whether it carries over to multimodal LLMs. We replicate the Cleveland–McGill and Heer–Bostock graphical perception experiments across six models from three capability tiers under six prompting conditions, and extend the task set with two color-based channels. We report three findings. The geometric channels are interchangeable, with no significant pairwise difference, consistently across models and prompts. Luminance and saturation are not usable quantitative channels at all: errors exceed those expected from random responding. A follow-up experiment shows that distractor elements degrade estimation, whereas spatial separation between the compared marks does not, suggesting that for agent-facing charts, the amount of surrounding visual material matters more than the choice of encoding.