Data visualization understanding, which requires integrating visual, numerical, and linguistic inputs, poses fundamental challenges to multimodal reasoning. Recently developed vision-language models (VLMs) are promising candidates for modeling the computations that underlie human data visualization understanding. However, whether they are useful as scientific models depends not only on whether they produce correct answers, but also whether they approximate the patterns of responses that people produce on the same tasks. This distinction has been ignored in recent evaluations of VLMs, which typically only report overall model performance (accuracy) without direct comparison to human behavioral patterns. Here we introduce a human-centered evaluation protocol that compares VLM and human responses on the same set of visualization understanding items. We apply this protocol to eight VLMs across six assessments: five established tests of human visualization literacy, plus one designed specifically for evaluating VLMs. Models performed worse than humans on average, and this gap persisted even under lenient criteria. Moreover, while relative performance across items was somewhat correlated between models and humans, all models produced error patterns that were reliably distinct from those of humans. We envision our evaluation protocol helping to identify artificial systems that might serve as promising scientific cognitive models of how humans reason about data visualizations.

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