Human-centered evaluation of data visualization understanding in vision-language models
Researchers compared eight vision-language models against human performance on six diverse data visualization understanding tests. The core finding is that these AI models consistently underperformed humans, even when using lenient evaluation criteria. Furthermore, the error patterns produced by the models were reliably different from those of humans, suggesting opportunities for developing better cognitive AI.