Dual Thinking and Logical Processing in Human Vision and Multimodal Large Language Models

Summary

Researchers built a novel adversarial dataset to test the dual thinking framework, which separates fast intuitive processing from slower logical processing, in human vision and in deep learning models. Psychophysical studies showed multiple inferences in rapid succession, and errors arose when early stopping of visual processing missed relevant information. The authors report that multimodal large language models and vision language models have improved more on intuitive processing than on logical processing, while segmentation models lack understanding of substructures.