Dual Thinking and Logical Processing -- Are Multi-modal Large Language Models Closing the Gap with Human Vision ?

The dual thinking framework considers fast, intuitive processing and slower, logical processing. The perception of dual thinking in vision requires images where inferences from intuitive and logical processing differ. We introduce an adversarial dataset to provide evidence for the dual thinking framework in human vision, which also aids in studying the qualitative behavior of deep learning models. The evidence underscores the importance of shape in identifying instances in human vision. Our psychophysical studies show the presence of multiple inferences in rapid succession, and analysis of errors shows the early stopping of visual processing can result in missing relevant information. Our study shows that segmentation models lack an understanding of sub-structures, as indicated by errors related to the position and number of sub-components. Additionally, the similarity in errors made by models and intuitive human processing indicates that models only address intuitive thinking in human vision. In contrast, multi-modal LLMs, including open-source models, demonstrate tremendous progress on errors made in intuitive processing. The models have improved performance on images that require logical reasoning and show recognition of sub-components. However, they have not matched the performance improvements made on errors in intuitive processing.