Multimodal Large Language Models Naturally Develop Human-like Object Concept Representations

The referenced articles explore various aspects of human image understanding, categorization, and the neural basis of visual perception. Biederman’s “Recognition-by-components” theory emphasizes how shape components aid object recognition. Subsequent studies, such as those by Edelman and Goldstone, delve into the relationship between similarity and categorization, proposing frameworks for cognitive processes. Research by Rosch and others discusses natural categories and basic objects, while Mahon and Caramazza examine cognitive neuropsychology’s insights into concepts and categories. Emerging studies focus on the interplay between deep learning models and human cognition, such as capturing human categorization and object representation. Cross-disciplinary research investigates the alignment of visual and linguistic representations in the human brain and large language models. Ultimately, these works form a cohesive body of literature that informs our understanding of cognitive processes, combining insights from psychology, neuroscience, and artificial intelligence.

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