A Unified Geometric Space Bridging AI Models and the Human Brain
Silin Chen, Yuzhong Chen, Zifan Wang, Junhao Wang, Zifeng Jia, Keith M Kendrick, Tuo Zhang, Lin Zhao, Dezhong Yao, Tianming Liu, and Xi Jiang

TL;DR
This paper introduces a unified geometric space called Brain-like Space that allows for the comparison of AI models and human brain organization across different modalities and tasks, revealing a continuum of brain-likeness.
Contribution
It proposes a novel geometric framework to map and compare AI models' intrinsic organization with human brain networks across modalities and tasks.
Findings
A continuous arc-shaped geometry reflects increasing brain-likeness in models.
Model brain-likeness correlates with pretraining paradigms and encoding schemes.
Brain-likeness does not directly predict downstream task performance.
Abstract
For decades, neuroscientists and computer scientists have pursued a shared ambition: to understand intelligence and build it. Modern artificial neural networks now rival humans in language, perception, and reasoning, yet it is still largely unknown whether these artificial systems organize information as the brain does. Existing brain-AI alignment studies have shown the striking correspondence between the two systems, but such comparisons remain bound to specific inputs and tasks, offering no common ground for comparing how AI models with different kinds of modalities-vision, language, or multimodal-are intrinsically organized. Here we introduce a groundbreaking concept of Brain-like Space: a unified geometric space in which every AI model can be precisely situated and compared by mapping its intrinsic spatial attention topological organization onto canonical human functional brain…
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