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This research paper investigates the convergence of artificial intelligence models with the human brain's visual processing, specifically using DINOv3 self-supervised vision transformers. It aims to disentangle the factors influencing this brain-model similarity, such as model architecture, training methodology, and data type. The authors utilize fMRI and MEG brain recordings to compare the AI models' representations, employing three key metrics: overall representational similarity (encoding score), topographical organization (spatial score), and temporal dynamics (temporal score). The study finds that larger models, extended training, and human-centric image data all contribute significantly to achieving higher brain-similarity scores, with brain-like representations emerging in a specific chronological order during training that aligns with the human brain's developmental and structural properties.

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