BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain
Understanding how the human brain represents visual concepts, and in which brain regions these representations are encoded, remains a long-standing challenge. Decades of work have advanced our understanding of visual representations, yet brain signals remain large and complex, and the space of possible visual concepts is vast. As a result, most studies remain small-scale, rely on manual inspection, focus on specific regions and properties, and rarely include systematic validation. We present a large-scale, automated framework for discovering and explaining visual representations across the human cortex. Our method comprises two main stages. First, we discover candidate interpretable patterns in fMRI activity through unsupervised, data-driven decomposition methods. Next, we explain each pattern by identifying the set of natural images that most strongly elicit it and generating a natural-language description of their shared visual meaning. To scale this process, we introduce an automated pipeline that tests multiple candidate explanations, assigns quantitative reliability scores, and selects the most consistent description for each voxel pattern. Our framework reveals thousands of interpretable patterns spanning many distinct visual concepts, including fine-grained representations previously unreported.
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Navve Wasserman
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2025-12-11T10:07:40.033Z
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navve wasserman
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Matias Cosarinsky
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2025-12-11T10:45:36.526Z
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Matias Cosarinsky
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mcosarinsky
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Yuval Golbari
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2025-12-11T10:45:41.956Z
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Yuval Golbari
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Aude Oliva
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Antonio Torralba
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2025-12-11T10:45:57.490Z
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Antonio
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Tamar Rott Shaham
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2025-12-11T10:46:03.740Z
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Tamar Rott Shaham
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Michal Irani
BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain
Understanding how the human brain represents visual concepts, and in which brain regions these representations are encoded, remains a long-standing challenge. Decades of work have advanced our understanding of visual representations, yet brain signals remain large and complex, and the space of possible visual concepts is vast. As a result, most studies remain small-scale, rely on manual inspection, focus on specific regions and properties, and rarely include systematic validation. We present a large-scale, automated framework for discovering and explaining visual representations across the human cortex. Our method comprises two main stages. First, we discover candidate interpretable patterns in fMRI activity through unsupervised, data-driven decomposition methods. Next, we explain each pattern by identifying the set of natural images that most strongly elicit it and generating a natural-language description of their shared visual meaning. To scale this process, we introduce an automated pipeline that tests multiple candidate explanations, assigns quantitative reliability scores, and selects the most consistent description for each voxel pattern. Our framework reveals thousands of interpretable patterns spanning many distinct visual concepts, including fine-grained representations previously unreported.
An automated framework uses unsupervised decomposition and natural language descriptions to identify and explain visual representations in human brain fMRI data.
fMRI unsupervised decomposition natural images natural-language descriptions voxel patterns