BrainExplore：大規模發現人類大腦中可解釋的視覺表徵

摘要

理解人類大腦如何表徵視覺概念，以及這些表徵在哪些腦區被編碼，始終是一項長期存在的挑戰。數十年的研究推進了我們對視覺表徵的認知，然而腦信號依然龐大而複雜，且可能的視覺概念空間極為廣闊。因此，大多數研究仍局限於小規模範疇，依賴人工檢驗，聚焦於特定腦區與屬性，且鮮少包含系統性驗證。本文提出一個大規模自動化框架，用於探索並解釋遍佈人類大腦皮層的視覺表徵。我們的方法包含兩個主要階段：首先，通過無監督的數據驅動分解方法，從功能性磁共振成像活動中發現候選的可解釋模式；接著，通過識別最能強烈激發各模式的自然圖像集，並生成描述其共享視覺意義的自然語言解釋，來闡明每個模式。為實現規模化處理，我們引入自動化流程，測試多個候選解釋、分配量化可靠性評分，並為每個體素模式選擇最一致的描述。該框架揭示了數千個可解釋模式，涵蓋眾多不同的視覺概念，包括以往未被報告的細粒度表徵。

English

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.

BrainExplore：大規模發現人類大腦中可解釋的視覺表徵

BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain

摘要

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