BrainCause:从激活到因果--发现人脑中的因果视觉表征
阅读原文· arxiv.orgBrainCause 是一个自动化框架,结合生成模型与脑模型,通过合成受控刺激并进行因果测试来验证人脑中的神经表征。给定目标概念,框架构建由概念图像、去除目标概念的反事实编辑图像及相关干扰项组成的刺激集,利用图像到 fMRI 编码模型预测脑反应,识别对目标概念具有特异性的表征。该方法在预测和实测 fMRI 数据上成功复现已知功能定位,并发现数十个概念的新候选表征。关键结论:仅凭激活强度不足以证明表征存在,缺乏因果验证会导致大量假阳性定位。
Identifying which brain regions represent a visual concept in the human brain is a central challenge in neuroscience. Existing approaches have localized coarse functional regions (e.g., faces, places) through activation maximization, identifying regions that activate strongly for a target concept relative to other concepts. Yet strong activation alone does not establish that a region represents the concept itself, as responses may instead be driven by correlated visual or semantic cues. We introduce BrainCause, an automated framework that combines generative and brain models to synthesize controlled stimuli and validate neural representations through targeted causal testing. Given a query specifying a concept of interest, our framework constructs targeted stimulus sets comprising concept images, counterfactual edits that remove the target concept while preserving other image content, and images with candidate correlated distractors. It then uses an image-to-fMRI encoding model to predict brain responses and searches for representations that respond specifically to the target concept over correlated alternatives. BrainCause returns validated candidate representations and proposes follow-up fMRI experiments to further test or extend its discoveries. Our approach successfully recovers known functional localizations and identifies new candidate representations across dozens of concepts, validated on both predicted and measured fMRI data. Critically, we show that without causal validation, a large fraction of localizations would be false positives, confirming that activation alone is insufficient evidence of representation.