Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

中文翻译：

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功能和深度神经网络特征对人脑和行为中场景的表征相似性的不同贡献

现实世界场景中视觉和语义特征之间的内在相关性使得很难确定不同的场景属性如何对神经表征做出贡献。在这里，我们通过将人类行为和大脑测量中解释的方差划分为三个特征模型来评估多个属性对场景表示的贡献，这些模型的相互关联通过刺激预选先验地最小化。场景相似性的行为评估反映了功能特征模型的独特贡献，该模型指示场景中的潜在动作以及来自深度神经网络 (DNN) 的高级视觉特征。相比之下，场景选择性区域中皮层反应的相似性仅由中级和高级 DNN 特征唯一解释，而对象标签模型对这两个领域都没有唯一贡献。功能特征和 DNN 特征在对场景的行为和大脑表示的贡献中的显着分离表明，场景选择性皮层仅代表与行为相关的场景信息的一个子集。