标题： 深度神经网络是否在人类-物体交互的神经表征中模拟非线性组合性？ 显示英文标题

标题： Do Deep Neural Networks Model Nonlinear Compositionality in the Neural Representation of Human-Object Interactions?

摘要： 视觉场景理解通常需要处理人与物体的交互。 在这里，我们试图探讨深度神经网络（DNN）模型在多大程度上能够捕捉到类似于大脑对人类、物体及其交互的表示特征。 我们研究处理人类、物体或交互特定信息的脑区，并建立它们与DNN特征之间的对应关系。 我们的结果表明，我们可以利用DNN表示来推断这些区域对特定视觉刺激的选择性。 我们还将DNN的特征映射到这些脑区，从而将DNN表示与视觉皮层特定部分中发现的表示联系起来。 特别是，我们的结果表明，典型的DNN表示包含对人-物体交互的组合信息编码，这超出了两个组成部分编码的线性组合，因此表明DNN可能能够模拟生物视觉的这一重要特性。 显示英文摘要

摘要： Visual scene understanding often requires the processing of human-object interactions. Here we seek to explore if and how well Deep Neural Network (DNN) models capture features similar to the brain's representation of humans, objects, and their interactions. We investigate brain regions which process human-, object-, or interaction-specific information, and establish correspondences between them and DNN features. Our results suggest that we can infer the selectivity of these regions to particular visual stimuli using DNN representations. We also map features from the DNN to the regions, thus linking the DNN representations to those found in specific parts of the visual cortex. In particular, our results suggest that a typical DNN representation contains encoding of compositional information for human-object interactions which goes beyond a linear combination of the encodings for the two components, thus suggesting that DNNs may be able to model this important property of biological vision.