论文标题
拥挤揭示了人类和机器中本地处理与全球处理的根本差异
Crowding Reveals Fundamental Differences in Local vs. Global Processing in Humans and Machines
论文作者
论文摘要
前馈卷积神经网络（FFCNN）已成为计算机视觉和神经科学中的最新模型。但是，FFCNN的人类表现不一定意味着类似人类的计算。先前的研究表明，当前的FFCNN不利用全球形状信息。但是，目前尚不清楚这是否反映了FFCNN和人类加工之间的基本差异，还是仅仅是对FFCNN的训练方式的伪像。在这里，我们将视觉拥挤作为一个控制良好的特定探针来测试全局形状计算。我们的结果提供了证据表明，出于原则上的建筑原因，FFCNN无法产生类似人类的全球形状计算。我们阐述了可能解决FFCNNS的缺点以提供人类视觉系统的更好模型的方法。
Feedforward Convolutional Neural Networks (ffCNNs) have become state-of-the-art models both in computer vision and neuroscience. However, human-like performance of ffCNNs does not necessarily imply human-like computations. Previous studies have suggested that current ffCNNs do not make use of global shape information. However, it is currently unclear whether this reflects fundamental differences between ffCNN and human processing or is merely an artefact of how ffCNNs are trained. Here, we use visual crowding as a well-controlled, specific probe to test global shape computations. Our results provide evidence that ffCNNs cannot produce human-like global shape computations for principled architectural reasons. We lay out approaches that may address shortcomings of ffCNNs to provide better models of the human visual system.
