  Convolutional neural networks (CNNs) are one of the most successful computer
vision systems to solve object recognition. Furthermore, CNNs have major
applications in understanding the nature of visual representations in the human
brain. Yet it remains poorly understood how CNNs actually make their decisions,
what the nature of their internal representations is, and how their recognition
strategies differ from humans. Specifically, there is a major debate about the
question of whether CNNs primarily rely on surface regularities of objects, or
whether they are capable of exploiting the spatial arrangement of features,
similar to humans. Here, we develop a novel feature-scrambling approach to
explicitly test whether CNNs use the spatial arrangement of features (i.e.
object parts) to classify objects. We combine this approach with a systematic
manipulation of effective receptive field sizes of CNNs as well as minimal
recognizable configurations (MIRCs) analysis. In contrast to much previous
literature, we provide evidence that CNNs are in fact capable of using
relatively long-range spatial relationships for object classification.
Moreover, the extent to which CNNs use spatial relationships depends heavily on
the dataset, e.g. texture vs. sketch. In fact, CNNs even use different
strategies for different classes within heterogeneous datasets (ImageNet),
suggesting CNNs have a continuous spectrum of classification strategies.
Finally, we show that CNNs learn the spatial arrangement of features only up to
an intermediate level of granularity, which suggests that intermediate rather
than global shape features provide the optimal trade-off between sensitivity
and specificity in object classification. These results provide novel insights
into the nature of CNN representations and the extent to which they rely on the
spatial arrangement of features for object classification.
