A major principle of human brain organization is “integrating” some regions into networks while “segregating” other sets of regions into separate networks. However, little is known about the cognitive function of the integration and segregation of brain networks. Here, we examined the well-studied brain network for face processing, and asked whether the integration and segregation of the face network (FN) are related to face recognition performance. To do so, we used a voxel-based global brain connectivity method based on resting-state fMRI to characterize the within-network connectivity (WNC) and the between-network connectivity (BNC) of the FN. We found that 95.4% of voxels in the FN had a significantly stronger WNC than BNC, suggesting that the FN is a relatively encapsulated network. Importantly, individuals with a stronger WNC (i.e., integration) in the right fusiform face area were better at recognizing faces, whereas individuals with a weaker BNC (i.e., segregation) in the right occipital face area performed better in the face recognition tasks. In short, our study not only demonstrates the behavioral relevance of integration and segregation of the FN but also provides evidence supporting functional division of labor between the occipital face area and fusiform face area in the hierarchically organized FN.SIGNIFICANCE STATEMENTAlthough the integration and segregation are major principles of human brain organization, little is known about whether they support the cognitive processes. By correlating the within-network connectivity (WNC) and between-network connectivity (BNC) of the face network with face recognition performance, we found that individuals with stronger WNC in the right fusiform face area or weaker BNC in the right occipital face area were better at recognizing faces. Our study not only demonstrates the behavioral relevance of the integration and segregation but also provides evidence supporting functional division of labor between the occipital face area and fusiform face area in the hierarchically organized face network.

Our mind can represent various objects from physical world in an abstract and complex high-dimensional object space, with axes encoding critical features to quickly and accurately recognize objects. Among object features identified in previous neurophysiological and fMRI studies that may serve as the axes, objects’ real-world size is of particular interest because it provides not only visual information for broad conceptual distinctions between objects but also ecological information for objects’ affordance. Here we use deep convolutional neural networks (DCNNs), which enable direct manipulation of visual experience and units’ activation, to explore how objects’ real-world size is extracted to construct the axis of object space. Like the human brain, the DCNNs pre-trained for object recognition also encode objects’ size as an independent axis of the object space. Further, we find that the shape of objects, rather than retinal size, context, task demands or texture features, is critical to inferring objects’ size for both DCNNs and humans. In short, with DCNNs as a brain-like model, our study devises a paradigm supplemental to conventional approaches to explore the structure of object space, which provides computational support for empirical observations on human perceptual and neural representations of objects.

Real-world scenes usually contain a set of cluttered and yet contextually related objects. Here we used fMRI to investigate where and how contextually related multiple objects were represented in the human ventral visual pathway. Specifically, we measured the responses in face-selective and body-selective regions along the ventral pathway when faces and bodies were presented either simultaneously or in isolation. We found that, in the posterior regions, the response for the face and body pair was the weighted average response for faces and bodies presented in isolation. In contrast, the anterior regions encoded the face and body pair in a mutually facilitative fashion, with the response for the pair significantly higher than that for its constituent objects. Furthermore, in the right fusiform face area, the face and body pair was represented as one inseparable object, possibly to reduce perceptual load and increase representation efficiency. Therefore, our study suggests that the visual system uses a hierarchical representation scheme to process multiple objects in natural scenes: the average mechanism in posterior regions helps retaining information of individual objects in clutter, whereas the nonaverage mechanism in the anterior regions uses the contextual information to optimize the representation for multiple objects.