Geometric structure of features underlies human VTC object recognition.

The ventral temporal cortex (VTC) plays a crucial role in human object recognition. Within VTC neural space, object-specific domains align with domain-general features, e.g. face- and scene-domain align with the feature distribution of animacy, generating a hierarchical knowledge structure for categorization. However, the neural process of integrating information from VTC to distinguish different objects remains unclear. Here, we employed a combination of ANN modeling, functional MRI, and MEG to investigate how VTC features affect object manifold separability. The representational geometry analysis shows that domain-general features in VTC form a unique structure, different from ANN, to assist object classification. Moreover, VTC dynamically adjusts the geometrical relationship of these features during object recognition, influencing the geometrical properties of object manifolds and, consequently, their separability. These findings advance our understanding of the neural computation involved in VTC object recognition, revealing how downstream neurons can flexibly access category information in diverse recognition tasks.