Functional neural imaging studies have identified a network of brain areas that are more active to faces than to other objects. However, it remains largely unclear how these areas encode individual facial identity. To investigate the neural representations of facial identity, we constructed a multidimensional face space structure, whose dimensions were derived from geometric information of faces using the Principal Component Analysis (PCA). Using fMRI, we recorded participants' neural responses when viewing blocks of faces that differed only on one dimension within a block. Although the response magnitudes to different blocks of faces did not differ in a univariate analysis, multi-voxel pattern analysis revealed distinct patterns related to different face space dimensions in brain areas that have a higher response magnitude to faces than to other objects. The results indicate that dimensions of the face space are encoded in the face-selective brain areas in a spatially distributed way.
Keywords: Face space dimension; Facial identity; Multi-voxel pattern analysis; PCA; fMRI.
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