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The representation of shape and texture in category-selective regions of ventral-temporal cortex.

David D CogganDavid M WatsonAo WangRobert BrownbridgeChristopher EllisKathryn JonesCharlotte KilroyTimothy J Andrews
Published in: The European journal of neuroscience (2022)
Neuroimaging studies using univariate and multivariate approaches have shown that the fusiform face area (FFA) and parahippocampal place area (PPA) respond selectively to images of faces and places. The aim of this study was to determine the extent to which this selectivity to faces or places is based on the shape or texture properties of the images. Faces and houses were filtered to manipulate their texture properties, while preserving the shape properties (spatial envelope) of the images. In Experiment 1, multivariate pattern analysis (MVPA) showed that patterns of fMRI response to faces and houses in FFA and PPA were predicted by the shape properties, but not by the texture properties of the image. In Experiment 2, a univariate analysis (fMR-adaptation) showed that responses in the FFA and PPA were sensitive to changes in both the shape and texture properties of the image. These findings can be explained by the spatial scale of the representation of images in the FFA and PPA. At a coarser scale (revealed by MVPA), the neural selectivity to faces and houses is sensitive to variation in the shape properties of the image. However, at a finer scale (revealed by fMR-adaptation), the neural selectivity is sensitive to the texture properties of the image. By combining these neuroimaging paradigms, our results provide insights into the spatial scale of the neural representation of faces and places in the ventral-temporal cortex.
Keyphrases
  • deep learning
  • convolutional neural network
  • contrast enhanced
  • machine learning
  • computed tomography