The neural representation of the fake objects

Earlier research suggested that the IT cortex's functional structure can be understood through an object space model with DCNN. However, category-specific regions in the IT cortex, such as areas dedicated to faces and bodies, imply that its organization might also be based on semantic categories. To distinguish between these two hypotheses, we used fMRI to measure human subjects' responses to artificial images, referred to as “fake objects”, which were generated with GAN and lacked semantic category information. We projected these generated fake objects onto the PC1-PC2 space, built with the fMRI responses to 500 real objects. We chose 100 fake objects based on their projections onto the space, resulting in a ring-like structure. Subjects were instructed to perform three tasks in separate scans: two image categorization tasks based on the images' projection onto the two orthogonal axes in the object space and a fixation color discrimination task. The study's results show that the IT cortex can be effectively modulated by these fake objects, and the modulation of each voxel can be accurately represented by the object space model as the projection on the preferred axis. This holds true even for voxels located in category-selective regions, such as the FFA and EBA. Furthermore, the preferred axis of each voxel in the IT cortex remained consistent across the three tasks, although the absolute selectivity decreased in the fixation task. Additionally, the modulation of the two different image categorization tasks was more noticeable in the frontal and parietal cortex. Our results demonstrate that the functional organization of the IT cortex can be better explained by the object space model than the semantic model, and the representation of object space is relatively stable across different tasks, whose outputs can be read out by the later stages of the brain.