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Skyseeker fs12/11/2022 ![]() ![]() Humans can extract a wealth of information, including spatial structure, semantic category, and the identity of embedded objects, from images viewed for fewer than 100 msecs. Visual understanding of real-world scenes is near-instantaneous. These results are consistent with both low-level models of scene categorization and models that build categories around a prototype. ![]() A simulation of our neuroimaging experiment suggests that such a difference in variance could account for the observed differences in decoding accuracy. Analysis of the image statistics of our good and bad exemplars shows that variability in low-level features and image structure is higher among bad than good exemplars. These results provide further evidence that V1, RSC and the PPA not only contain information relevant for natural scene categorization, but their activity patterns mirror the fundamentally graded nature of human categories. This difference in decoding accuracy cannot be explained by differences in overall BOLD signal, as average BOLD activity was either equivalent or higher for bad than good scenes in these areas. A multi-voxel pattern classifier trained to discriminate among category blocks showed higher decoding accuracy for good than bad exemplars in the PPA, RSC and V1. In an fMRI experiment participants passively viewed blocks of good or bad exemplars from the same six categories. In a behavioral experiment participants were more accurate and faster at categorizing good than bad exemplars of natural scenes. Here we first confirmed that humans could categorize “good” exemplars better than “bad” exemplars of six scene categories and then explored whether brain regions previously implicated in natural scene categorization showed a similar sensitivity to how well an image exemplifies a category. Within the range of images that we might categorize as a “beach”, for example, some will be more representative of that category than others. ![]()
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