Proceedings of the Annual Meeting of the Cognitive Science Society

Preferential activation to faces in the brain’s fusiform gyrus hasled to the proposed existence of a face module termed theFusiform Face Area (FFA) (Kanwisher et. al, 1997). However,arguments for distributed, topographical object-formrepresentations in FFA and across visual cortex have beenproposed to explain data showing that FFA activation patternscontain decodable information about non-face categories(Haxby et. al, 2001; Hanson & Schmidt, 2011). Using two deepconvolutional neural network models able to perform human-level object and facial recognition, respectively, wedemonstrate that both localized category representations(LCRs) and high-level face-specific representations allow forsimilar decoding accuracy between non-preferred visualcategories as between a preferred and non-preferred category.Our results suggest that neuroimaging of a cortical “module”optimized for face processing should yield significantdecodable information for non-face categories so long asrepresentations within the module are activated by non-facestimuli.