High-resolution imaging of the fusiform face area (FFA) using multivariate non-linear classifiers shows diagnosticity for non-face categories

► The FFA shows a heterogeneous and distributed selectivity code for 4 categories. ► Faces do not have an advantage according to non-linear classification analysis. ► Multivariate non-linear classifiers detect information that standard analyses do not. ► A linear bias for faces can explain past contradictions about the nature of the FFA.