Indeed, we find that encoding models based on the categories learned

from natural scenes provide PD0325901 mw significantly better predictions of brain activity than do encoding models based on the null categories and for all subjects (p < 1 × 10−10 for all subjects, Wilcox rank-sum test for differences in median prediction accuracy across all cortical voxels and candidate scene category settings; subject S1: W(15,025,164) = 9.96 × 1013; subject S2: W(24,440,399) = 3.04 × 1014; subject S3: W(15,778,360) = 9.93 × 1013; subject S4: W(14,705,625) = 1.09 × 1014). In a set of supplemental analyses, we also compared the LDA-based models to several other plausible models of scene category representation. We find that the LDA-based models provide superior prediction accuracy to all these alternative models (see Figures S12–S15). These results support our central hypothesis that the human brain encodes categories that reflect the co-occurrence statistics of objects in natural scenes. Previous fMRI studies have identified functional regions of interest (ROIs) tuned to very broad scene categories, such as places (Epstein and Kanwisher, 1998), as well as to narrow object categories such as faces (Kanwisher et al., 1997) or body parts (Downing et al., 2001). Can selectivity in these

regions be explained in terms of the categories learned from natural scene object statistics? We evaluated scene category tuning for voxels located within the boundaries of several conventional functional ROIs: the fusiform face area (FFA; Kanwisher et al., 1997), the see more occipital face area (OFA; Gauthier et al., 2000), the extrastriate body area (EBA; Downing et al., 2001), the parahippocampal place area (PPA; Epstein and Kanwisher, 1998), the transverse occipital sulcus (TOS; Nakamura et al., 2000, Grill-Spector, 2003 and Hasson et al., 2003), the retrosplenial cortex (RSC; Maguire, 2001), and lateral occipital cortex (LO; Malach et al., 1995). Figure 3A shows the boundaries of these ROIs, identified using separate functional localizer experiments, and projected on the cortical flat map of one

representative subject. The color of each location on the cortical map indicates the prediction accuracy of the corresponding encoding model. All encoding models were based Telomerase on the 20 best scene categories identified across subjects. These data show that the encoding models accurately predict responses of voxels located in many ROIs within anterior visual cortex. To quantify this effect, we calculated the proportion of response variance explained by the encoding models, averaged across all voxels within each ROI. We find that the average proportion of variance explained to be significantly greater than chance for every anterior visual cortex ROI and for all subjects (p < 0.01; see Experimental Procedures for details).