125, selleck chemicals SD = 0.079) compared with attend-face trials trials (M = 0.485, SD = 0.248), t6 = −4.84,

P = 0.0028. This shows that category-specific voxels responded strongly to the preferred category than to the non-preferred category. Anatomical grouping of voxels used by the decoder showed that the selected voxels were distributed across 31 distinct brain regions across the subjects (see Fig. S4 for a list of all these regions). Regions not activated in at least three subjects were excluded from further analysis. This left only nine brain regions, as shown in Fig. 4F. These included bilateral fusiform and lingual gyri, right parahippocampal gyrus, left and right inferior occipital lobes, and right middle and superior temporal lobes. Right fusiform gyrus, left and right inferior occipital lobes, and right middle and superior temporal lobes were assigned positive weights and responded

strongly to faces during the localizer task (Fig. 5A). Hence, these were labeled as face-selective regions. Left fusiform gyrus, bilateral lingual gyri and right parahippocampal gyrus were assigned negative weights HSP phosphorylation and were more responsive to place stimuli in the localizer task (Fig. 5B), and therefore labeled as place-selective regions. The classifier weights summed across all subjects for all these regions are shown in Fig. 4G. The MVA-G model not only gave decoding performance similar to that of MVA-W, but also recruited voxels from the same regions as were used in the MVA-W model. While nine regions were used in the MVA-W decoding model,

10 regions were recruited in the MVA-G model (Fig. 6), out of which six were the same as that in the MVA-W decoder. Percent signal change across these regions is shown in Fig. 7. The fact that MVA-G identified a number of different regions compared with MVA-W may be explained by the fact that these regions contain redundant information that is ignored by MVA-W due to the sparseness constraint imposed by the elastic net classifier. ADP ribosylation factor MVA-T also gave above-chance classification performance, though the observed trend was that it was generally lower than MVA-G. Thirty-four distinct clusters were found across the group in the individual GLM. Those clusters that were not activated in three or more subjects were removed from further analysis. Decoding performance for the remaining 12 clusters is summarized in Fig. 8. As stated earlier, the average decoding performance for MVA-C was found to be significantly lower than MVA-W and MVA-G. These results suggest that within each small cluster not much discriminable information is present about the attended category. However, if decoding is extended to multiple brain regions such as that in MVA-W or MVA-G, then distributed patterns of cortical activation can help increase the decoding performance dramatically.