From what scale of representation does multivariate pattern analysis decode information?

Editor's Note: These short, critical reviews of recent papers in the Journal, written exclusively by graduate students or postdoctoral fellows, are intended to summarize the important findings of the paper and provide additional insight and commentary. For more information on the format and purpose of the Journal Club, please see Review of Swisher et al. Functional magnetic resonance imaging (fMRI) is severely limited in the extent of detail that it can reveal. In practice, most fMRI studies investigate large areas sub-tending centimeters, such as the primary visual cortex or face-selective regions. Electrophysiological recordings, however, have demonstrated much smaller scales of organization, for example, columns coding only for specific orientations within the primary visual cortex. These orientation columns are substantially smaller than the conventionally available fMRI resolution of 3 ϫ 3 mm, so it was assumed that fMRI would be insensitive to these finely spaced orientation columns. Nonetheless, by using multivariate analysis techniques, Kamitani and Tong (2005) demonstrated that it is possible to decode orientation information in the human brain even with conventional fMRI resolution. These multivariate techniques combine the information of multiple vox-els (Haxby et al., 2001), thereby revealing a sensitivity to different orientations, even though individual voxels provide only a very weak sensitivity to this distinction. This sensitivity was argued to result from an unequal distribution of different orientation columns within each voxel, providing a subtle bias that multivariate techniques can exploit to discriminate between orienta-tions [for review, see Norman et al. (2006) or Haynes and Rees (2006)]. However, given that there are also larger scales of organization within the primary visual cortex, such as those deriving from a preference for radial orientations (Sasaki et al., 2006) or the overrepresentation of cardinal orientations (Furmanski and Engel, 2000), is it plausible to suppose that the demonstrated decoding depends on fine-scale columnar organization rather than these larger structures? A possible role for such larger scales of organization was suggested by the demonstration by Op de Beeck (2010) that the decoding of orientation was unaffected by large-scale (8 mm) smoothing. If orientation decoding really depends on fine-scale variability in the distribution of columns, it is unclear how this signal could still be detected when blurred via smoothing. Swisher et al. (2010) attempted to resolve this question regarding the scale of representation that contributes to a successful orientation classification by testing classification performance with high-resolution fMRI in cats (9.4 T) and humans (7 …