Recent studies have demonstrated that classification algorithms applied to human fMRI data can decode information segregated in cortical columns, although the voxel-size was large relative to the width of columns. The mechanism by which low-resolution imaging decodes information represented at higher resolution is not clear. We show that using GE-fMRI signals, the mechanism underlying the decoding signals involves contributions from both gray matter and macroscopic blood vessels. We hypothesize that draining regions biased towards columns with preference to one eye underlie the specificity of vessels. Decoding at high-resolution is superior to low-resolution when applied to data from small cortical volumes.