Understanding multivariate brain activity: Evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging
Fig 11
A three-voxel simulation illustrating the disproportional benefits of good covariance in multivariate decoding.
Panel A illustrates the trial-by-trial responses of voxels X and Y towards two stimuli. The covariance structure of X and Y enhances classification accuracy. Similarly, panel B illustrates that the covariance structure of voxels Y and Z impairs classification. Voxels X and Z have no systematic NC (panel C). Panel D depicts the classification accuracy based on population responses of X and Y, Y and Z, X and Z, and all three units. We also include a situation where we set all NCs among three units to 0 and keep other settings the same (i.e., X, Y&Z without NC). We add this condition because in most empirical scenarios we are interested in comparing a population code with and without NCs. The beneficial and detrimental effects of the covariance structures in panels A and B do not cancel each other if all three voxels are combined.