Noise correlations in the human brain and their impact on pattern classification
Fig 9
Classification accuracy improved monotonically with an increase in the magnitude of heterogeneous noise correlations in simulated populations of face- and scene-selective voxels. Solid lines represent mean classification accuracy as the magnitude of noise correlations increased, with all other parameters fixed. Ribbons represent SEM across model participants. (A) Overall classification accuracy dropped as voxel selectivity decreased. However, across all selectivity profiles, classification accuracy improved monotonically with an increase in the magnitude of noise correlations. (B) Overall classification accuracy dropped as voxel variance increased. However, across all levels of variance, classification accuracy improved monotonically with an increase in the magnitude of noise correlations. (C) Increasing diversity in the response properties of individual voxels within the simulated face- and scene-selective populations did not qualitatively change the pattern of results. Indeed, increasing population diversity led to a steeper improvement in classification accuracy as a function of noise correlations. See Methods for the parameter values used in each simulation.