Noise correlations in the human brain and their impact on pattern classification
Fig 2
Noise correlations and MVPA decoding.
(A) Classification accuracy was better for patterns of activity over voxels with high (top 1%) vs. low (bottom 1%) noise correlations in the raw distribution, and the positive values from the raw distribution; the same pattern held for negative values from the raw distribution, but with high and low defined as the top and bottom 6%, respectively (to accommodate the smaller sample of negative correlations). Columns represent means and error bars represent SEM across participants. The number below each column is the average noise correlation, across the voxels in the selected set and across all participants, provided for descriptive purposes. The dashed gray line denotes the baseline “chance” level of classification accuracy obtained by permuting the class labels 10,000 times. The classifier was trained on three classes (face, scene, and blank), but chance is not 33% because there were more blank samples. (B) Classification accuracy improved monotonically with an increase in the magnitude of noise correlations. The solid purple line represents mean classification accuracy in every percentile of voxels, and the ribbon represents SEM across participants. The solid gray line represents mean noise correlations in every percentile (for descriptive purposes, as this was the basis of sorting), and the ribbon represents SEM across participants. The dashed purple line denotes the empirically defined chance level of classification accuracy obtained from the permutation analysis. ***p < 0.001, **p < 0.01.