Fig 6. Surface maps of best-predicting model stage.

(A) To investigate correspondence between model stages and brain regions, we plot the model stage that best predicts each voxel as a surface map (FsAverage) (median best stage across participants). We assigned each model stage a position index between 0 and 1 (using minmax normalization such that the first stage is assigned a value of 0 and the last stage a value of 1). We show this map for the 8 best-predicting models as evaluated by the median noise-corrected R² plotted in Fig 2A (see S4 Fig for maps from other models). The color scale limits were set to extend from 0 to the stage beyond the most common best stage (across voxels). We found that setting the limits in this way made the variation across voxels in the best stage visible by not wasting dynamic range on the deep model stages, which were almost never the best-predicting stage. Because the relative position of the best-predicting stage varied across models, the color bar scaling varies across models. For both datasets, middle stages best predict primary auditory cortex, while deep stages best predict non-primary cortex. We note that the B2021 dataset contained voxel responses in parietal cortex, some of which passed the reliability screen. We have plotted a best-predicting stage for these voxels in these maps for consistency with voxel inclusion criteria in the original publication [51], but note that these voxels only passed the reliability screen in a few participants (see panel D) and that the variance explained for these voxels was low, such that the best-predicting stage is not very meaningful. (B) Best-stage map averaged across all models that produced better predictions than the baseline SpectroTemporal model. The map plots the median value across models and thus is composed of discrete color values. The thin black outline plots the borders of an anatomical ROI corresponding to primary auditory cortex. (C) Best-stage map for the same models as in panel B, but with permuted weights. (D) Maps showing the number of participants per voxel location on the FsAverage surface for both datasets (1–8 participants for NH2015; 1–20 participants for B2021). Darker colors denote a larger number of participants per voxel. Because we only analyzed voxels that passed a reliability threshold, some locations only passed the threshold in a few participants. Note also that the regions that were scanned were not identical in the 2 datasets. Data and code with which to reproduce results are available at https://github.com/gretatuckute/auditory_brain_dnn.