Fig 8. Model predictions of brain responses are better for models trained in background noise.

(A) Effect of noise in training on model-brain similarity assessed via regression. Using regression, explained variance was measured for each voxel and the aggregated median variance explained was obtained for the best-predicting stage for each model, selected using independent data. Grey line shows variance explained by the SpectroTemporal baseline model. Colors indicate the nature of the model architecture with CochCNN9 architectures in shades of red, and CochResNet50 architectures in shades of green. Models trained in the presence of background noise are shown in the same color scheme as in Fig 2; models trained with clean speech are shown with hashing. Error bars are within-participant SEM. For both datasets, the models trained in the presence of background noise exhibit higher model-brain similarity than the models trained without background noise. (B) Effect of noise in training on model-brain representational similarity. Same conventions as (A), except that the dashed black line shows the noise ceiling measured by comparing one participant’s RDM with the average of the RDMs from each of the other participants. Error bars are within-participant SEM. Data and code with which to reproduce results are available at https://github.com/gretatuckute/auditory_brain_dnn.