Fig. 2. Architecture variation.

Degree of brain predictivity (r_Pearson) is plotted for the controlled set of convolutional neural networks (CNNs) and transformer models in our survey. Each small box corresponds an individual model. The horizontal midline of each box indicates the mean score of each model’s most brain-predictive layer (selected by cross-validation) across the 4 subjects, with the height of the box indicating the grand-mean-centered 95% bootstrapped confidence intervals (CIs)¹³⁷ of the model’s score across subjects. The cRSA score is plotted in open boxes, and the veRSA score is plotted in filled boxes. For each class of model architecture (convolutional, transformer) the class mean is plotted as a striped horizontal ribbon. The width of this ribbon reflects the 95% grand-mean-centered bootstrapped 95% CIs over the mean score for all models in a given set. The noise ceiling of the occipitotemporal brain data is plotted in the gray horizontal ribbon at the top of the plot, and reflects the mean of the noise ceilings computed for each individual subject. The secondary y-axis shows explainable variance explained (the squared model score, divided by the squared noise ceiling). Source data are provided as a Source Data file.