Fig. 4. Input variation.

Degree of brain predictivity (r_Pearson) is plotted for the sets of models with controlled variation in input diet. A The first set of models shows scores across paired model architectures trained either on ImageNet1K or ImageNet21K (a ∼13× increase in number of training images). B The second set of models shows scores across 4 variants of a self-supervised IPCL-AlexNet model trained on different image datasets. Each small box corresponds an individual model. In all subplots, 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) of the model’s score across subjects. The cRSA score is plotted in open boxes, and the veRSA score is plotted in filled boxes. The class mean for each distinct set of models is plotted in striped horizontal ribbons across the individual models. The width of this ribbon reflects the 95% grand-mean-centered bootstrapped 95% CIs over the mean score for all models in this 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.