Experiment 2 model performance split by category level.

Models pre-trained on grayscale ecoset images with various blur conditions were fine-tuned for 1000-way image classification on ImageNet; the performance of these models is shown separately for the basic-level and subordinate-level category labels in ImageNet. Only results from models pre-trained with grayscale images are plotted because few differences were observed between models pre-trained with color images (see Fig 3). Validation set accuracy was computed using (A) all categories, (B) basic-level labeled categories only, and (C) subordinate-level labeled categories only. Bar heights and error bars indicate mean ± SEM across 10 trials of each model, light gray dots show accuracy for individual trials. Brackets above bars indicate the significance of pairwise comparisons between conditions, assessed using a two-tailed independent samples t-test for each pairwise condition comparison (FDR corrected, ɑ = 0.05) where * denotes a significant difference in either direction and n.s. denotes no significant difference.