Experiment 1 model performance.

Performance is shown over training for models in six conditions defined by the factors of blur and color. All models used the ResNet-50 architecture and were trained to perform basic-level object categorization using the ecoset image dataset. Each plotted point is an average across 10 runs of an otherwise identical model with different random seeds. Shaded error bars reflect mean ± SEM across these 10 trials. Dots along the top of each plot indicate time points at which a linear mixed effects model over a sliding temporal window revealed a significant effect of the specified pairwise condition comparison, in either direction (FDR corrected, ɑ = 0.05). (A) Validation set accuracy. Gray corresponds to models trained with non-blurred images, blue to models trained with images whose blur decreases linearly over the first 50 epochs (linear-blur condition), and green to models trained with images whose blur decreases according to a logarithmic function over the first 50 epochs (nonlinear-blur condition). Left and right plots show averages for models trained using either grayscale images or color images, respectively. Accuracy was temporally smoothed to reduce noise. (B) Estimated learning rate computed as slope of accuracy over time. Colors correspond to models as in (A).