We estimate the correspondence between subject–subject choice behaviors. First, we generate a random split of each subject’s performance. We then compute the between-subject correlation, iterating across 100 random splits. Each row contains a given subjects’ (e.g. subject 1, top row) correspondence with all other experimental subjects, including perirhinal cortex (PRC)-intact (purple) and -lesioned (green) monkeys. Using this same subject–subject measure, we also estimate subject–model correspondence (gray). We visualize our results at two resolutions: (a) for the morph-level analysis, we average performance across all images within each morph level (e.g. 10%, 20%, etc.; as per the analysis in Figure 3b) and compare a single subject’s behaviors to all other experimental subjects, as well as model performance; (b) for the image-level analysis we average performance across a random split of trials containing each image, for each subject, then compare each single subject’s behaviors to all other experimental subjects, as well as model performance (as outlined in Methods: Consistency estimates). For the morph-level analysis, the model choice behavior is ‘subject-like’; the distribution of model–subject correspondence is within the distribution of between-subject correspondence (in Figure 3b, subject-level choices behaviors are on the diagonal). However, at the resolution of single images, model choice behavior is not subject-like; model correspondence to each subject is not likely observed under the between-subject distributions (i.e. subject-level choices behaviors do not fall along the diagonal in Figure 3c). We note the PRC-intact monkeys are subjects 1–3, PRC-lesioned monkeys are subjects 4–6.