Object classification has been proposed as a principal objective of the primate ventral visual stream. However, optimizing for object classification alone does not constrain how other variables may be encoded in high-level visual representations. Here, we studied how the latent sources of variation in a visual scene are encoded within high-dimensional population codes in primate visual cortex and in deep neural networks (DNNs). In particular, we focused on the degree to which different sources of variation are represented in non-overlapping (“factorized”) subspaces of population activity. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions. To test the importance of factorization in computational models of the brain, we then conducted a detailed large-scale analysis of factorization of individual scene parameters – lighting, background, camera viewpoint, and object pose – in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. In contrast, invariance to object pose and camera viewpoint in models was negatively associated with a match to neural and behavioral data. Intriguingly, we found that factorization was similar in magnitude and complementary to classification performance as an indicator of the most brainlike models suggesting a new principle. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models.
