The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities
Fig 5
The effect of increasing levels of object-scene co-occurrence in DCNN object space.
We trained a DCNN multiple times (N = 5) in 4 training conditions with increasing levels of object-background regularity from 0% to 100%. (A) Accuracy results for the model’s validation test. (B) The RSA results for the 4 models (GIST, condition, domain, co-occurrence) are shown for AlexNet’s fc7 that underwent different training regimes with increasing co-occurrence (0%, 58%, 87%, 100%) between objects and backgrounds (n = 6). Error bars indicate SEM. In the dissimilarity matrices, orange represents animal conditions and red represents background conditions.