We present an anatomically-inspired neurocomputational model, including a foveated retina and the log-polar mapping from the visual field to the primary visual cortex, that recreates image inversion effects long seen in psychophysical studies. We show that visual expertise, the ability to discriminate between subordinate-level categories, changes the performance of the model on inverted images. We first explore face discrimination, which, in humans, relies on configural information. The log-polar transform disrupts configural information in an inverted image and leaves featural information relatively unaffected. We suggest this is responsible for the degradation of performance with inverted faces. We then recreate the effect with other subordinate-level category discriminators and show that the inversion effect arises as a result of visual expertise, where configural information becomes relevant as more identities are learned at the subordinate-level. Our model matches the classic result: faces suffer more from inversion than mono-oriented objects, which are more disrupted than non-mono-oriented objects when objects are only familiar at a basic-level, and simultaneously shows that expert-level discrimination of other subordinate-level categories respond similarly to inversion as face experts.