Computational models of perceptual expertise reveal a domain-specific inversion effect for objects of expertise

The question of whether perceptual expertise is mediated by general-expert or domain-specific processing mechanisms has been debated for decades. Because humans are face experts, face-like effects in objects of expertise were considered support for the general-expertise hypothesis. Conversely, stronger effects for faces, were considered support for the domain-specific hypothesis. However, effects of domain, experience, and level of categorization, are confounded in human studies, which may lead to erroneous inferences. To overcome these limitations, we used computational models of perceptual expertise and tested different domains and levels of categorization in isolation, matched for amount of experience. Like humans, the models generated a larger inversion effect for faces than for objects. Importantly, a face-like inversion effect was found for individual-based categorization of non-faces but only in a network specialized for that domain. Thus, contrary to prevalent assumptions, face-like effects in objects of expertise may originate from domain-specific rather than general-expert processing mechanisms.