Deep learning models of perceptual expertise support a domain-specific account

Abstract

Perceptual expertise is an acquired skill that enables fine discrimination of members of a homogenous category. The question of whether perceptual expertise is mediated by general-expert or domain-specific processing mechanisms has been hotly debated for decades in human behavioral and neuroimaging studies. To decide between these two hypotheses, most studies examined whether expertise for different domains is mediated by the same mechanisms used for faces, for which most humans are expert. Here we used deep convolutional neural networks (DCNNs) to test whether perceptual expertise is best achieved by computations that are optimized for face or object classification. We re-trained a face-trained and an object-trained DCNNs to classify birds at the sub-ordinate or individual-level of categorization. The face-trained DCNN required deeper retraining to achieve the same level of performance for bird classification as an object-trained DCNN. These findings indicate that classification at the subordinate- or individual-level of categorization does not transfer well between domains. Thus, fine-grained classification is best achieved by using domain-specific rather than domain-general computations.