Visual crowding and category specific deficits for pictorial stimuli: A neural network model

This paper describes a series of modular neural network simulations of visual object processing. In a departure from much previous work in this domain, the model described here comprises both supervised and unsupervised modules and processes real pictorial representations of items from different object categories. The unsupervised module carries out bottom-up encoding of visual stimuli, thereby developing a "perceptual" representation of each presented picture. The supervised component then classifies each perceptual representation according to a target semantic category. Model performance was assessed (1) during learning, (2) under generalisation to novel instances, and (3) after lesion damage at different stages of processing. Strong category effects were observed throughout the different experiments, with living things and musical instruments eliciting greater recognition failures relative to other categories. This pattern derives from within-category similarity effects at the level of perceptual representation and our data support the view that visual crowding can be a potentially important factor in the emergence of some category-specific impairments. The data also accord with the cascade model of object recognition, since increased competition between perceptual representations resulted in category-specific impairments even when the locus of damage was within the semantic component of the model. Some strengths and limitations of this modelling approach are discussed and the results are evaluated against some other accounts of category-specific recognition failure.