Simulating the emergence of the Visual Word Form Area: Recycling a convolutional neural network for reading
The visual word form area (VWFA) is a region of human inferotemporal cortex that emerges at a fixed location in occipitotemporal cortex during reading acquisition, and systematically responds to written words in literate individuals. According to the neuronal recycling hypothesis, this region arises through the repurposing, for letter recognition, of a subpart of the ventral visual pathway initially involved in face and object recognition. Furthermore, according to the biased connectivity hypothesis, its universal localization is due to pre-existing connections from this subregion to areas involved in spoken language processing. Here, we evaluate those hypotheses in an explicit computational model. We trained a deep convolutional neural network of the ventral visual pathway, first to categorize pictures, and then to recognize written words invariantly for case, font and size. We show that the model can account for many properties of the VWFA, particularly when a subset of units possesses a biased connectivity to word output units. The network develops a sparse, invariant representation of written words, based on a restricted set of reading-selective units. Their activation mimics several properties of the VWFA, and their lesioning causes a reading-specific deficit. Our simulation fleshes out the neuronal recycling hypothesis, and make several testable predictions concerning the neural code for written words.