Proceedings of the Annual Meeting of the Cognitive Science Society

The visual word form area (VWFA) is a region of the cortex lo-cated in the left fusiform gyrus, that appears to be a waystationin the reading pathway. The discovery of the VWFA occurredin the late twentieth century with the advancement of func-tional magnetic resonance imaging (fMRI). Since then, therehas been an increasing number of neuroimaging studies to un-derstand the VWFA, and there are disagreements as to its prop-erties. One such disagreement is regarding whether or not theVWFA is more selective for real words over pseudowords1. Arecent study using fMRI adaptation (Glezer, et al., 2009) pro-vided evidence that neurons in the VWFA are selectively tunedto real words. This contradicts the hypothesis that the VWFAis tuned to the sublexical structure of visual words, and there-fore has no preference for real words over pseudowords. Inthis paper, we develop a realistic model of the VWFA by train-ing a deep convolutional neural network to map printed wordsto their labels. The network is able to achieve an accuracy of98.5% on the test set. We then analyze this network to see ifit can account for the data Glezer et al. found for words andpseudowords, and find that it does.