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Abstract: Increasingly, the neural mechanisms supporting visual cognition are being conceptualized as a distributed but integrated system, as opposed to a set of individual, specialized regions each subserving a particular visual behavior. Consequently, there is an emerging emphasis on characterizing the functional, structural and computational properties of these broad networks. We present a novel theoretical perspective, which elucidates the developmental emergence, computational properties and vulnerabilities of integrated circuits using face and word recognition as a model domain, and we offer empirical data to support this account. Additionally, we suggest that, rather than being disparate and independent, these neural circuits are overlapping and subject to the same computational constraints. Specifically, the claim is that both word and face recognition rely on fine-grained visual representations but, by virtue of pressure to couple visual and language areas and to keep connection length short, the left hemisphere becomes more finely tuned for word recognition and, consequently, the right hemisphere becomes more finely tuned for face recognition. Thus, both hemispheres ultimately participate in both forms of visual recognition but their respective contributions are asymmetrically weighted.

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