Perseverative and Semantic In uences on Visual Object Naming Errors in Optic Aphasia: A Connectionist Account

A recurrent back-propagation network is trained to generate semantic representations of objects from high-level visual representations. In addition to the standard weights, the network has correlational weights useful for implementing short-term associative memory. Under damage, the network exhibits the complex semantic and perseverative eeects of patients with a visual naming disorder known as \optic aphasia," in which previously presented objects innuence the response to the current object. Like optic aphasics, the network produces predominantly semantic rather than visual errors because, in contrast to reading, there is some structure in the mapping from visual to semantic representations for objects. and Dave Tourtezky for their help. All of the simulations described in this paper were run on a Silicon Graphics Iris-4D/240S using the Xerion simulator developed by Tony Plate.