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Abstract: The traditional view of the lexical system stipulates word-specific representations and separate pathways for regular and exception words. An alternative approach views lexical knowledge as developing from general learning principles applied to mappings among distributed representations of written and spoken words and their meanings. On this distributed account, distinctions among words, and between words and nonwords are not reified in the structure of the system but reflect the sensitivity of learning to the relative systematicity in the various mappings. Two computational simulations address findings that have seemed problematic for the distributed approach. Both involve a consideration of the role of semantics in normal and impaired lexical processing. The first simulation accounts for patients with impaired comprehension but intact reading in terms of individual differences in the division of labor between the semantic and phonological pathways. The second simulation demonstrates that a distributed network can reliably distinguish words from nonwords based on a measure of familiarity defined over semantics. The results underscore the importance of relating function to structure in the lexical system within the context of an explicit computational framework.

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