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Abstract: Developing a theory of semantic ambiguity resolution (i.e., selecting a contextually appropriate interpretation of a word with multiple meanings such as BANK) has proven difficult because of discrepancies in the effects of relatedness of meaning observed across tasks. Hino, Pexman, and Lupker (2006) suggested that these task differences could not be attributed to a general semantic coding process as this process is shared across the tasks, but instead must be due to differences in the configuration of a decision making system. We argue that these task differences can be explained in terms of the settling dynamics of semantic coding within a distributed network. We support our account with a connectionist model of the semantic coding process and a lexical decision experiment in which we vary the difficulty of the task. The results show that increasing the degree of semantic coding alone produces results similar to those observed in different tasks.

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