Armstrong, B. C.*, & Plaut, D. C. (2008).  Settling dynamics in distributed networks explain task differences in semantic ambiguity effects: Computational and behavioral evidence.  in B. C. Love, K. McRae, & V. M. Sloutsky (Eds.) Proceedings of the 30th Annual Meeting of the Cognitive Science Society (pp. 273-278).  Austin, TX: Cognitive Science Society.  


Author's self-archived version (.pdf)  (6 pages)

Official version hosted by the Cognitive Science Society [external link]


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.

Keywords: semantic ambiguity; word comprehension; processing dynamics; computational/connectionist modeling; decision making; lexical decision

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