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