Incremental lexical learning in speech production: a computational model and empirical evaluation
Oppenheim, Gary M.
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https://hdl.handle.net/2142/29749
Description
Title
Incremental lexical learning in speech production: a computational model and empirical evaluation
Author(s)
Oppenheim, Gary M.
Issue Date
2012-02-06T20:14:22Z
Director of Research (if dissertation) or Advisor (if thesis)
Dell, Gary S.
Doctoral Committee Chair(s)
Dell, Gary S.
Committee Member(s)
Bock, J. Kathryn
Cohen, Neal C.
Hummel, John E.
Ross, Brian H.
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
lexical access
incremental learning
neural network model
language production
speech production
word retrieval
Abstract
Naming a picture of a dog primes the subsequent naming of a picture of a dog (repetition priming) and interferes with the subsequent naming of a picture of a cat (semantic interference). Behavioral studies suggest that these effects derive from persistent changes in the way that words are activated and selected for production, and some have claimed that the findings are only understandable by positing a competitive mechanism for lexical selection. This dissertation presents and evaluates a simple model of lexical retrieval in speech production that applies error-driven learning to its lexical activation network. This model naturally produces repetition priming and semantic interference effects. It predicts the major findings from several published experiments, demonstrating that these effects may arise from incremental learning. Furthermore, analysis of the model suggests that competition during lexical selection is not necessary for semantic interference if the learning process is itself competitive. Three additional experiments seek to evaluate the temporal persistence of semantic interference effects, as predicted by an incremental learning account.
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