Incremental lexical learning in speech production: a computational model and empirical evaluation

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Psychology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2012

Author dc:creator

• Oppenheim, Gary M.

Contributors dc:contributor

• Dell, Gary S.
• Bock, J. Kathryn
• Cohen, Neal C.
• Hummel, John E.
• Ross, Brian H.

• lexical access
• incremental learning
• neural network model
• language production
• speech production
• word retrieval

Statement dc:rights

• Copyright 2011 Gary M. Oppenheim

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/29749

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/29749