title: A computational cognitive model of syntactic priming.
abstract: The psycholinguistic literature has identified two syntactic adaptation effects in language production: rapidly decaying short-term priming and long-lasting adaptation. To explain both effects, we present an ACT-R model of syntactic priming based on a wide-coverage, lexicalized syntactic theory that explains priming as facilitation of lexical access. In this model, two well-established ACT-R mechanisms, base-level learning and spreading activation, account for long-term adaptation and short-term priming, respectively. Our model simulates incremental language production and in a series of modeling studies, we show that it accounts for (a) the inverse frequency interaction; (b) the absence of a decay in long-term priming; and (c) the cumulativity of long-term adaptation. The model also explains the lexical boost effect and the fact that it only applies to short-term priming. We also present corpus data that verify a prediction of the model, that is, that the lexical boost affects all lexical material, rather than just heads.
authors.affiliation: School of Informatics, University of Edinburgh, UK. reitter@cmu.edu
authors.forename: David
authors.initials: D
authors.lastname: Reitter
authors.forename: Frank
authors.initials: F
authors.lastname: Keller
authors.forename: Johanna D
authors.initials: JD
authors.lastname: Moore
mesh_terms: D000465:Algorithms; D001245:Association Learning; D002755:Choice Behavior; D003071:Cognition; D003198:Computer Simulation; D003210:Concept Formation; D003657:Decision Making; D006801:Humans; D011939:Mental Recall; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011578:Psycholinguistics; D012660:Semantics; D014705:Verbal Behavior; D014706:Verbal Learning