title: Modeling the word recognition data of Vitevitch and Luce (1998): is it ARTful?
abstract: Vitevitch and Luce (1998) showed that the probability with which phonemes co-occur in the language (phonotactic probability) affects the speed with which words and nonwords are named. Words with high phonotactic probabilities between phonemes were named more slowly than words with low probabilities, whereas with nonwords, just the opposite was found. To reproduce this reversal in performance, a model would seem to require not merely sublexical representations, but sublexical representations that are relatively independent of lexical representations. ARTphone (Grossberg, Boardman, & Cohen, 1997) is designed to meet these requirements. In this study, we used a technique called parameter space partitioning to analyze ARTphone's behavior and to learn if it can mimic human behavior and, if so, to understand how. To perform best, differences in sublexical node probabilities must be amplified relative to lexical node probabilities to offset the additional source of inhibition (from top-down masking) that is found at the sublexical level.
authors.affiliation: Department of Psychology, Ohio State University, Columbus, Ohio 43210, USA. pitt.2@osu.edu
authors.forename: Mark A
authors.initials: MA
authors.lastname: Pitt
authors.forename: Jay I
authors.initials: JI
authors.lastname: Myung
authors.forename: Nicholas
authors.initials: N
authors.lastname: Altieri
mesh_terms: D006801:Humans; D007802:Language; D008037:Linguistics; D008960:Models, Psychological; D010465:Perception; D021641:Recognition, Psychology; D015203:Reproducibility of Results