title: Opposition logic and neural network models in artificial grammar learning.
abstract: Following neural network simulations of the two experiments of, argued that the opposition logic advocated by was incapable of distinguishing between single and multiple influences on performance of artificial grammar learning and more generally. We show that their simulations do not support their conclusions. We also provide different neural network simulations that do simulate the essential results of Higham et al. (2000).
authors.affiliation: Department of Psychology and Neuroscience, University of Lethbridge, Lethbridge, Alta., T1K 3M4, Canada. vokey@uleth.ca
authors.forename: John R
authors.initials: JR
authors.lastname: Vokey
authors.forename: Philip A
authors.initials: PA
authors.lastname: Higham
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D008037:Linguistics; D008128:Logic; D016571:Neural Networks, Computer; D014706:Verbal Learning