title: Lesioning an attractor network: investigations of acquired dyslexia.
abstract: A recurrent connectionist network was trained to output semantic feature vectors when presented with letter strings. When damaged, the network exhibited characteristics that resembled several of the phenomena found in deep dyslexia and semantic-access dyslexia. Damaged networks sometimes settled to the semantic vectors for semantically similar but visually dissimilar words. With severe damage, a forced-choice decision between categories was possible even when the choice of the particular semantic vector within the category was not possible. The damaged networks typically exhibited many mixed visual and semantic errors in which the output corresponded to a word that was both visually and semantically similar. Surprisingly, damage near the output sometimes caused pure visual errors. Indeed, the characteristic error pattern of deep dyslexia occurred with damage to virtually any part of the network.
authors.affiliation: Department of Computer Science, University of Toronto, Ontario, Canada.
authors.forename: G E
authors.initials: GE
authors.lastname: Hinton
authors.forename: T
authors.initials: T
authors.lastname: Shallice
mesh_terms: D001921:Brain / Q000503:physiopathology; D001925:Brain Damage, Chronic / Q000503:physiopathology*; D001931:Brain Mapping; D004411:Dyslexia, Acquired / Q000503:physiopathology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000503:physiopathology*; D009483:Neuropsychological Tests