Convergence of a spreading activation neural network with application of simulating aphasic naming errors in Finnish language

Aphasia is a language disorder caused by brain damage. Difficulties in word processing called anomia constitute the most common type of errors in aphasia. Naming errors are characteristic of aphasia and can be used to investigate the disorder. In this context, naming refers to a psycholinguistic test where the subject is asked to utter the names of target objects presented to him or her in the form of simple pictures. In the case of aphasia the subject may name objects presented or concepts incorrectly by word blends (e.g. semantic error to say “right” for “left”) or even forming nonwords (words without sensible meaning). Previously [[Reference to 5], [Reference to 6], [Reference to 8], [Reference to 10] and [Reference to 11]], we have constructed a simulation technique with a spreading activation algorithm to model the mental word processing of an aphasic by employing a succession of four neural networks with semantic, phonological, syllabic and phonemic processing functions, respectively. Treating words and their component parts as textual units, naming errors are generated in the system by spreading activation in the networks. In this paper we study the convergence properties of the algorithms which are used for spreading activation in these networks. Convergence shows which component parts dominate other parts in the networks employed. It can be reasonable also in the psycholinguistic sense, at least concerning the so-called perseveration in which some word or sound is repeated being unable to form a correct word. To enable to predict and control the behaviour of the model the convergence property is useful to understand.