Modelling anomia by the discrete two-stage word production architecture

Abstract

Recent advancements in computational modelling have opened new ways to understand the underlying mechanisms of cognitive deficits resulting from brain damage. These implementations have followed interactive activation and parallel distributed processing approaches and the computational potential of more transparent and serial cognitive architectures has not received much attention. We tested the ability of the discrete two-stage word production model to account for naming response patterns in aphasia. Our implementation is a simple local connectionist model with two serially ordered networks representing lexical–semantic and phoneme levels. Anomic deficits were simulated by manipulating three parameters: noise within the two networks leading to semantically and phonologically based errors, and between-level threshold leading to no responses. Comparisons to actual naming data from 10 aphasics showed that this simple model provides a rather good fit to a variety of clinically observed naming response patterns. However, the existence of output-type semantic naming errors in some aphasics call for a modified discrete two-stage model which would possess monitoring mechanisms for lexical output.