Learning to divide the labor: an account of deficits in light and heavy verb production

Abstract

Theories of sentence production that involve a convergence of activation from conceptual-semantic and syntactic-sequential units inspired a connectionist model that was trained to produce simple sentences. The model used a learning algorithm that resulted in a sharing of responsibility (or “division of labor”) between syntactic and semantic inputs for lexical activation according to their predictive power. Semantically rich, or “heavy”, verbs in the model came to rely on semantic cues more than on syntactic cues, whereas semantically impoverished, or “light”, verbs relied more on syntactic cues. When the syntactic and semantic inputs were lesioned, the model exhibited patterns of production characteristic of agrammatic and anomic aphasic patients, respectively. Anomic models tended to lose the ability to retrieve heavy verbs, whereas agrammatic models were more impaired in retrieving light verbs. These results obtained in both sentence production and single-word naming simulations. Moreover, simulated agrammatic lexical retrieval was more impaired overall in sentences than in single-word tasks, in agreement with the literature. The results provide a demonstration of the division-of-labor principle, as well as general support for the claim that connectionist learning principles can contribute to the understanding of non-transparent neuropsychological dissociations.