Simulating phonological and semantic impairment of English tense inflection with linear discriminative learning

This study applies the computational theory of the ‘discriminative lexicon’ (Baayen, Chuang, and Blevins, 2019) to the modeling of the production of English verbs in aphasic speech. Under semantic impairment, speakers have been reported to have greater difficulties with irregular verbs, whereas speakers with phonological impairment are described as having greater problems with regulars. Joanisse and Seidenberg (1999) were able to model this dissociation, but only by adding noise to the semantic units of their model. We report two simulation studies in which topographically coherent regions of phonological and semantic networks were selectively damaged. Our model replicated the main findings, including the high variability in the consequences of brain lesions for speech production. Importantly, our model generated these results without having to lesion the semantic system more than the phonological system. The model’s success hinges on the use of a corpus-based distributional vector space for representing verbs’ meanings. Irregular verbs have denser semantic neighborhoods than do regular verbs (Baayen and Moscoso del Prado Martín, 2005). In our model this renders irregular verbs more fragile under semantic impairment. These results provide further support for the central idea underlying the discriminative lexicon: that behavioral patterns can, to a considerable extent, be understood as emerging from the distributional properties of a language and basic principles of human learning.