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Article published In: The Mental Lexicon
Vol. 15:3 (2020) ► pp.385–421

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

 | University of Tübingen
 | University of Tübingen
Published online: 22 January 2021
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https://doi.org/10.1075/ml.20003.hei
Abstract
This study applies the computational theory of the ‘discriminative lexicon’ (Baayen, R. H., Chuang, Y.-Y., Shafaei-Bajestan, E., and Blevins, J. (2019). The discriminative lexicon: A unified computational model for the lexicon and lexical processing in comprehension and production grounded not in (de)composition but in linear discriminative learning. Complexity, 2019(1):1–39. Google Scholar logo with link to Google Scholar) 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, M. F. and Seidenberg, M. S. (1999). Impairments in verb morphology after brain injury: a connectionist model. Proceedings of the National Academy of Sciences, 961:7592–7597. Google Scholar logo with link to Google Scholar 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, R. H. and Moscoso del Prado Martín, F. (2005). Semantic density and past-tense formation in three Germanic languages. Language, 811:666–698. Google Scholar logo with link to Google Scholar). 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.
Article outline
  • 1.Introduction
  • 2.Previous computational modeling studies
  • 3.The discriminative lexicon
  • 4.Lesioning a discriminative lexicon
    • a.Topological maps for form and meaning
    • b.Lesioning topological maps
  • 5.Simulations
    • a.Experiment 1: Stimuli controlled for frequency
      • Materials and procedure
      • Results
    • b.Experiment 2: Controlling for phonological complexity
      • Materials and procedure
      • Results
    • c.Discussion
  • 6.Summary and conclusions
  • Notes
  • References
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