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Article published In: The Mental Lexicon
Vol. 18:3 (2023) ► pp.417–445

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German nominal number interpretation in an impaired mental lexicon

A naive discriminative learning perspective

ORCiD logo with link | Heinrich-Heine-Universität Düsseldorf
 | Universität Tübingen
 | Universität Erfurt
Published online: 21 June 2024
DOI logo with link
https://doi.org/10.1075/ml.23017.pla
Abstract
There is an ongoing debate on how speakers and listeners process and interpret information in a morphological system that is very complex and not very transparent. A well-known test case is the German nominal number system. In this paper we employ discriminative learning (e.g., Ramscar, M. and Yarlett, D. (2007). Linguistic self-correction in the absence of feedback: A new approach to the logical problem of language acquisition. Cognitive science, 31(6):927–960. Google Scholar logo with link to Google Scholar; Baayen, R. H., Milin, P., Filipović Durdević, D., Hendrix, P., and Marelli, M. (2011). An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118(3):438–481. Google Scholar logo with link to Google Scholar, Baayen, R. H., Chuang, Y.-Y., Shafaei-Bajestan, E., and Blevins, J. P. (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, 1–39. Google Scholar logo with link to Google Scholar) to test whether discriminative learning networks can be used to better understand the processing of German number. We analyse behavioral data obtained from a patient with primary progressive aphasia (Domahs, F., Bartha-Doering, L., Domahs, U., and Delazer, M. (2017). Wie muss ein “guter” deutscher plural klingen? In Fuhrhop, N., Szczepaniak, R., and Schmidt, K., editors, Sichtbare und hörbare Morphologie, pages 205–237. De Gruyter Mouton, Berlin & Boston. Google Scholar logo with link to Google Scholar), and the unimpaired system. We test a model that implements the traditional cues borrowed from the schema approach (Köpcke, K.-M. (1988). Schemas in german plural formation. Lingua, 74(4):303–335. Google Scholar logo with link to Google Scholar, (1993). Schemata bei der Pluralbildung im Deutschen: Versuch einer kognitiven Morphologie, volume 47 of Studien zur deutschen Grammatik. G. Narr, Tübingen.Google Scholar logo with link to Google Scholar; Köpcke, K.-M., Schimke, S., and Wecker, V. (2021). Processing of german noun plurals: Evidence for first- and second-order schemata. Word Structure, 14(1):1–24. Google Scholar logo with link to Google Scholar), and compare it to a model that uses segmental and phonotactic information only. Our results for the unimpaired system demonstrate that a model based on only biphones as cues is better able to predict the number of a given word-form than a model using structural phonological cues. We also test whether a discriminative learning model can predict the number decisions by the aphasic patient. The results demonstrate that a biphone-based discriminative model trained on the patient’s responses is superior to a structure-based model in approximating the patient’s behavior.
Article outline
  • 1.Introduction
  • 2.Approaches to German nominal number
    • 2.1Schemas
    • 2.2Computational approaches
    • 2.3Modeling nominal number with discriminative learning
  • 3.Methods
    • 3.1The patient
    • 3.2Data set
    • 3.3Setting up the NDL models
  • 4.Results
    • 4.1NDL: Overall performance
      • 4.1.1The language
      • 4.1.2The patient
    • 4.2NDL: The strength of the structural cues
  • 5.Discussion
  • Acknowledgements
  • Notes
  • References
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Monakhov, Sergei, Holger Diessel & Brisca Balthes
2026. Learn what is detectable, detect what is useful: acquisition of German plural as a classification problem. Cognition 266 ► pp. 106292 ff. DOI logo
Plag, Ingo, Maria Heitmeier & Frank Domahs
2025. Morpho-phonology is not independent of semantics. The Mental Lexicon DOI logo

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