German nominal number interpretation in an impaired mental lexicon

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 & Yarlett, 2007; Baayen et al., 2011, 2019) 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 et al., 2017), and the unimpaired system. We test a model that implements the traditional cues borrowed from the schema approach (Köpcke, 1988, 1993; Köpcke et al., 2021), 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.