Engelmann, Felix, Granlund, Sonia, Kolak, Joanna, Szreder, Marta, Ambridge, Ben ORCID: 0000-0003-2389-8477, Pine, Julian ORCID: 0000-0002-7077-9713, Theakston, Anna and Lieven, Elena
(2019)
How the input shapes the acquisition of verb morphology: Elicited production and computational modelling in two highly inflected languages.
Cognitive Psychology, 110.
pp. 30-69.
Text
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Abstract
The aim of the present work was to develop a computational model of how children acquire inflectional morphology for marking person and number; one of the central challenges in language development. First, in order to establish which putative learning phenomena are sufficiently robust to constitute a target for modelling, we ran large-scale elicited production studies with native learners of Finnish (N = 77; 35–63 months) and Polish (N = 81; 35–59 months), using a novel method that, unlike previous studies, allows for elicitation of all six person/number forms in the paradigm (first, second and third person; singular and plural). We then proceeded to build and test a connectionist model of the acquisition of person/number marking which not only acquires near adult-like mastery of the system (including generalisation to unseen items), but also yields all of the key phenomena observed in the elicited-production studies; specifically, effects of token frequency and phonological neighbourhood density of the target form, and a pattern whereby errors generally reflect the replacement of low frequency targets by higher-frequency forms of the same verb, or forms with the same person/number as the target, but with a suffix from an inappropriate conjugation class. The findings demonstrate that acquisition of even highly complex systems of inflectional morphology can be accounted for by a theoretical model that assumes rote storage and phonological analogy, as opposed to formal symbolic rules.
Item Type: | Article |
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Uncontrolled Keywords: | Language acquisition, Verb marking, Morphology, Computational modelling, Cross-linguistic, Elicited production, Neural networks |
Depositing User: | Symplectic Admin |
Date Deposited: | 01 Mar 2019 08:37 |
Last Modified: | 19 Jan 2023 01:00 |
DOI: | 10.1016/j.cogpsych.2019.02.001 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3033574 |