NDLI: Cross-Linguistic Cognitive Modeling of Verbal Morphology Acquisition

Content Provider	Springer Nature Link
Author	Oliva, Jesús Serrano, J. Ignacio del Castillo, M. Dolores Iglesias, Ángel
Copyright Year	2017
Abstract	How children acquire and process inflectional morphology is still an open question. Despite the fact that English past tense acquisition has been studied and modeled in depth, the current approaches do not account for many of the errors made by humans. Moreover, not much work has been done with highly inflected languages, like Spanish. However, the modeling of any linguistic phenomenon in different languages is very important in order to understand the general cognitive processes underlying each particular phenomenon. This paper presents an ACT-R dual-mechanism model that accomplishes the task of acquiring verbal morphology systems from one of the simplest systems (the English one) to one of the most complex systems (the Spanish one), by using a double analogy process of stem and suffix. The model proposed was able to match all types of errors that developing children make (from a sample of them), both in English and Spanish. The models for both languages used very similar parameters. The introduced approach not only shows how children could acquire a highly inflected morphology system in terms of dual-mechanism theories but, given its cross-linguistic character, also sheds light on the possible general processes involved in the acquisition and processing of inflectional morphology.
Starting Page	237
Ending Page	258
Page Count	22
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	9
Issue Number	2
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2017-03-06
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cognitive linguistics Cognitive modeling Language acquisition Inflectional morphology English inflectional system Spanish inflectional system Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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