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Learning biases in person-number linearization


Maldonado, Mora; Saldana, Carmen; Culbertson, Jennifer (2020). Learning biases in person-number linearization. PsyArXiv Preprints o.Nr., University of Zurich.

Abstract

The idea that universal representations of hierarchical structure constrain patterns of linear order is a central to many linguistic theories. In this paper we use Artificial Language Learning techniques to experimentally probe this claim. Specifically, we investigate how a hypothesized hierarchy of φ-features impacts the linearization of person and number affixes by (English-speaking) learners in the lab.

Abstract

The idea that universal representations of hierarchical structure constrain patterns of linear order is a central to many linguistic theories. In this paper we use Artificial Language Learning techniques to experimentally probe this claim. Specifically, we investigate how a hypothesized hierarchy of φ-features impacts the linearization of person and number affixes by (English-speaking) learners in the lab.

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Item Type:Working Paper
Communities & Collections:06 Faculty of Arts > Department of Comparative Linguistics
Dewey Decimal Classification:490 Other languages
890 Other literatures
410 Linguistics
Language:English
Date:2020
Deposited On:20 Jan 2021 09:28
Last Modified:20 Jan 2021 09:28
Series Name:PsyArXiv Preprints
ISSN:0010-9452
Additional Information:The 50th Annual Meeting of the North East Linguistic Society
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.31234/osf.io/5s2r8
Related URLs:https://www.linguisticsociety.org/content/50th-annual-meeting-north-east-linguistic-society

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