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Rethinking Verb Second$
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Rebecca Woods and Sam Wolfe

Print publication date: 2020

Print ISBN-13: 9780198844303

Published to Oxford Scholarship Online: June 2020

DOI: 10.1093/oso/9780198844303.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2020. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 08 August 2020

The role of ambiguity in child errors

The role of ambiguity in child errors

A comparison with Dependency Length Minimization

Chapter:
(p.810) 35 The role of ambiguity in child errors
Source:
Rethinking Verb Second
Author(s):

Isaac Gould

Publisher:
Oxford University Press
DOI:10.1093/oso/9780198844303.003.0035

This chapter compares two contrasting approaches to accounting for the verb placement errors in child Swiss German that are described in Schönenberger (2001). The first is a learning model that captures the errors because it both learns from ambiguous input and has a rich hypothesis space of interacting parameters (Gould 2017). The second captures the errors instead by means of a cognitive bias early in development, namely a heuristic for Dependency Length Minimization (DLM) (cf. Futrell et al. 2015). The latter approach is notable in that it (a) does not rely on learning from ambiguous input to capture child errors (cf. Sakas and Fodor 2001), (b) offers a prima facie simpler way of capturing the errors, and (c) is novel in applying DLM to account for child errors. Nevertheless, closer investigation shows that a DLM-based model does not provide a principled account of the children’s developmental trajectory and is clearly not any simpler than the alternative. Further, there is some reason to think more generally that DLM does not play a role in the development of the Swiss German children during the course of Schönenberger’s study. In contrast, an approach based on parameter interaction does provide the desired principled account. This comparison provides support for a non-biased learning model that has parameter interaction and learns from ambiguous input.

Keywords:   child error, Dependency Length Minimization, DLM, parameter interaction, Swiss German, verb placement

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