DMCA
Subject Omission in Children's Language: (2002)
| Venue: | In |
BibTeX
@INPROCEEDINGS{For02subjectomission,
author = {The Case For and Daniel Freudenthal and Julian Pine and Fernand Gobet and Hug Mummy},
title = {Subject Omission in Children's Language:},
booktitle = {In},
year = {2002},
pages = {334--339},
publisher = {Erlbaum}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Several theories have been put forward to explain the phenomenon that children who are learning to speak their native language tend to omit the subject of the sentence. According to the pro-drop hypothesis, children represent the wrong grammar. According to the performance limitations view, children represent the full grammar, but omit subjects due to performance limitations in production. This paper proposes a third explanation and presents a model which simulates the data relevant to subject omission. The model consists of a simple learning mechanism that carries out a distributional analysis of naturalistic input. It does not have any overt representation of grammatical categories, and its performance limitations reside mainly in its learning mechanism. The model clearly simulates the data at hand, without the need to assume large amounts of innate knowledge in the child, and can be considered more parsimonious on these grounds alone. Importantly, it employs a unified and objective measure of processing load, namely the length of the utterance, which interacts with frequency in the input. The standard performance limitations view assumes that processing load is dependent on a phrase's syntactic role, but does not specify a unifying underlying principle.
Keyphrases
subject omission performance limitation grammatical category several theory pro-drop hypothesis third explanation objective measure unifying underlying principle omit subject full grammar distributional analysis syntactic role standard performance limitation naturalistic input overt representation innate knowledge native language simple learning mechanism large amount learning mechanism wrong grammar data relevant
