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Paper Abstract and Keywords
Presentation 2014-08-13 13:00
An ERP study of parsing and memory load in Japanese sentence processing -- A comparison between left-corner parsing and the Dependency Locality Theory --
Shodai Uchida (Univ. of Tokyo), Edson T. Miyamoto (Univ. of Tsukuba), Yuki Hirose, Yuki Kobayashi, Takane Ito (Univ. of Tokyo) TL2014-30
Abstract (in Japanese) (See Japanese page) 
(in English) We report event-related potentials to long locative adjuncts following different types of NPs. First, when the adjunct followed an accusative object NP, it elicited a sustained right anterior negativity compared to when it followed a nominative subject NP, suggesting that accusative objects impose a larger memory load. Second, after a dative object NP, the adjunct elicited a combination of N400 and sustained left anterior positivity compared to when it followed a nominative subject NP, suggesting that a larger mismatch cost is involved in the processing of dative objects. The results are compatible with a model that measures memory load based on the syntactic representations built incrementally by a subclass of left-corner parsers. In such a model, an accusative object NP leads to the prediction of a verb, therefore memory load increases when a VP-internal adjunct intervenes between the accusative object NP and the predicted verb. In contrast, a dative object NP leads to the prediction of an accusative NP; therefore, a mismatching cost is incurred when the prediction is falsified by a VP-internal adjunct. The nominative subject serves as a baseline because it predicts neither a verb nor a direct object. The results are not compatible with previous models such as the Dependency Locality Theory, which use dependency relations to predict memory load.
Keyword (in Japanese) (See Japanese page) 
(in English) left-corner parsing / ERP / working memory / parsing algorithm / DLT / sentence processing / Japanese /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 176, TL2014-30, pp. 101-106, Aug. 2014.
Paper # TL2014-30 
Date of Issue 2014-08-05 (TL) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee TL  
Conference Date 2014-08-12 - 2014-08-13 
Place (in Japanese) (See Japanese page) 
Place (in English) The University of Tokyo (Komaba) 18 Bldg. Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mental Architecture for Processing and Learning of Language 
Paper Information
Registration To TL 
Conference Code 2014-08-TL 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An ERP study of parsing and memory load in Japanese sentence processing 
Sub Title (in English) A comparison between left-corner parsing and the Dependency Locality Theory 
Keyword(1) left-corner parsing  
Keyword(2) ERP  
Keyword(3) working memory  
Keyword(4) parsing algorithm  
Keyword(5) DLT  
Keyword(6) sentence processing  
Keyword(7) Japanese  
Keyword(8)  
1st Author's Name Shodai Uchida  
1st Author's Affiliation University of Tokyo (Univ. of Tokyo)
2nd Author's Name Edson T. Miyamoto  
2nd Author's Affiliation University of Tsukuba (Univ. of Tsukuba)
3rd Author's Name Yuki Hirose  
3rd Author's Affiliation University of Tokyo (Univ. of Tokyo)
4th Author's Name Yuki Kobayashi  
4th Author's Affiliation University of Tokyo (Univ. of Tokyo)
5th Author's Name Takane Ito  
5th Author's Affiliation University of Tokyo (Univ. of Tokyo)
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Speaker Author-1 
Date Time 2014-08-13 13:00:00 
Presentation Time 30 minutes 
Registration for TL 
Paper # TL2014-30 
Volume (vol) vol.114 
Number (no) no.176 
Page pp.101-106 
#Pages 6 
Date of Issue 2014-08-05 (TL) 

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