NotesFAQContact Us
Collection
Advanced
Search Tips
Back to results
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1005702
Record Type: Journal
Publication Date: 2013-May
Pages: 18
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0749-596X
EISSN: N/A
Prediction in Language Comprehension beyond Specific Words: An ERP Study on Sentence Comprehension in Polish
Szewczyk, Jakub M.; Schriefers, Herbert
Journal of Memory and Language, v68 n4 p297-314 May 2013
Recently, several ERP studies have shown that the human language comprehension system anticipates words that are highly likely continuations of a given text. However, it remains an open issue whether the language comprehension system can also make predictions that go beyond a specific word. Here, we address the question of whether readers predict broad semantically defined classes of words. Event-related brain potentials were recorded, while native Polish speakers read short stories for comprehension. The stories were setting up a context that was very strongly biasing towards either an animate or an inanimate direct object noun in the story-final sentence. At the same time, the context was highly predictive for a specific direct object noun or not predictive for a specific direct object noun. The noun that was actually presented either did fit the animacy bias of the context or did not fit. The noun was preceded by an adjective. Polish has four classes of grammatical gender in the singular: feminine, neuter, masculine-animate, and masculine-inanimate. The prenominal adjective agrees with the direct object noun with respect to case and, in the case of masculine-animate and masculine-inanimate nouns, with respect to the in-/animacy of the noun. This allowed us to probe, at the adjective, whether the comprehension system predicts the in-/animacy of the direct object noun. Prediction-inconsistent adjectives elicited a negativity relative to prediction-consistent adjectives. This negativity was of the same size for contexts biasing towards a specific noun and for contexts not biasing towards a specific noun. These findings show that the comprehension system can predict semantically defined classes of words. (Contains 2 tables and 5 figures.)
Elsevier. 3251 Riverport Lane, Maryland Heights, MO 63043. Tel: 800-325-4177; Tel: 314-447-8000; Fax: 314-447-8033; e-mail: JournalCustomerService-usa@elsevier.com; Web site: http://www.elsevier.com
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A