Animacy or Case Marker Order?: Priority Information for Online Sentence Comprehension in a Head-Final Language
Author(s) -
Satoru Yokoyama,
Kei Takahashi,
Ryuta Kawashima
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0093109
Subject(s) - animacy , word order , nominative case , linguistics , computer science , sentence , sentence processing , argument (complex analysis) , dative case , natural language processing , artificial intelligence , verb , chemistry , philosophy , biochemistry
It is well known that case marker information and animacy information are incrementally used to comprehend sentences in head-final languages. However, it is still unclear how these two kinds of information are processed when they are in competition in a sentence's surface expression. The current study used sentences conveying the potentiality of some event (henceforth, potential sentences) in the Japanese language with theoretically canonical word order (dative–nominative/animate–inanimate order) and with scrambled word order (nominative–dative/inanimate–animate order). In Japanese, nominative–first case order and animate–inanimate animacy order are preferred to their reversed patterns in simplex sentences. Hence, in these potential sentences, case information and animacy information are in competition. The experiment consisted of a self-paced reading task testing two conditions (that is, canonical and scrambled potential sentences). Forty-five native speakers of Japanese participated. In our results, the canonical potential sentences showed a scrambling cost at the second argument position (the nominative argument). This result indicates that the theoretically scrambled case marker order (nominative–dative) is processed as a mentally canonical case marker order, suggesting that case information is used preferentially over animacy information when the two are in competition. The implications of our findings are discussed with regard to incremental simplex sentence comprehension models for head-final languages.
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