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Sentence First, Arguments After : = Mechanisms of Morphosyntax Acquisition.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Sentence First, Arguments After :/
其他題名:	Mechanisms of Morphosyntax Acquisition.
作者:	Getz, Heidi Rose.
面頁冊數:	1 online resource (143 pages)
附註:	Source: Dissertation Abstracts International, Volume: 80-01(E), Section: A.
Contained By:	Dissertation Abstracts International80-01A(E).
標題:	Linguistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780438330887

Sentence First, Arguments After : = Mechanisms of Morphosyntax Acquisition.
Getz, Heidi Rose.

Sentence First, Arguments After :Mechanisms of Morphosyntax Acquisition. - 1 online resource (143 pages)

Source: Dissertation Abstracts International, Volume: 80-01(E), Section: A.

Thesis (Ph.D.)--Georgetown University, 2018.

Includes bibliographical references

Natural languages contain complex grammatical patterns. For example, in German, finite verbs occur second in main clauses while non-finite verbs occur last, as in dein Bruder mochte in den Zoo gehen ("Your brother wants to go to the zoo"). Children easily acquire this type of morphosyntactic contingency (Poeppel & Wexler, 1993; Deprez & Pierce, 1994). There is extensive debate in the literature over the nature of children's linguistic representations, but there are considerably fewer mechanistic ideas about how knowledge is actually acquired. Regarding German, one approach might be to learn the position of prosodically prominent open-class words ("verbs go 2nd or last") and then fill in the morphological details. Alternatively, one could work in the opposite direction, learning the position of closed-class morphemes ("-te goes 2nd and -en goes last") and fitting open-class items into the resulting structure. This second approach is counter-intuitive, but I will argue that it is the one learners take.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438330887Subjects--Topical Terms:

557829
Linguistics.
Index Terms--Genre/Form:

554714
Electronic books.

Sentence First, Arguments After : = Mechanisms of Morphosyntax Acquisition.
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500 $a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: A.
500 $a Adviser: Elissa L. Newport.
502 $a Thesis (Ph.D.)--Georgetown University, 2018.
504 $a Includes bibliographical references
520 $a Natural languages contain complex grammatical patterns. For example, in German, finite verbs occur second in main clauses while non-finite verbs occur last, as in dein Bruder mochte in den Zoo gehen ("Your brother wants to go to the zoo"). Children easily acquire this type of morphosyntactic contingency (Poeppel & Wexler, 1993; Deprez & Pierce, 1994). There is extensive debate in the literature over the nature of children's linguistic representations, but there are considerably fewer mechanistic ideas about how knowledge is actually acquired. Regarding German, one approach might be to learn the position of prosodically prominent open-class words ("verbs go 2nd or last") and then fill in the morphological details. Alternatively, one could work in the opposite direction, learning the position of closed-class morphemes ("-te goes 2nd and -en goes last") and fitting open-class items into the resulting structure. This second approach is counter-intuitive, but I will argue that it is the one learners take.
520 $a Previous research suggests that learners focus distributional analysis on closed-class items because of their distinctive perceptual properties (Braine, 1963; Morgan, Meier, & Newport, 1987; Shi, Werker & Morgan, 1999; Valian & Coulson, 1988). The Anchoring Hypothesis (Valian & Coulson, 1988) posits that, because these items tend to occur at grammatically important points in the sentence (e.g., phrase edges), focusing on them helps learners acquire grammatical structure. Here I ask how learners use closed-class items to acquire complex morphosyntactic patterns such as the verb form/position contingency in German. Experiments 1--4 refute concerns that morphosyntactic contingencies like those in German are too complex to learn distributionally. Experiments 5--8 explore the mechanisms underlying learning, showing that adults and children analyze closed-class items as predictive of the presence and position of open-class items, but not the reverse. In these experiments, subtle mathematical distinctions in learners' input had significant effects on learning, illuminating the biased computations underlying anchored distributional analysis. Taken together, results suggest that learners organize knowledge of language patterns relative to a small set of closed-class items---just as patterns are represented in modern syntactic theory (Rizzi & Cinque, 2016).
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