Acquiring and Processing Verb Argument Structure: Distributional Learning in a Miniature Language

Autor/inn/en	Wonnacott, Elizabeth; Newport, Elissa L.; Tanenhaus, Michael K.
Titel	Acquiring and Processing Verb Argument Structure: Distributional Learning in a Miniature Language
Quelle	In: Cognitive Psychology, 56 (2008) 3, S.165-209 (45 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0010-0285
DOI	10.1016/j.cogpsych.2007.04.002
Schlagwörter	Language Processing; Language Research; Cues; Semantics; Verbs; Artificial Languages; Generalization; Linguistic Input; Language Acquisition; Language Patterns; Language Usage + Suchen Sie Ihr Suchwort? Sprachverarbeitung; Sprachforschung; Stichwort; Semantik; Sprachbildung; Sprachaneignung; Spracherwerb; Sprachmodell; Sprachstruktur; Sprachgebrauch
Abstract	Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a "rational" procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10