Artificial Language Learning and Feature-Based Generalization

Autor/inn/en	Finley, Sara; Badecker, William
Titel	Artificial Language Learning and Feature-Based Generalization
Quelle	In: Journal of Memory and Language, 61 (2009) 3, S.423-437 (15 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0749-596X
DOI	10.1016/j.jml.2009.05.002
Schlagwörter	Play; Vowels; Phonology; Artificial Languages; Learning Strategies; Grammar; Experiments; Adults; Suffixes; Learning Processes + Suchen Sie Ihr Suchwort? Spiel; Fonologie; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Grammatik; Erprobung; Learning process; Lernprozess
Abstract	Abstract representations such as subsegmental phonological features play such a vital role in explanations of phonological processes that many assume that these representations play an equally prominent role in the learning process. This assumption is tested in three artificial grammar experiments involving a mini language with morpho-phonological alternations based on back vowel harmony. In Experiments 1 and 2, adult participants were trained using positive data from four vowels in a six-vowel inventory: the two remaining vowels appeared at test only. If participants use subsegmental phonological features and natural classes for learning, they should generalize to the novel test segments. Results support a subsegmental feature-based learning strategy that makes use of phonetic information and knowledge of phonological principles. A third experiment (Experiment 3) tests for generalizations to novel suffixes, providing further evidence for the generality of learning. (Contains 3 tables and 4 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10