Close Encounters of the Word Kind: Attested Distributional Information Boosts Statistical Learning

Autor/inn/en	Stärk, Katja; Kidd, Evan; Frost, Rebecca L. A.
Titel	Close Encounters of the Word Kind: Attested Distributional Information Boosts Statistical Learning
Quelle	In: Language Learning, 73 (2023) 2, S.341-373 (33 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Stärk, Katja) ORCID (Kidd, Evan) ORCID (Frost, Rebecca L. A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0023-8333
DOI	10.1111/lang.12523
Schlagwörter	Linguistic Input; Adults; Prior Learning; Task Analysis; German; Speech Communication; Syllables; Artificial Languages; Native Language; Transfer of Training; Phrase Structure; Memory; Linguistic Theory + Suchen Sie Ihr Suchwort? Sprachbildung; Vorkenntnisse; Aufgabenanalyse; Deutscher; Silbe; Training; Transfer; Ausbildung; Phrasenstruktur; Gedächtnis; Linguistische Theorie
Abstract	Statistical learning, the ability to extract regularities from input (e.g., in language), is likely supported by learners' prior expectations about how component units co-occur. In this study, we investigated how adults' prior experience with sublexical regularities in their native language influences performance on an empirical language learning task. Forty German-speaking adults completed a speech repetition task in which they repeated eight-syllable sequences from two experimental languages: one containing disyllabic words comprised of frequently occurring German syllable transitions (naturalistic words) and the other containing words made from unattested syllable transitions (non-naturalistic words). The participants demonstrated learning from both naturalistic and non-naturalistic stimuli. However, learning was superior for the naturalistic sequences, indicating that the participants had used their existing distributional knowledge of German to extract the naturalistic words faster and more accurately than the non-naturalistic words. This finding supports theories of statistical learning as a form of chunking, whereby frequently co-occurring units become entrenched in long-term memory. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01