Learning Nonadjacent Dependencies Embedded in Sentences of an Artificial Language: When Learning Breaks Down

Autor/inn/en	Wang, Felix Hao; Mintz, Toben H.
Titel	Learning Nonadjacent Dependencies Embedded in Sentences of an Artificial Language: When Learning Breaks Down
Quelle	In: Journal of Experimental Psychology: Learning, Memory, and Cognition, 44 (2018) 4, S.604-614 (11 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wang, Felix Hao)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0278-7393
DOI	10.1037/xlm0000483
Schlagwörter	Artificial Languages; Sentences; Second Language Learning; Undergraduate Students; Learning Processes; Speech; Acoustics; Regression (Statistics); Statistical Analysis; Bayesian Statistics; California (Los Angeles) + Suchen Sie Ihr Suchwort? Sentence analysis; Satzanalyse; Zweitsprachenerwerb; Learning process; Lernprozess; Speaking; Sprechen; Akustik; Regression; Regressionsanalyse; Statistische Analyse
Abstract	The structure of natural languages give rise to many dependencies in the linear sequences of words, and within words themselves. Detecting these dependencies is arguably critical for young children in learning the underlying structure of their language. There is considerable evidence that human adults and infants are sensitive to the statistical properties of sequentially adjacent items. However, the conditions under which learners detect nonadjacent dependencies (NADs) appears to be much more limited. This has resulted in proposals that the kinds of learning mechanisms learners deploy in processing adjacent dependencies are fundamentally different from those deployed in learning NADs. Here we challenge this view. In 4 experiments, we show that learning both kinds of dependencies is hindered in conditions when they are embedded in longer sequences of words, and facilitated when they are isolated by silences. We argue that the findings from the present study and prior research is consistent with a theory that similar mechanisms are deployed for adjacent and nonadjacent dependency learning, but that NAD learning is simply computationally more complex. Hence, in some situations NAD learning is only successful when constraining information is provided, but critically, that additional information benefits adjacent dependency learning in similar ways. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01