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ERIC Number: EJ1176607
Record Type: Journal
Publication Date: 2018-Apr
Pages: 11
Abstractor: As Provided
ISSN: ISSN-0278-7393
Learning Nonadjacent Dependencies Embedded in Sentences of an Artificial Language: When Learning Breaks Down
Wang, Felix Hao; Mintz, Toben H.
Journal of Experimental Psychology: Learning, Memory, and Cognition, v44 n4 p604-614 Apr 2018
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: California (Los Angeles)
Grant or Contract Numbers: N/A