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Learning Non-Adjacent Dependencies Embedded in Sentences of an Artificial Language: When Learning Breaks Down (in press, JEP: LMC) ...
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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 non-adjacent 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 four 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 ...
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Keyword:
Cognition and Perception; Cognitive Psychology; FOS Languages and literature; FOS Psychology; Linguistics; Psycholinguistics and Neurolinguistics; Psychology; Social and Behavioral Sciences
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URL: https://dx.doi.org/10.17605/osf.io/b3udt
https://doi.org/10.31234/osf.io/b3udt
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Infants' Sensitivity to Vowel Harmony and its Role in Segmenting Speech ...
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Top-Down Structure Influences Learning of Non-Adjacent Dependencies in an Artificial Language ...
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