Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech

Frost, Rebecca Louise Ann and Monaghan, Padraic John (2016) Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech. Cognition, 147. pp. 70-74. ISSN 0010-0277

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Abstract

Language learning requires mastering multiple tasks, including segmenting speech to identify words, and learning the syntactic role of these words within sentences. A key question in language acquisition research is the extent to which these tasks are sequential or successive, and consequently whether they may be driven by distinct or similar computations. We explored a classic artificial language learning paradigm, where the language structure is defined in terms of non-adjacent dependencies. We show that participants are able to use the same statistical information at the same time to segment continuous speech to both identify words and to generalise over the structure, when the generalisations were over novel speech that the participants had not previously experienced. We suggest that, in the absence of evidence to the contrary, the most economical explanation for the effects is that speech segmentation and grammatical generalisation are dependent on similar statistical processing mechanisms.

Item Type:
Journal Article
Journal or Publication Title:
Cognition
Additional Information:
This is the author’s version of a work that was accepted for publication in Cognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Cognition, 147, 2016 DOI: 10.1016/j.cognition.2015.11.010
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1200/1203
Subjects:
ID Code:
76790
Deposited By:
Deposited On:
24 Nov 2015 11:00
Refereed?:
Yes
Published?:
Published
Last Modified:
03 Dec 2020 02:44