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  • Frost_Monaghan_2015_Cognition

    Rights statement: 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

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

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Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech. / Frost, Rebecca Louise Ann; Monaghan, Padraic John.

In: Cognition, Vol. 147, 02.2016, p. 70-74.

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@article{3b817950b2b6405eb4379e562e2d0228,
title = "Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech",
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.",
keywords = "Language acquisition, Artificial grammar learning, Speech segmentation, Grammatical processing, Statistical learning",
author = "Frost, {Rebecca Louise Ann} and Monaghan, {Padraic John}",
note = "This is the author{\textquoteright}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",
year = "2016",
month = feb
doi = "10.1016/j.cognition.2015.11.010",
language = "English",
volume = "147",
pages = "70--74",
journal = "Cognition",
issn = "0010-0277",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech

AU - Frost, Rebecca Louise Ann

AU - Monaghan, Padraic John

N1 - 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

PY - 2016/2

Y1 - 2016/2

N2 - 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.

AB - 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.

KW - Language acquisition

KW - Artificial grammar learning

KW - Speech segmentation

KW - Grammatical processing

KW - Statistical learning

U2 - 10.1016/j.cognition.2015.11.010

DO - 10.1016/j.cognition.2015.11.010

M3 - Journal article

VL - 147

SP - 70

EP - 74

JO - Cognition

JF - Cognition

SN - 0010-0277

ER -