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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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -