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dc.contributor.authorSandoval, Michelle
dc.contributor.authorPatterson, Dianne
dc.contributor.authorDai, Huanping
dc.contributor.authorVance, Christopher J.
dc.contributor.authorPlante, Elena
dc.date.accessioned2017-08-23T23:22:14Z
dc.date.available2017-08-23T23:22:14Z
dc.date.issued2017-07-27
dc.identifier.citationNeural Correlates of Morphology Acquisition through a Statistical Learning Paradigm 2017, 8 Frontiers in Psychologyen
dc.identifier.issn1664-1078
dc.identifier.doi10.3389/fpsyg.2017.01234
dc.identifier.urihttp://hdl.handle.net/10150/625334
dc.description.abstractThe neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the "rules" for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task- related sub- networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system.
dc.description.sponsorshipNIDCD [1R01DC011276]en
dc.language.isoenen
dc.publisherFRONTIERS MEDIA SAen
dc.relation.urlhttp://journal.frontiersin.org/article/10.3389/fpsyg.2017.01234/fullen
dc.rightsCopyright © 2017 Sandoval, Patterson, Dai, Vance and Plante. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).en
dc.subjectstatistical learningen
dc.subjectimplicit learningen
dc.subjectlanguageen
dc.subjectmorphologyen
dc.subjectfMRIen
dc.subjectsecond language acquisitionen
dc.titleNeural Correlates of Morphology Acquisition through a Statistical Learning Paradigmen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Speech Language & Hearing Scien
dc.identifier.journalFrontiers in Psychologyen
dc.description.noteOpen access journal.en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
refterms.dateFOA2018-09-11T22:28:22Z
html.description.abstractThe neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the "rules" for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task- related sub- networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system.


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