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dc.contributor.authorOrdin, Mikhail
dc.contributor.authorPolyanskaya, Leona
dc.contributor.authorSoto, David
dc.date2021-01-09
dc.date.accessioned2020-06-01T12:33:46Z
dc.date.available2020-06-01T12:33:46Z
dc.date.issued2020
dc.identifier.citationOrdin, M., Polyanskaya, L. and Soto, D. (2020), Neural bases of learning and recognition of statistical regularities. Ann. N.Y. Acad. Sci., 1467: 60-76. doi:10.1111/nyas.14299es_ES
dc.identifier.issn0077-8923
dc.identifier.urihttp://hdl.handle.net/10810/43666
dc.descriptionFirst published: 09 January 2020es_ES
dc.description.abstractStatistical learning is a set of cognitive mechanisms allowing for extracting regularities from the environment and segmenting continuous sensory input into discrete units. The current study used functional magnetic resonance imaging (fMRI) (N = 25) in conjunction with an artificial language learning paradigm to provide new insight into the neural mechanisms of statistical learning, considering both the online process of extracting statistical regularities and the subsequent offline recognition of learned patterns. Notably, prior fMRI studies on statistical learning have not contrasted neural activation during the learning and recognition experimental phases. Here, we found that learning is supported by the superior temporal gyrus and the anterior cingulate gyrus, while subsequent recognition relied on the left inferior frontal gyrus. Besides, prior studies only assessed the brain response during the recognition of trained words relative to novel nonwords. Hence, a further key goal of this study was to understand how the brain supports recognition of discrete constituents from the continuous input versus recognition of mere statistical structure that is used to build new constituents that are statistically congruent with the ones from the input. Behaviorally, recognition performance indicated that statistically congruent novel tokens were less likely to be endorsed as parts of the familiar environment than discrete constituents. fMRI data showed that the left intraparietal sulcus and angular gyrus support the recognition of old discrete constituents relative to novel statistically congruent items, likely reflecting an additional contribution from memory representations for trained items.es_ES
dc.description.sponsorshipThe research was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through the “Severo Ochoa” Programme for Centres/Units of Excellence in R&D (SEV-2015- 490), and project Grant RTI2018-098317-B-I00 awarded to M.O., by the Basque Government through project Grant PI-2017-25 awarded to D.S., and by the European Commission as Marie Skłodowska-Curie Fellowship DLV-792331 to L.P.es_ES
dc.language.isoenges_ES
dc.publisherAnnals of the New York Academy of Scienceses_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/SEV-2015-0490es_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/RTI2018-098317-B-I00es_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/MC/792331es_ES
dc.rightsinfo:eu-repo/semantics/embargoedAccesses_ES
dc.subjectstatistical learninges_ES
dc.subjectsegmentationes_ES
dc.subjectstatistical generalizationes_ES
dc.subjectfMRIes_ES
dc.subjectsensory inputes_ES
dc.subjectinformationes_ES
dc.titleNeural bases of learning and recognition of statistical regularitieses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2020 New York Academy of Scienceses_ES
dc.relation.publisherversionhttps://nyaspubs.onlinelibrary.wiley.com/journal/17496632es_ES
dc.identifier.doi10.1111/nyas.14299


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