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The P600 in Implicit Artificial Grammar Learning

2017-01Journal article Open access
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dc.contributor.authorSilva, Susana
dc.contributor.authorFolia, Vasiliki
dc.contributor.authorHagoort, Peter
dc.contributor.authorPetersson, Karl Magnus
dc.date.accessioned2018-12-07T14:53:06Z
dc.date.available2018-12-07T14:53:06Z
dc.date.issued2017-01
dc.description.abstractThe suitability of the artificial grammar learning (AGL) paradigm to capture relevant aspects of the acquisition of linguistic structures has been empirically tested in a number of EEG studies. Some have shown a syntax-related P600 component, but it has not been ruled out that the AGL P600 effect is a response to surface features (e. g., subsequence familiarity) rather than the underlying syntax structure. Therefore, in this study, we controlled for the surface characteristics of the test sequences (associative chunk strength) and recorded the EEG before (baseline preference classification) and after (preference and grammaticality classification) exposure to a grammar. After exposure, a typical, centroparietal P600 effect was elicited by grammatical violations and not by unfamiliar subsequences, suggesting that the AGL P600 effect signals a response to structural irregularities. Moreover, preference and grammaticality classification showed a qualitatively similar ERP profile, strengthening the idea that the implicit structural mere-exposure paradigm in combination with preference classification is a suitable alternative to the traditional grammaticality classification test.
dc.description.sponsorshipMax Planck Institute for Psycholinguistics; Donders Institute for Brain, Cognition and Behaviour; Fundacao para a Ciencia e a Tecnologia [PTDC/PSI-PCO/110734/2009, UID/BIM/04773/2013 CBMR 1334, PEst-OE/EQB/LA0023/2013, UID/PSI/00050/2013]
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1111/cogs.12343
dc.identifier.issn0364-0213
dc.identifier.issn1551-6709
dc.identifier.urihttp://hdl.handle.net/10400.1/11351
dc.language.isoeng
dc.peerreviewedyes
dc.publisherWiley
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBrain potentials
dc.subjectMere exposure
dc.subjectSyntactic violations
dc.subjectChunk strength
dc.subjectLanguage
dc.subjectErp
dc.subjectKnowledge
dc.subjectAcquisition
dc.subjectNeurobiology
dc.subjectInformation
dc.titleThe P600 in Implicit Artificial Grammar Learning
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FPSI-PCO%2F110734%2F2009/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FPSI%2F00050%2F2013/PT
oaire.citation.endPage157
oaire.citation.issue1
oaire.citation.startPage137
oaire.citation.titleCognitive Science
oaire.citation.volume41
oaire.fundingStream3599-PPCDT
oaire.fundingStream5876
person.familyNameSilva
person.familyNamePetersson
person.givenNameSusana
person.givenNameKarl Magnus
person.identifier287376
person.identifier13089
person.identifier.ciencia-idBB18-CDC1-228F
person.identifier.ciencia-id6D14-B1D1-1532
person.identifier.orcid0000-0003-2240-1828
person.identifier.orcid0000-0002-8245-0392
person.identifier.ridE-8188-2012
person.identifier.scopus-author-id58205577900
person.identifier.scopus-author-id7006470225
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
rcaap.typearticle
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