Semantic coherence in psychometric schizotypy: An investigation using Latent Semantic Analysis

dc.contributor.authorMarggraf, Matthew P.
dc.contributor.authorCohen, Alex S.
dc.contributor.authorDavis, Beshaun J.
dc.contributor.authorDeCrescenzo, Paula
dc.contributor.authorBair, Natasha
dc.contributor.authorMinor, Kyle S.
dc.contributor.departmentPsychology, School of Scienceen_US
dc.date.accessioned2017-11-21T15:06:18Z
dc.date.available2017-11-21T15:06:18Z
dc.date.issued2018-01
dc.description.abstractTechnological advancements have led to the development of automated methods for assessing semantic coherence in psychiatric populations. Latent Semantic Analysis (LSA) is an automated method that has been used to quantify semantic coherence in schizophrenia-spectrum disorders. The current study examined whether: 1) Semantic coherence reductions extended to psychometrically-defined schizotypy and 2) Greater cognitive load further reduces semantic coherence. LSA was applied to responses generated during category fluency tasks in baseline and cognitive load conditions. Significant differences between schizotypy and non-schizotypy groups were not observed. Findings suggest that semantic coherence may be relatively preserved at this point on the schizophrenia-spectrum.en_US
dc.eprint.versionAuthor's manuscripten_US
dc.identifier.citationMarggraf, M. P., Cohen, A. S., Davis, B. J., DeCrescenzo, P., Bair, N., & Minor, K. S. (2018). Semantic coherence in psychometric schizotypy: an investigation using Latent Semantic Analysis. Psychiatry Research. https://doi.org/10.1016/j.psychres.2017.09.078en_US
dc.identifier.urihttps://hdl.handle.net/1805/14627
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/j.psychres.2017.09.078en_US
dc.relation.journalPsychiatry Researchen_US
dc.rightsPublisher Policyen_US
dc.sourceAuthoren_US
dc.subjectschizotypyen_US
dc.subjectlatent semantic analysisen_US
dc.subjectsemantic coherenceen_US
dc.titleSemantic coherence in psychometric schizotypy: An investigation using Latent Semantic Analysisen_US
dc.typeArticleen_US
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