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dc.contributor.authorBeevers, Christopher G
dc.contributor.authorMullarkey, Michael C
dc.contributor.authorDainer-Best, Justin
dc.contributor.authorStewart, Rochelle A
dc.contributor.authorLabrada, Jocelyn
dc.contributor.authorAllen, John J B
dc.contributor.authorMcGeary, John E
dc.contributor.authorShumake, Jason
dc.date.accessioned2019-05-22T01:30:33Z
dc.date.available2019-05-22T01:30:33Z
dc.date.issued2019-04-01
dc.identifier.citationBeevers, C. G., Mullarkey, M. C., Dainer-Best, J., Stewart, R. A., Labrada, J., Allen, J. J. B., . . . Shumake, J. (2019). Association between negative cognitive bias and depression: A symptom-level approach. Journal of Abnormal Psychology, 128(3), 212-227. http://dx.doi.org/10.1037/abn0000405en_US
dc.identifier.issn1939-1846
dc.identifier.pmid30652884
dc.identifier.doi10.1037/abn0000405
dc.identifier.urihttp://hdl.handle.net/10150/632360
dc.description.abstractCognitive models of depression posit that negatively biased self-referent processing and attention have important roles in the disorder. However, depression is a heterogeneous collection of symptoms and all symptoms are unlikely to be associated with these negative cognitive biases. The current study involved 218 community adults whose depression ranged from no symptoms to clinical levels of depression. Random forest machine learning was used to identify the most important depression symptom predictors of each negative cognitive bias. Depression symptoms were measured with the Beck Depression Inventory-II. Model performance was evaluated using predictive R-squared (R-pred(2)), the expected variance explained in data not used to train the algorithm, estimated by 10 repetitions of 10-fold cross-validation. Using the self-referent encoding task (SRET), depression symptoms explained 34% to 45% of the variance in negative self-referent processing. The symptoms of sadness, self-dislike, pessimism, feelings of punishment, and indecision were most important. Notably, many depression symptoms made virtually no contribution to this prediction. In contrast, for attention bias for sad stimuli, measured with the dot-probe task using behavioral reaction time (RT) and eye gaze metrics, no reliable symptom predictors were identified. Findings indicate that a symptom-level approach may provide new insights into which symptoms, if any, are associated with negative cognitive biases in depression.en_US
dc.description.sponsorshipNational Institute of Health [R56MH108650, R21MH110758, R33MH109600]; Texas Advanced Computing Center (TACC) at The University of Texas at Austinen_US
dc.language.isoenen_US
dc.publisherAMER PSYCHOLOGICAL ASSOCen_US
dc.rights© 2018, American Psychological Association.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectcognitive model of depressionen_US
dc.subjectsymptom importanceen_US
dc.subjectmachine learningen_US
dc.titleAssociation between negative cognitive bias and depression: A symptom-level approachen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Psycholen_US
dc.identifier.journalJOURNAL OF ABNORMAL PSYCHOLOGYen_US
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_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleJournal of abnormal psychology
refterms.dateFOA2019-05-22T01:30:33Z


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