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dc.contributor.advisorRouder, Jeffrey Neil, 1966-eng
dc.contributor.authorPratte, Michael S., 1981-eng
dc.date.issued2010eng
dc.date.submitted2010 Springeng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on May 27. 2010).eng
dc.descriptionThe entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.eng
dc.descriptionDissertation advisor: Dr. Jeffrey N. Rouder.eng
dc.descriptionVita.eng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionPh. D. University of Missouri--Columbia 2010.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Psychology.eng
dc.description.abstractRecognition memory refers to a person's ability to recognize something that has been previously encountered. For several decades recognition memory has been thought to be governed by a single process whereby the strength of a memory for an item dictates whether people judge the item as having been previously encountered or not. More recently, it has been proposed that recognition memory is governed by two, independent processes: Sometimes a memory judgement is based on strength, sometimes it is based on explicit recollection. Whereas this two-process theory has been embraced by many researchers, others claim that only one process is necessary to explain recognition memory. Here, I argue that all previous evidence for both the one and the two-process theories is questionable -- because all models of recognition memory are non-linear models, averaging data over factors that vary (e.g., items) will distort the conclusions drawn. In all previous work it has been necessary to average data over items in order to fit formal models. To avoid the distortions from averaging, I develop hierarchical versions of popular recognition memory models that simultaneously account for person and item variability. These models are fit to data from several experiments to assess the veracity of previous claims. The results of this hierarchical modeling suggest that 1) ROC asymmetry, which has served as strong evidence for particular one and two-process model, is not an artifact of averaging, 2) The Yonelinas two-process model provides a superior account of recognition memory data when compared with the unequal-variance signal detection model via the DIC model-fit statistic, and 3) Two-process model fits reveal that estimates of recollection and familiarity co-vary across items and people. Moreover, manipulations of depth-of-processing, perceptual matchmismatch, response deadline, and list length all affect both recollection and familiarity to some degree. This result implies that, although the two-process model is the best-fitting parametric model, the data are being generated from a yet-to-be specified one-process model.eng
dc.format.extentxi, 143 pageseng
dc.identifier.merlinb7783155xeng
dc.identifier.merlinb7783155xeng
dc.identifier.oclc656273947eng
dc.identifier.otherPratteM-050710-D4190eng
dc.identifier.urihttp://hdl.handle.net/10355/8326eng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartof2010 Freely available dissertations (MU)eng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Dissertations. 2010 Dissertationseng
dc.subject.lcshRecognition (Psychology) -- Mathematical modelseng
dc.subject.lcshMemoryeng
dc.subject.lcshBayesian statistical decision theoryeng
dc.titleAssessing single- and dual-process accounts of recognition memory using hierarchical Bayesian modelseng
dc.typeThesiseng
thesis.degree.disciplinePsychology (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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