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dc.contributor.advisorRouder, Jeffrey Neil, 1966-en_US
dc.contributor.authorPratte, Michael S., 1981-en_US
dc.date.issued2010eng
dc.date.submitted2010 Springen_US
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on May 27. 2010).en_US
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.en_US
dc.descriptionDissertation advisor: Dr. Jeffrey N. Rouder.en_US
dc.descriptionVita.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.descriptionPh. D. University of Missouri--Columbia 2010.en_US
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Psychology.en_US
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.en_US
dc.format.extentxi, 143 pagesen_US
dc.identifier.merlinb7783155xen_US
dc.identifier.merlinb7783155x
dc.identifier.oclc656273947en_US
dc.identifier.otherPratteM-050710-D4190en_US
dc.identifier.urihttp://hdl.handle.net/10355/8326
dc.publisherUniversity of Missouri--Columbiaen_US
dc.relation.ispartof2010 Freely available dissertations (MU)en_US
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Dissertations. 2010 Dissertations
dc.subject.lcshRecognition (Psychology) -- Mathematical modelsen_US
dc.subject.lcshMemoryen_US
dc.subject.lcshBayesian statistical decision theoryen_US
dc.titleAssessing single- and dual-process accounts of recognition memory using hierarchical Bayesian modelsen_US
dc.typeThesisen_US
thesis.degree.disciplinePsychologyen_US
thesis.degree.disciplinePsychologyeng
thesis.degree.grantorUniversity of Missouri--Columbiaen_US
thesis.degree.levelDoctoralen_US
thesis.degree.namePh. D.en_US


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