Assessing single- and dual-process accounts of recognition memory using hierarchical Bayesian models

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Assessing single- and dual-process accounts of recognition memory using hierarchical Bayesian models

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dc.contributor.advisor Rouder, Jeffrey Neil, 1966- en_US
dc.contributor.author Pratte, Michael S., 1981- en_US
dc.date.accessioned 2010-08-23T16:43:23Z
dc.date.available 2010-08-23T16:43:23Z
dc.date.issued 2010 en_US
dc.date.submitted 2010 Spring en_US
dc.identifier.other PratteM-050710-D4190 en_US
dc.identifier.uri http://hdl.handle.net/10355/8326
dc.description Title from PDF of title page (University of Missouri--Columbia, viewed on May 27. 2010). en_US
dc.description The 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.description Dissertation advisor: Dr. Jeffrey N. Rouder. en_US
dc.description Vita. en_US
dc.description Includes bibliographical references. en_US
dc.description Ph. D. University of Missouri--Columbia 2010. en_US
dc.description Dissertations, Academic -- University of Missouri--Columbia -- Psychology. en_US
dc.description.abstract Recognition 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.extent xi, 143 pages en_US
dc.language.iso en_US en_US
dc.publisher University of Missouri--Columbia en_US
dc.relation.ispartof 2010 Freely available dissertations (MU) en_US
dc.subject.lcsh Recognition (Psychology) -- Mathematical models en_US
dc.subject.lcsh Memory en_US
dc.subject.lcsh Bayesian statistical decision theory en_US
dc.title Assessing single- and dual-process accounts of recognition memory using hierarchical Bayesian models en_US
dc.type Thesis en_US
thesis.degree.discipline Psychology en_US
thesis.degree.grantor University of Missouri--Columbia en_US
thesis.degree.name Ph. D. en_US
thesis.degree.level Doctoral en_US
dc.identifier.merlin b7783155x en_US
dc.identifier.merlin b7783155x
dc.identifier.oclc 656273947 en_US
dc.relation.ispartofcommunity University of Missouri-Columbia. Graduate School. Theses and Dissertations. Dissertations. 2010 Dissertations


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