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Signal Detection Models with Random Participant and Item Effects

Published online by Cambridge University Press:  01 January 2025

Jeffrey N. Rouder ,
Jun Lu ,
Dongchu Sun ,
Paul Speckman ,
Richard Morey  and
Moshe Naveh-Benjamin
Jeffrey N. Rouder*
Affiliation:
University of Missouri-Columbia
Jun Lu
Affiliation:
American University
Dongchu Sun
Affiliation:
University of Missouri-Columbia
Paul Speckman
Affiliation:
University of Missouri-Columbia
Richard Morey
Affiliation:
University of Missouri-Columbia
Moshe Naveh-Benjamin
Affiliation:
University of Missouri-Columbia
*
Requests for reprints should be sent to Jeffrey N. Rouder, Department of Psychological Sciences, 210 McAlester Hall, University of Missouri, Columbia, MO 65211, USA. E-mail: [email protected]
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Abstract

The theory of signal detection is convenient for measuring mnemonic ability in recognition memory paradigms. In these paradigms, randomly selected participants are asked to study randomly selected items. In practice, researchers aggregate data across items or participants or both. The signal detection model is nonlinear; consequently, analysis with aggregated data is not consistent. In fact, mnemonic ability is underestimated, even in the large-sample limit. We present two hierarchical Bayesian models that simultaneously account for participant and item variability. We show how these models provide for accurate estimation of participants’ mnemonic ability as well as the memorability of items. The model is benchmarked with a simulation study and applied to a novel data set.

Keywords

recognition memorytheory of signal detectionBayesian modelshierarchical modelsMCMC methods
Type
Application Reviews and Case Studies
Information
Psychometrika , Volume 72 , Issue 4 , December 2007 , pp. 621 - 642
Copyright
Copyright © 2007 The Psychometric Society

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Footnotes

This research is supported by NSF grants SES-0095919 and SES-0351523, NIH grant R01-MH071418, a University of Missouri Research Leave grant and fellowships from the Spanish Ministry of Education and the University of Leuven, Belgium.

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