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Signal detection models with random participant and item effects

Rouder, Jeffrey N., Lu, Jun, Sun, Dongchu, Speckman, Paul, Morey, Richard D. and Naveh-Benjamin, Moshe 2007. Signal detection models with random participant and item effects. Psychometrika 72 (4) , pp. 621-642. 10.1007/s11336-005-1350-6

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Uncontrolled Keywords: Recognition memory; theory of signal detection; Bayesian models; hierarchical models; MCMC methods.
Publisher: Springer Verlag
ISSN: 0033-3123
Last Modified: 04 Jun 2017 07:52
URI: http://orca.cf.ac.uk/id/eprint/68972

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