, 72:621 | Cite as

Signal Detection Models with Random Participant and Item Effects

  • Jeffrey N. Rouder
  • Jun Lu
  • Dongchu Sun
  • Paul Speckman
  • Richard Morey
  • Moshe Naveh-Benjamin
Application Reviews and Case Studies

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.

Key words:

recognition memory theory of signal detection Bayesian models hierarchical models MCMC methods 


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Copyright information

© The Psychometric Society 2007

Authors and Affiliations

  • Jeffrey N. Rouder
    • 1
  • Jun Lu
    • 2
  • Dongchu Sun
    • 1
  • Paul Speckman
    • 1
  • Richard Morey
    • 1
  • Moshe Naveh-Benjamin
    • 1
  1. 1.Department of Psychological SciencesUniversity of MissouriColumbiaUSA
  2. 2.American UniversityUSA

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