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Signal Detection Models with Random Participant and Item Effects

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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.

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Author information

Correspondence to Jeffrey N. Rouder.

Additional information

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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Rouder, J.N., Lu, J., Sun, D. et al. Signal Detection Models with Random Participant and Item Effects. Psychometrika 72, 621 (2007). https://doi.org/10.1007/s11336-005-1350-6

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Key words:

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