Signal detection theory with finite mixture distributions: theoretical developments with applications to recognition memory.

Full Abstract

An extension of signal detection theory (SDT) that incorporates mixtures of the underlying distributions is presented. The mixtures can be motivated by the idea that a presentation of a signal shifts the location of an underlying distribution only if the observer is attending to the signal; otherwise, the distribution is not shifted or is only partially shifted. Thus, trials with a signal presentation consist of a mixture of 2 (or more) latent classes of trials. Mixture SDT provides a general theoretical framework that offers a new perspective on a number of findings. For example, mixture SDT offers an alternative to the unequal variance signal detection model; it can also account for nonlinear normal receiver operating characteristic curves, as found in recent research.

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Author information

Author/s: DeCarlo, Lawrence T (LT);

Affiliation: Department of Human Development, Teachers College, Columbia University, New York, New York 10027, USA. decarlo(-atsign-)exchange.tc.columbia.edu

Journal and publication information

Publication Type: Journal Article

Journal: Psychological review (Psychol Rev), published in United States. (Language: eng)

Reference: 2002-Oct; vol 109 (issue 4) : pp 710-21

Dates: Created 2002/10/10; Completed 2002/11/04; Revised 2004/11/17;

PMID: 12374325, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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