title: Signal detection theory with finite mixture distributions: theoretical developments with applications to recognition memory.
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.
authors.affiliation: Department of Human Development, Teachers College, Columbia University, New York, New York 10027, USA. decarlo@exchange.tc.columbia.edu
authors.forename: Lawrence T
authors.initials: LT
authors.lastname: DeCarlo
mesh_terms: D006801:Humans; D008960:Models, Psychological; D012372:ROC Curve; D021641:Recognition, Psychology; D017603:Signal Detection, Psychological