title: Toward a complete decision model of item and source recognition: A discrete-state approach.
abstract: In source-monitoring experiments, participants study items from two sources (A and B). At test, they are presented Source A items, Source B items, and new items. They are asked to decide whether a test item is old or new (item memory) and whether it is a Source A or a Source B item (source memory). Hautus, Macmillan, and Rotello (2008) developed models, couched in a bivariate signal detection framework, that account for item and source memory across several data sets collected in a confidence-rating response format. The present article enlarges the set of candidate models with a discrete-state model. The model is a straightforward extension of Bayen, Murnane, and Erdfelder's (1996) multinomial model of source discrimination to confidence ratings. On the basis of the evaluation criteria adopted by Hautus et al., it provides a better account of the data than do Hautus et al.'s models.
authors: Albert-Ludwigs-Universität Freiburg, Freiburg, Germany. christoph.klauer@psychologie.uni-freiburg.de
authors: Karl Christoph
authors: KC
authors: Klauer
authors: David
authors: D
authors: Kellen
mesh_terms: D001288:Attention; D003071:Cognition; D003661:Decision Support Techniques; D004192:Discrimination, Psychological; D006801:Humans; D007206:Individuality; D007600:Judgment; D008960:Models, Psychological; D015233:Models, Statistical; D012372:ROC Curve; D021641:Recognition, Psychology; D017603:Signal Detection, Psychological