MODEL FOR RECOGNITION MEMORY - REM - RETRIEVING EFFECTIVELY FROM MEMORY

A new model of recognition memory is reported. This model is placed wi thin, and introduces, a more elaborate theory that is being developed to predict the phenomena of explicit and implicit, and episodic and ge neric, memory. The recognition model is applied to basic findings, inc luding phenomena that pose problems for extant models: the list-streng th effect (e.g., Ratcliff, Clark, & Shiffrin, 1990), the mirror effect (e.g., Glanzer & Adams, 1990), and the normal-ROC slope effect (e.g., Ratcliff, McKoon, & Tindall, 1994). The model assumes storage of sepa rate episodic images for different words, each image consisting of a v ector of feature values. Each image is an incomplete and error prone c opy of the studied vector. For the simplest case, it is possible to ca lculate the probability that a test item is ''old,'' and it is assumed that a default ''old'' response is given if this probability is great er than .5. It is demonstrated that this model and its more complete a nd realistic versions produce excellent qualitative predictions.