Applications of multiprocess models for memory to continuous recognition tasks

Abstract

Several multiprocess models for memory and learning are applied to the results of an experiment comparing performance on four types of recognition tests. The task involved a continuous sequence of trials, each trial consisting of a test on one of the stimuli followed by a study on that same stimulus paired with a new response. One of four types of tests was presented on each trial, the choice of test being made randomly. The four types of tests employed were a 2, 4, and 26 alternative forced-choice test, and a yes-no test. During the study period the subject had no way of knowing which mode of test would be given, and thus could not engage in differential storage processes. The basic dependent variable was the probability of a correct response as a function of the number of trials intervening between study and test on a given stimulus-response pair (called the “lag”). The lag curves for the forced-choice tests and the hit curve for the yes-no test increased monotonically as the lag increased, while the false alarm curve of the yes-no test increased as the lag increased. A model which postulates a distinction between short-term and long-term memory stores was applied successfully to these data. The model assumes that information in short-term store is perfectly retrievable and utilizes an analysis derived from Signal Detectability Theory to describe long-term processes.