Abstract
A new model of recognition memory is reported. This model is placed within, and introduces, a more elaborate theory that is being developed to predict the phenomena of explicit and implicit, and episodic and generic, memory. The recognition model is applied to basic findings, including phenomena that pose problems for extant models: the list-strength 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 separate episodic images for different words, each image consisting of a vector of feature values. Each image is an incomplete and error prone copy of the studied vector. For the simplest case, it is possible to calculate the probability that a test item is “old,” and it is assumed that a default “old” response is given if this probability is greater than .5. It is demonstrated that this model and its more complete and realistic versions produce excellent qualitative predictions.
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A computer program written in C for simulating REM 1, 2, and 3 is available upon request. The author is indebted to the University of Amsterdam, the graduate research institute EPOS (sponsored by the University of Amsterdam, the University of Leiden, and the Free University of Amsterdam), and Indiana University, which helped support the first author during a sabbatical at the University of Amsterdam in 1994-1995 when this research was initiated. The research was also supported by NIMH Grant 12717.
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Shiffrin, R.M., Steyvers, M. A model for recognition memory: REM—retrieving effectively from memory. Psychon Bull Rev 4, 145–166 (1997). https://doi.org/10.3758/BF03209391
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DOI: https://doi.org/10.3758/BF03209391