Sharpening the echo: An iterative‐resonance model for short‐term recognition memory

Abstract

We present an iterative‐resonance model for recognition memory. On successive iterations, the probe is compared against a feature‐by‐feature profile of the study set. Yes decisions depend on the similarity of the probe to the profile; No decisions depend on a count of elements in the probe that are not in the profile. Successive iterations sharpen the evidence, and response latency is a function of the number of iterations needed to obtain a sufficiently clear result. The model successfully simulates classic data as well as recent data problematic for alternate models.

Notes

Correspondence should be addressed to D. J. K. Mewhort, Department of Psychology, Queen's University, Kingston, Ontario, Canada K7L 3N6. Email: [email protected]

The research was supported by grants from the Natural Sciences and Engineering Research Council of Canada and by an AEG grant from SUN Microsystems of Canada. We also acknowledge assistance of the High Performance Computing Virtual Laboratory (HPCVL) for HPC infrastructure and support. We are indebted to Andrew Heathcote for suggesting the smoothing algorithm.

REM has three main parameters; we used values from simulations described by Shiffrin and Steyvers (1997). Specifically, we used 10 rehearsals; we set u* = 0.04, and we set c = 0.7.