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Abstract

In recognition memory for unfamiliar faces, performance for target-present items (hits) does not correlate with performance for target-absent items (false positives), a result which runs counter to the more usual mirror effect. In this paper we examinesubjects' performance on fac e matching, a nd demonstrate no relationship-between performance on matching items and performance on nonmatching items. This absence of a mirror effect occurs for multidistractor, 1-in-10 matching tasks (Experiment 1) and for simple paired-item tasks (Experiment 2). In Experiment 3 we demonstrate that matching familiar faces produces a strong mirror effect. However, inverting the familiar faces causes the association to disappear once more (Experiment 4). We argue thatfamiliar and unfamiliar faces are represented in qualitatively different ways.

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Looking at upside-down faces.

Robert K. Yin (1969)

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A model for recognition memory: REM-retrieving effectively from memory.

M Steyvers, Richard M. Shiffrin (1997)

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.