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Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/935

Title: ROC Curves within the Framework of Neural Network Assembly Memory Model: Some Analytic Results
Authors: Gopych, Petro
Keywords: ROC
mROC
Memory
Neural Network
Cue Index
Recall
Recognition
Signal Detection Theory
Issue Date: 2003
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: On the basis of convolutional (Hamming) version of recent Neural Network Assembly Memory Model (NNAMM) for intact two-layer autoassociative Hopfield network optimal receiver operating characteristics (ROCs) have been derived analytically. A method of taking into account explicitly a priori probabilities of alternative hypotheses on the structure of information initiating memory trace retrieval and modified ROCs (mROCs, a posteriori probabilities of correct recall vs. false alarm probability) are introduced. The comparison of empirical and calculated ROCs (or mROCs) demonstrates that they coincide quantitatively and in this way intensities of cues used in appropriate experiments may be estimated. It has been found that basic ROC properties which are one of experimental findings underpinning dual-process models of recognition memory can be explained within our one-factor NNAMM.
URI: http://hdl.handle.net/10525/935
ISSN: 1313-0463
Appears in Collections:Volume 10 Number 2

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