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: ROCmROCMemoryNeural NetworkCue IndexRecallRecognitionSignal 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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