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Comprehensive and computational models of human performance have both scientific and practical importance to human-machine system design and humancentered computing. This article describes QN-ACES, a cognitive architecture that aims to integrate two complementary classes of cognitive architectures: Queueing network (QN) mathematical architecture and ACT-R, CAPS, EPIC, and Soar (ACES) symbolic architectures. QN-ACES represents the fourth major step along the QN architecture development fordoi:10.1080/10447310902973182 fatcat:nkymxsxttvg2tdrqtddzr577xi