Kim, Hyoungnyoun Park, Ji-Hyung 2024-01-20T08:34:34Z 2024-01-20T08:34:34Z 2021-09-05 2014-10 1687-5265 https://pubs.kist.re.kr/handle/201004/126272 Cognitive agents are expected to interact with and adapt to a nonstationary dynamic environment. As an initial process of decision making in a real-world agent interaction, familiarity judgment leads the following processes for intelligence. Familiarity judgment includes knowing previously encoded data as well as completing original patterns from partial information, which are fundamental functions of recognition memory. Although previous computational memory models have attempted to reflect human behavioral properties on the recognition memory, they have been focused on static conditions without considering temporal changes in terms of lifelong learning. To provide temporal adaptability to an agent, in this paper, we suggest a computational model for recognition memory that enables lifelong learning. The proposed model is based on a hypergraph structure, and thus it allows a high-order relationship between contextual nodes and enables incremental learning. Through a simulated experiment, we investigate the optimal conditions of the memory model and validate the consistency of memory performance for lifelong learning. English HINDAWI LTD SIGNAL-DETECTION-THEORY INTEGRATED THEORY IMPLICIT MEMORY FAMILIARITY RECOLLECTION JUDGMENTS RETRIEVAL Hypergraph-Based Recognition Memory Model for Lifelong Experience Article 10.1155/2014/354703 1 COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, v.2014 COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2014 scie scopus 000344291700001 2-s2.0-84908342403 Mathematical & Computational Biology Neurosciences Mathematical & Computational Biology Neurosciences & Neurology Article SIGNAL-DETECTION-THEORY INTEGRATED THEORY IMPLICIT MEMORY FAMILIARITY RECOLLECTION JUDGMENTS RETRIEVAL recognition memory hypergraph lifelong learning familiarity computational model