title: Hypergraph-based recognition memory model for lifelong experience.
abstract: 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.
authors.affiliation: Korea Institute of Science and Technology, Korea University of Science and Technology, Hwarang-ro 14-gil 5, Seongbuk-gu, Seoul 136-791, Republic of Korea.
authors.forename: Hyoungnyoun
authors.identifier: 0000-0002-6663-4386
authors.initials: H
authors.lastname: Kim
authors.affiliation: Korea Institute of Science and Technology, Korea University of Science and Technology, Hwarang-ro 14-gil 5, Seongbuk-gu, Seoul 136-791, Republic of Korea.
authors.forename: Ji-Hyung
authors.identifier: 0000-0002-4760-1984
authors.initials: JH
authors.lastname: Park
mesh_terms: D000375:Aging; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007600:Judgment; D007858:Learning; D008297:Male; D008960:Models, Psychological; D017711:Nonlinear Dynamics; D021641:Recognition, Psychology / Q000502:physiology*