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Poster B79

Compressive learning of knowledge network

Poster Session B - Sunday, April 14, 2024, 8:00 – 10:00 am EDT, Sheraton Hall ABC

Muzhi Wang1,2,3, Xiangjuan Ren1,2,6,7, Tingting Qin4, Aming Li4,5, Huan Luo1,2,3; 1School of Psychological and Cognitive Sciences, Peking University, 2PKU-IDG/McGovern Institute for Brain Research, Peking University, 3Beijing Key Laboratory of Behavior and Mental Health, Peking University, 4Center for Systems and Control, College of Engineering, Peking University, 5Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, 6Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, 7Institute of Psychology, Universität Hamburg

Knowledge is acquired by learning how isolated data in a network relate to each other, yet typical learning relies on inefficient random-walk explorations. We propose that higher-order associations in networks serve a key role in conveying relational knowledge and could be leveraged to facilitate network learning. We examined the hypothesis in two large-scale behavioral experiments and one magnetoencephalogy (MEG) study. Human subjects learned the transitional probabilities among 16 images by trial and error, with the transitional links comprising lattice, random, small-world, or scale-free networks. First, the scale-free network, endowed with strong inhomogeneous higher-order properties, displayed the highest learnability. Second, we developed a novel pre-learning HubToLeaf path that schematizes inhomogeneous higher-order network properties to facilitate subsequent network learning. Third, the HubToLeaf pre-learning path elicits stronger neural representations of the learned network in human brains, encoded in the anterior cingulate cortex (ACC). Finally, we built a computational model incorporating hypergraph theories to characterize higher-order structures and their impacts on network learning. Taken together, network learning benefits from higher-order network structures, emphasizing which would establish a 'compressive' scaffold for knowledge networks to develop.

Topic Area: EXECUTIVE PROCESSES: Working memory


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April 13–16  |  2024