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Cognitive memory enhancement based on concept reduction

delete2026-01-29
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PRE
AI
Y
Yukang Lou
C
Chenyang Jia
F
Fei Hao *
L
Longjiang Guo
J
J. W. Li
DOI:10.1007/s13042-025-02981-7delete
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摘要

摘要

En 中文
In today’s information age, the amount of data is growing rapidly, and humans face severe cognitive memory challenges. This study focuses on the concept reduction method. It constructs a symmetrical formal context and a concept lattice, enabling each simplified concept to be mapped back to a specific relationship in the original data. This mapping allows researchers to recover original information from simplified data and understand the data’s internal structure and rules through analyzing simplified concept characteristics. This method lets people remember all information by only remembering tiny subsets of concepts, greatly reducing memory burden. Simulation experiments show that concept reduction can restore information 100% on different-sized datasets, proving its effectiveness. Clinical trials cover multiple information types and diverse subjects. Results show the concept reduction method significantly improves subjects’ cognitive memory level, with the highest improvement reaching 34.62%. In multimodal information processing, subjects of different ages, backgrounds, and genders show specific differences and rules. The study also confirms the positive impact of learning strategy transfer on cognitive memory. After multiple test modules, strategy transfer helps improve subjects’ ability to process abstract information. This research result provides empirical support for cognitive science theory, filling the research gap in the concept reduction method for improving cognitive memory.
Keyword:
Concept reduction method
Cognitive memory enhancement
Symmetrical formal context
Multimodal information processing
Learning strategy transfer

期刊

International Journal of Machine Learning and Cybernetics 封面图
International Journal of Machine Learning and Cybernetics
IF:
2.7
论文数:
3.2K
被引数:
5.6K

机构

S
School of Artificial Intelligence and Computer Science
学者数:
112
论文数: 43
被引数: 0
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