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September 18, 2026
When Pictures Hinder Understanding: Illustrations May Impede Learning of Abstract Ideas
Illustrations can help remember specific actions but do not always make abstract ideas easier to learn. Researchers from HSE University and Humboldt University compared how people learn from texts with different levels of abstractness. They found that participants remembered illustrations better and performed better on related tasks after reading a multimedia text about yoga asanas than after reading an abstract text about the Nash equilibrium. The findings could help improve the selection of illustrations for educational and informational materials. The study has been published in Learning and Instruction.
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Atomic Patterns for Efficient Computation with Pattern Structures

P. 178–194.
Dudyrev E., Couceiro M., Kaytoue M., Sergei O. Kuznetsov, Napoli A.

Pattern Structures is a framework in FCA allowing objects to have complex descriptions, only requiring that the set of descriptions forms a complete meet-semi-lattice. However, some particular descrip tions or patterns, such as subgraphs and subsequences, do not necessarily ensure that every pair of descriptions has a unique infimum and ask for additional operations, e.g., anti-chain completion. Moreover, meet-based approaches struggle to generate non-trivial implications for complex data since, in general, they only output closed descriptions. For overcoming such limitations, we introduce in this paper an alternative view of pat tern structures based on the join operation and the so-called “atomic patterns”. Such atomic patterns correspond to join-irreducible descrip tions in the join-semi-lattice of all possible descriptions. They enable an efficient traversal of the description space and the computation of closures, minimal generators, pseudo-intents, implications among others, while showing very good computational performance.

Language: English
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Keywords: Formal Concept Analysis (FCA)Pattern StructuresAtomic PatternsDescription SpaceJoin-Irreducible Element
Publication based on the results of:
Complex language and semantic models in artificial intelligence (2025)

In book

Second International Joint Conference, CONCEPTS 2025, Cluj-Napoca, Romania, September 8–12, 2025, Proceedings. Conceptual Knowledge Structures. LNCS, volume 15941
Cham: Springer, 2025.
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