Representation and Abstraction

In this paper we propose a mathematical model for cognitive phenomena such as abstraction, generalization and extension. The main concept is the coordination of the observable world with relevant features, a concept defined mathematically in the paper. These features represent necessary conditions for performing a task. There are different strategies to choose a coordination, and some of them can be associated with the System 1 and System 2 ways of thinking. System 2 uses multiple, context dependent relevant features for coordination, which makes it possible to speak about abstraction and the generalization of concepts. We also discuss the way extension works in these systems.

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Publication Details

Journal
Mathematics
Published
2025-05-19
DOI
https://doi.org/10.3390/math13101666
Citations
1
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
2.48
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article

Representation and Abstraction

András Telcs, Antal Jakovác
1 citations
Mathematics
Bayesian Modeling and Causal Inference
2.48
article

Representation and Abstraction

András Telcs, Antal Jakovác
article en
1 citations

Abstract

In this paper we propose a mathematical model for cognitive phenomena such as abstraction, generalization and extension. The main concept is the coordination of the observable world with relevant features, a concept defined mathematically in the paper. These features represent necessary conditions for performing a task. There are different strategies to choose a coordination, and some of them can be associated with the System 1 and System 2 ways of thinking. System 2 uses multiple, context dependent relevant features for coordination, which makes it possible to speak about abstraction and the generalization of concepts. We also discuss the way extension works in these systems.

MathematicsVol. 13(10)
University of Pannonia (HU), Corvinus University of Budapest (HU), HUN-REN Wigner Research Centre for Physics (HU)
Openalex Percentile: Top 10%
Bayesian Modeling and Causal Inference
2.48
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