Otsuka, Jun (2026) What Machine Learning Tells Us About the Mathematical Structure of Concepts. [Preprint]

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Abstract

This paper examines the connections among various approaches to understanding concepts in philosophy, cognitive science, and machine learning, with a particular focus on their mathematical nature.
By categorizing these approaches into Abstractionism, the Similarity Approach, the Functional Approach, and the Invariance Approach, the study highlights how each framework provides a distinct mathematical perspective for modeling concepts. The synthesis of these approaches bridges philosophical theories and contemporary machine learning models, providing a comprehensive framework for future research. This work emphasizes the importance of interdisciplinary dialogue, aiming to enrich our understanding of the complex relationship between human cognition and artificial intelligence.

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Item Type:	Preprint
Creators:	Creators Email ORCID Otsuka, Jun junotk@gmail.com 0000-0003-4774-9740
Keywords:	Concepts, representations, abstraction, mathematical modeling, AI
Subjects:	Specific Sciences > Cognitive Science > Concepts and Representations Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User:	Jun Otsuka
Date Deposited:	20 May 2026 12:34
Last Modified:	20 May 2026 12:34
Item ID:	29712
Subjects:	Specific Sciences > Cognitive Science > Concepts and Representations Specific Sciences > Artificial Intelligence > Machine Learning
Date:	May 2026
URI:	https://philsci-archive.pitt.edu/id/eprint/29712

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