Learnability and individual-society distinction: unveiling the multidimensional representation and categorization of Chinese abstract concepts

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Authors: Yafang Jiang, Xiangyu Wang, Xin He, Jiehui Hu, Shan Gao, Zhao Gao
Cited by: 0
References: 54

Abstract

Abstract concepts are typically defined as those distinct from concrete concepts, characterized by less specific and tangible referents. This overly simplistic dichotomy fails to enhance a deeper understanding of the intrinsic nature of abstract concepts. According to multiple representation theory, experiences such as sensorimotor interactions, social context, language, and emotions form the foundation for understanding abstract concepts. Previous research on the construct of multiple representations of abstract concepts has yielded inconsistent findings across different language communities. Therefore, this study aims to explore the features and representational categories of Chinese abstract concepts by feature rating 503 Chinese abstract words across 19 dimensions. Results showed four semantic features: entity, inner grounding, sociality, and learnability. Seven categories were then clustered: basic abstract terms, intrinsic human attributes and spiritual concepts, emotional concepts, disciplinary terminology, physical and spatiotemporal and quantitative concepts, interpersonal communication concepts, and social concepts. These findings suggested that learnability is an important feature in the semantic processing of Chinese abstract concepts and that Chinese people draw a clearer distinction between the individual and society. This data-driven study empirically confirms the distinctive features in the semantic construct of Chinese abstract concepts, likely reflecting the cultural influence on individuals’ conceptual processing.

Fields of Study & Subjects

Categorization perception and language Action Observation and Synchronization Language Metaphor and Cognition

Publication details

Journal / source
Humanities and Social Sciences Communications
Publisher
Springer Science and Business Media LLC
Volume
13
Issue
1
Publication date
25 May 2026
Language
EN