Cognitive characteristics of Mental Verbs in Abai's “The Book of Words”

2026

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DOI 10.55491/2411-6076-2026-2-93-105
Data confidence 70 medium
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Abstract

In contemporary linguistics, identifying the relationship between language and cognition on the basis of concrete textual material has become one of the key research directions. In this respect, Abai’s “The Book of Words” offers a unique source that reveals, through language, a person’s inner thought processes, self-knowledge, and moral orientation. The aim of this article is to determine the cognitive characterization of mental verbs in Abai’s “The Book of Words” and to demonstrate their role in organizing mental activity within the text. The study addresses the following tasks: 1) contextual analysis of mental-verb usage at the sentence level; 2) elucidation of their semantic core and additional meaning nuances; 3) description of how mental meaning is expressed through simple verbs, verb phrases, auxiliary-verb constructions, and stable idiomatic patterns; and 4) identification of the functional-communicative potential of mental units that guides the reader toward self-reflection, reasoned judgment, and adherence to a criterion of truth. The empirical basis comprises all 45 Words of Edification by Abai. The methodological framework draws on the analyses of R. Syzdyq and N. Uali regarding the language of Abai’s prose, as well as principles developed in works by A. Baitursynuly, Y. Mamanov, A. Iskakov, N. Oralbayeva, M. Orazov, and B. Kassym on verb studies and lexical semantics. Scientific novelty lies in describing the system of mental verbs in relation to their cognitive function and in showing how complex constructions built around the key lexemes ақыл (reason), ой (thought), and көңіл (inner state/mood) contribute to textual logic. The practical value of the study relates to teaching Abai’s prose and text analysis, developing lexicographic and instructional materials, and creating content-rich data and datasets for training Kazakh-language AI systems, including QazLLM models.