JOURNAL ARTICLE
Early 20th century beginnings of Hungarian theoretical psychology of language.
Published In: Hungarian Studies (02366568), 2025, v. 38, n. 2. P. 238 1 of 3
Database: Historical Abstracts with Full Text 2 of 3
Authored By: Pléh, Csaba 3 of 3
Abstract
The paper analyzes some early theoretical works on psychology of language presented in the works of the trend setting historical linguist Zoltán Gombocz (1887–1935), Antal Klemm (1883–1963) a master of historical syntax, and Gyula Lux (1884–1957) a successful language education expert. All three were representatives of classical mentalistic linguistics, and interpreted language as relevant for psychology, even if they emphasized change instead of structure. The paper presents the specific ideas of Klemm regarding the historical articulation of sentence structure along psychological and logical lines. Both Gombocz and Lux follow Wundt that we must draw evidence from gestural language for the origin of spoken language. Gombocz and Klemm deal with the possible mechanisms of word class formation. Klemm's reconstruction of word classes takes communication as a starting point. Initial communication is about an object given as a non-linguistic stimulus: [this thing] apple. The (psychological) subject is given, only the predicate is pronounced. This would be followed by combining two nominal words in a predicative way: wood-fire. Then the property words would appear, and finally the combination of object and property (apple-red). [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Hungarian Studies (02366568). 2025/07, Vol. 38, Issue 2, p238
- Document Type:Article
- Subject Area:Language and Linguistics
- Publication Date:2025
- ISSN:0236-6568
- DOI:10.1556/044.2025.00290
- Accession Number:187997754
- Copyright Statement:Copyright of Hungarian Studies (02366568) is the property of Akademiai Kiado and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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