Generative-Cognitive Model of Linguistic Structure and Thought Process in the Kazakh Language
DOI:
https://doi.org/10.61978/lingua.v3i1.617Keywords:
Kazakh Language, Agglutinative Morphology, Semantic Roles, Conceptual Metaphor, Event-Related PotentialsAbstract
Kazakh is a Turkic language with agglutinative morphology and relatively free word order. This feature makes it ideal for studying how syntax and semantics interact in language processing. Unlike languages with fixed word order, Kazakh signals semantic roles mainly through case affixes, which challenges traditional grammatical models. To investigate the interaction between syntax and semantics in Kazakh, we combined corpus-based semantic annotation with neurophysiological data. Our corpus comprises 1,200 sentences from classical Kazakh literature by Abai, Zhumabaev, and Auezov, annotated using the UCCA and PropBank frameworks, while metaphors were identified via the Metaphor Identification Procedure VU (MIPVU). Additionally, we performed a meta-analysis of 15 event-related potential (ERP) and fMRI studies on Turkic languages conducted between 2010 and 2024 to support our findings. Results show that approximately 98.3% of semantic roles (e.g., agent, patient) remain identifiable across varied word orders, demonstrating strong semantic stability despite syntactic variation. Based on these findings, we propose the Cognitive–Semantic Matching Model (CSMM), a generative-cognitive framework in which grammatical affixes and conceptual metaphors work together to support comprehension. This framework integrates generative syntax with cognitive semantics and offers insights relevant to linguistic theory, cognitive neuroscience, and natural language processing for agglutinative languages.
References
Abai. (1993). Qara sözder [Words of Instruction. Zhazushy.
Aĭmauytov, J. (1995). Tańdamaly shyǵarmalar [Selected Works. Ana Tili.
Alday, P. M. (2019). How much baseline correction do we need in ERP research? Extended GLM model can replace baseline correction while lifting its limits. Psychophysiology, 56(12), 13451. https://doi.org/10.1111/psyp.13451 DOI: https://doi.org/10.1111/psyp.13451
Altmann, G. T. M., & Steedman, M. J. (1988). Interaction with context during human sentence processing. Cognition, 30(3), 191–238. https://doi.org/10.1016/0010-0277(88)90020-0 DOI: https://doi.org/10.1016/0010-0277(88)90020-0
Auezov, M. O. (1942). Abai joly. Kazmembas.
Baitursynov, A. (2013). Til taǵylymy [Language Lessons. Atamura.
Bickel, B. (2010). Grammatical relations typology. In J. J. Song (Ed.), The Oxford Handbook of Linguistic Typology (pp. 399–444). Oxford University Press. DOI: https://doi.org/10.1093/oxfordhb/9780199281251.013.0020
Binder, J. R., & Desai, R. H. (2011). The neurobiology of semantic memory. Trends in Cognitive Sciences, 15(11), 527–536. https://doi.org/10.1016/j.tics.2011.10.001 DOI: https://doi.org/10.1016/j.tics.2011.10.001
Bohrn, I. C., Altmann, U., & Jacobs, A. M. (2012). Looking at the brains behind figurative language: A quantitative meta-analysis of neuroimaging studies on metaphor, idiom, and irony processing. Neuropsychologia, 50(11), 2669–2683. https://doi.org/10.1016/j.neuropsychologia.2012.07.021 DOI: https://doi.org/10.1016/j.neuropsychologia.2012.07.021
Bokeikhan, A. (1913). Til men ulttyn baǵyty turaly [Language and the fate of a nation. Qazaq, ue 20.
Burkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1). https://doi.org/10.18637/jss.v080.i01 DOI: https://doi.org/10.18637/jss.v080.i01
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1). https://doi.org/10.18637/jss.v076.i01 DOI: https://doi.org/10.18637/jss.v076.i01
Chaumon, M., Bishop, D. V. M., & Busch, N. A. (2015). A practical guide to the selection of independent components of the electroencephalogram for artifact correction. Journal of Neuroscience Methods, 250, 47–63. https://doi.org/10.1016/j.jneumeth.2015.02.025 DOI: https://doi.org/10.1016/j.jneumeth.2015.02.025
Chomsky, N. (1965). Aspects of the Theory of Syntax. MIT Press. DOI: https://doi.org/10.21236/AD0616323
Demiral, Ş. B., Schlesewsky, M., & Bornkessel-Schlesewsky, I. (2008). On the universality of language comprehension strategies: Evidence from Turkish. Cognition, 106(1), 484–500. https://doi.org/10.1016/j.cognition.2007.02.002 DOI: https://doi.org/10.1016/j.cognition.2007.01.008
Dulatov, M. (2002). Oıan, Qazaq! [Wake Up, Kazakh! Ana Tili.
Evans, N., & Levinson, S. C. (2009). The myth of language universals: Language diversity and its importance for cognitive science. Behavioral and Brain Sciences, 32(5), 429–492. https://doi.org/10.1017/S0140525X0999094X DOI: https://doi.org/10.1017/S0140525X0999094X
Friederici, A. D. (2020). Language in Our Brain: The Origins of a Uniquely Human Capacity. MIT Press.
Kudaiberdiev S. (2006). Tańdamaly shyǵarmalar [Selected Works. El-Shezhire.
Kudaiberdiev, S. (2008). Filosofialyq oilar [Philosophical Reflections. Bilim.
Lakoff, G. (1971). On generative semantics. In D. D. Steinberg & L. A. Jakobovits (Eds.), Semantics: An Interdisciplinary Reader in Philosophy, Linguistics and Psychology (pp. 232–296). Cambridge University Press.
Lakoff, G. (1987). Women, Fire, and Dangerous Things: What Categories Reveal About the Mind. University of Chicago Press. DOI: https://doi.org/10.7208/chicago/9780226471013.001.0001
Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press.
Langacker, R. W. (2008). Cognitive Grammar: A Basic Introduction. Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780195331967.001.0001
Levelt, W. J. M. (1999). Models of word production. Trends in Cognitive Sciences, 3(6), 223–232. https://doi.org/10.1016/S1364-6613(99)01319-4 DOI: https://doi.org/10.1016/S1364-6613(99)01319-4
Pulvermüller, F. (2018). Neural reuse of action perception circuits for language, concepts and communication. Progress in Neurobiology, 160, 1–44. https://doi.org/10.1016/j.pneurobio.2017.07.001 DOI: https://doi.org/10.1016/j.pneurobio.2017.07.001
Qudaiberdiev, S. (2008). Өмірі мен шығармалары: Үш томдық жинақ (А.
Sauppe, S., Bornkessel-Schlesewsky, I., & Schlesewsky, M. (2021). Language processing and its links to neurobiology: A scientometric review of event-related potential (ERP) research. Language, Cognition and Neuroscience, 36(2), 131–157. https://doi.org/10.1080/23273798.2020.1823706
Seyednozadi, Z., Pishghadam, R., & Pishghadam, M. (2021). Functional role of the N400 and P600 in language-related ERP studies with respect to semantic anomalies: An overview. Noro Psikiyatr Ars, 58(3), 249–252. https://doi.org/10.29399/npa.27422 DOI: https://doi.org/10.29399/npa.27422
Wang, L., Schlesewsky, M., Bickel, B., & Bornkessel-Schlesewsky, I. (2009). Exploring the nature of the ‘subject’-preference: Evidence from the online comprehension of simple sentences in Mandarin Chinese. Language and Cognitive Processes, 24(7), 1180–1226. https://doi.org/10.1080/01690960802365545 DOI: https://doi.org/10.1080/01690960802159937
Wilson, V. A. D., Zuberbühler, K., & Bickel, B. (2022). The evolutionary origins of syntax: Event cognition in nonhuman primates. Science Advances, 8(25), 8464. https://doi.org/10.1126/sciadv.abn8464 DOI: https://doi.org/10.1126/sciadv.abn8464
Zhansugurov, I. (2006). Tańdamaly shyǵarmalar [Selected Works. Almatykitap.
Zhu, J., & Wang, X. (2024). Advances in neurocognitive approaches to second language processing: A review of ERP findings. Journal of Neurolinguistics, 67, 101191. https://doi.org/10.1016/j.jneuroling.2023.101191
Zhumabaev M. (1991). Shyǵarmalar jinaǵy [Collected Works. Zhazushy.
Zhumabaev, M. (1992). Tańdamaly shyǵarmalar [Selected Works. Zhazushy.
