Theories of embodied cognition in the task of formal representation of semantics based on the multiagent neurocognitive architectures
D.G. Makoeva, K.F. Krai, A.Z. Enes
Upload the full text
Abstract: Today being surrounded by devices with artificial intelligence, it is obvious that natural language is the most convenient way to use them. To achieve this level of communication, intelligent systems have to understand language at semantic level. Formal representation of semantics has been an obstacle to the development of speech understanding systems for decades. In this article, we present a brief overview of theories of embodied cognition that we argue provide a plausible explanation of how semantic meaning is mapped into our brains, and multi-agent systems can provide a reliable tool for modeling these processes.
Keywords: multi-agent systems, neurocognitive architectures, grounded/embodied cognition, formal semantics, meaning representation, sensori-motor experience
For citation. Makoeva D.G., Krai K.F., Enes A.Z. Theories of embodied cognition in the task of formal representation of semantics based on the multiagent neurocognitive architectures. News of the Kabardino-Balkarian Scientific Center of RAS. 2023. No. 6(116). Pp. 391–399. DOI: 10.35330/1991-6639-2023-6-116-391-399
References
- Evans V., Green M. Cognitive Linguistics. Edinburgh: Edinburgh University Press, 2006. 857 p.
- Langacker R. Foundations of Cognitive Grammar. Vol. 1. Stanford: CA: Stanford University Press, 1987. 540 p.
- Harnad S. The symbol grounding problem. Physica D: Nonlinear Phenomena. 1990. No. 42. Pp. 335–346. DOI: 10.1016/0167-2789(90)90087-6
- Hummel J.E. Symbolic versus associative learning. Cognitive Science. 2010. No. 34. Pp. 958–965.
- Murphy G.L. The big book of concepts. MA: MIT Press, 2002. 568 p.
- Glenberg A.M. Few believe the world is flat: How embodiment is changing the scientific understanding of cognition. Canadian Journal of Experimental Psychology. 2015. No. 69. Pp. 165–171. DOI: 10.3758/BF03196313
- Mahon B.Z. The burden of embodied cognition. Canadian Journal of Experimental Psychology. 2015. No. 69. Pp. 172–178.
- Masson M.E.J. Toward a deeper understanding of embodiment. Canadian Journal of Experimental Psychology. 2015. No. 69. Pp. 159–164. DOI: 10.1037/cep0000055
- Anderson J.R. The architecture of cognition. MA: Harvard University Press, 1983. 360 p.
- Collins A.M., Quillian M.R. Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior. 1969. Vol. 8. Pp. 240–147. DOI: 10.1016/S0022-5371(69)80069-1
- Kintsch W. The role of knowledge in discourse processing: A construction-integration model. Psychological Review. 1988. Vol. 95. Pp. 163–182. DOI: 10.1016/S0166-4115(08)61551-4
- Landauer T.K., Dumais S.T. A solution to Plato’s problem: The Latent Semantic Analysis theory of acquisition, induction, and representation of knowledge. Psychological Review. 1997. Vol. 104. Pp. 211–240. DOI: 10.1037/0033-295X.104.2.211
- Fritz G., Dudschig C., Kaup B. Symbol Grounding Without Direct Experience: Do Words Inherit Sensorimotor Activation From Purely Linguistic Context? Cognitive Science. 2018. No. 42(2). Pp. 336–374. DOI: 10.1111/cogs.12549
- Quillian M.R. Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Science. 1967. No. 12. Pp. 410–430. DOI: 10.1002/bs.3830120511
- Lund K., Burgess C. Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instrumentation, and Computers. 1996. No. 28. Pp. 201–208. DOI: 10.3758/BF03204766
- McRae K., Cree G. S., Seidenberg M. S., McNorgan C. Semantic feature production norms for a large set of living and nonliving things. Behavior Research Methods. 2005. No. 37. Pp. 547–559. DOI: 10.3758/BF03192726
- Smith E.E., Medin D.L. The classical view. Categories and concepts. 1981. Pp. 22–60.
- Searle J.R. Minds, brains, and programs. Behavioral and Brain Sciences. 1980. No. 3. Pp. 417–424. DOI: 10.1017/S0140525X00005756
- Barsalou L.W. Perceptual symbol systems. Behavioral and Brain Sciences. 1999. Vol. 22. Pp. 637–660. DOI: 10.1017/S0140525X99002149
- Johnson-Laird P.N. Mental models: Towards a cognitive science of language, inference, and consciousness. Language. 1983. Pp. 897–903. DOI: 10.2307/414498
- Zwaan R.A., Madden C.J. Embodied sentence comprehension. Grounding cognition: The role of action and perception in memory, language, and thinking. 2005. Pp. 224–245. DOI: 10.1017/CBO9780511499968.010
- Hebb D. The organization of behavior. New York: Wiley, 1949. 378 p.
- Lachmair M., Dudschig C., De Filippis M., de la Vega I., Kaup B. Root versus roof: Automatic activation of location information during word processing. Psychonomic Bulletin & Review. 2011. No. 18. Pp. 1180–1188. DOI: 10.3758/s13423-011-0158-x
- Nagoev Z.V., Nagoeva O.V. Obosnovaniye simvolov i mul’tiagentnyye neyrokognitivnyye modeli semantiki yestestvennogo yazyka [Symbol grounding and multi-agent neurocognitive models of natural language semantics]. Nalchik: Izdatel’stvo KBNTS RAN, 2022. 150 p. (In Russian)
- Nagoev Z.V., Nagoeva O.V. Obosnovaniye simvolov i mul’tiagentnyye neyrokognitivnyye modeli semantiki yestestvennogo yazyka [Symbol grounding and multi-agent neurocognitive models of natural language semantics]. Nalchik: Izdatel’stvo KBNTS RAN, 2022. 150 p. (In Russian)
- Nagoev Z.V. Ontoneuromorphological modelling. News of the Kabardino-Balkarian Scientific Center of RAS. 2013. No. 4(54). Pp. 46–56. (In Russian)
- Vogt P. Language evolution and robotics: Issues on symbol grounding and language acquisition. Artificial Cognition Systems. 2006. Pp. 89–118.
Information about the authors
Makoeva Dana Gisovna, Candidate of Philological Sciences, Head of the Laboratory of Computational Linguistics, Institute of Computer Science and Problems of Regional Management – branch of KabardinoBalkarian Scientific Center of the Russian Academy of Sciences;
360000, Russia, Nalchik, 37-a I. Armand street;
makoevadana@mail.ru, ORCID: https://orcid.org/0000-0001-5955-2262
Krai Karina Faezovna, Junior Researcher of the Laboratory of Computational Linguistics, Institute of Computer Science and Problems of Regional Management – branch of Kabardino-Balkarian Scientific
Center of the Russian Academy of Sciences;
360000, Russia, Nalchik, 37-a I. Armand street;
kraykarina@mail.ru, ORCID: https://orcid.org/0000-0002-6927-7361
Enes Akhmed Zyulfikar, Junior Researcher of the Laboratory of Computational Linguistics, Institute of Computer Science and Problems of Regional Management – branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
360000, Russia, Nalchik, 37-a I. Armand street;
ahmedenes@mail.ru, ORCID: https://orcid.org/0000-0003-3633-4910











