Vol. 13 No. 80 (2024)
Articles

Visual metaphor analysis: a relevance theory approach

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  • Nataliia Kravchenko,
  • Maria Prokopchuk,
  • Oleksandr Muntian,
  • Maryna Zvereva,
  • Andrii Kozachuk
Nataliia Kravchenko
National University of Life and Environmental Sciences of Ukraine, Ukraine.
Bio
Maria Prokopchuk
Borys Grinchenko Kyiv Metropolitan University, Kyiv, Ukraine.
Bio
Oleksandr Muntian
Kyiv National University of Technologies and Design, Kyiv, Ukraine.
Bio
Maryna Zvereva
Borys Grinchenko Kyiv Metropolitan University, Kyiv, Ukraine.
Bio
Andrii Kozachuk
Borys Grinchenko Kyiv Metropolitan University, Kyiv, Ukraine.
Bio
DOI: https://doi.org/10.34069/AI/2024.80.08.19

Published 2024-08-30

Keywords

  • visual metaphor, Relevance Theory, hoc concept, ad hoc properties, emergent properties, meta-representations, visualization method, processing focus.

How to Cite

Kravchenko, N., Prokopchuk, M., Muntian, O., Zvereva, M., & Kozachuk, A. (2024). Visual metaphor analysis: a relevance theory approach. Amazonia Investiga, 13(80), 233–241. https://doi.org/10.34069/AI/2024.80.08.19

Copyright (c) 2024 Nataliia Kravchenko, Maria Prokopchuk, Oleksandr Muntian, Maryna Zvereva, Andrii Kozachuk

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The article proposes a method for analyzing visual metaphors using Relevance Theory tools, including ad hoc concepts, emergent properties, and meta-representations. It identifies the specific features of visual metaphor meaning inference as a multi-component structure based on the interplay of explicature, contextual assumptions, and implicatures from ad hoc properties of visually encoded source and target concepts. The study demonstrates that implicatures from the source domain concept's ad hoc properties, adapted to the target, form primary and secondary mappings between domains, checked for relevance against contextual constraints and meta-representations. A hypothesis is proposed and tested regarding the influence of domain visualization methods and processing focus on cognitive effort and achieving an optimal balance between effort and cognitive effects.

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