JOURNAL ARTICLE
Chromatic Symbolic Language and Color Blindness: A Comparative Analysis in Advertising.
Published In: International Journal of Visual Design, 2024, v. 18, n. 2. P. 49 1 of 3
Database: Art Source Ultimate 2 of 3
Authored By: Figueiredo, Gonçalo; Morais, Rodrigo 3 of 3
Abstract
The present research explores the impact of chromatic symbolic language as a mutable sign in advertising communication on the condition that can affect the perception of an interpreter with color blindness. It is noteworthy that colors play a crucial role in perceptual response and are essential tools in advertising. However, it is important to emphasize that vision is the most reliable sense for humans and is fundamental in advertising, even though approximately 90% of communication uses color as a visual means to convey information. It is estimated that around 350 million people worldwide have some degree of color blindness. The determination of the results was achieved through (1) the use of artificial intelligence to generate information and speculate symbolic processes, including messages, images, and colors; (2) indepth interviews; and (3) the creation of a comparative analysis based on the semiotic knowledge of C.S. Peirce. In this way, the results allowed for the identification of points of interference in the perception of advertising pieces, contributing to a more detailed understanding of the different perspectives of the individuals involved in the study. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Visual Design. 2024/12, Vol. 18, Issue 2, p49
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2024
- ISSN:2325-1581
- DOI:10.18848/2325-1581/CGP/v18i02/49-74
- Accession Number:182067820
- Copyright Statement:Copyright of International Journal of Visual Design is the property of Common Ground Research Networks 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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