Cognitive Power of Visual Images

Giuseppe Caglioti ()
Additional contact information
Giuseppe Caglioti: Politecnico di Milano, Department of Energy

A chapter in The Visual Language of Technique, 2015, pp 13-26 from Springer

Abstract: Abstract In the last century the development and transfer of knowledge had relied primarily on verbal language, a communication tool ideal for reflection, meditation, comprehension, analysis and the research of the know-why. No more than a dozen images are contained in the classical Course of Theoretical Physics by Lev Landau and Evgeny Lifshitz, a series of ten volumes used in the last century as a basic reference for three generations of physicists. Since the Eighties the development of technology has facilitated the production and dissemination of images. The visual language stimulates perception, promotes the acquisition of a synthesis and know-how, overcomes language barriers. And the communication by images facilitates the process of globalization, which in turn helps to stimulate it. Objective of this paper is to shed light on the cognitive power of the visual technical images. We’ll focus on two classes of images: the ambiguous images as tool for the transfer of knowledge, and the fractal images as source of knowledge.

Keywords: Symmetry Reduction; Fractal Image; Ambiguous Figure; Optical Illusion; Perceptive Alternation (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-05341-7_3

Ordering information: This item can be ordered from
http://www.springer.com/9783319053417

DOI: 10.1007/978-3-319-05341-7_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().