Search the World's Largest Database of Information Science & Technology Terms & Definitions
InfInfoScipedia LogoScipedia
A Free Service of IGI Global Publishing House
Below please find a list of definitions for the term that
you selected from multiple scholarly research resources.

What is Conceptual Blending Theory

Handbook of Research on Computational Arts and Creative Informatics
The name given to the processes described by Giles Fauconnier and Mark Turner that offer a scientific explanation for the ways in which humans cognitively create metaphors. In the CBT process, pieces of different concepts are transformed to produce the final metaphor. A key component of the Theory is the importance of both the recursive nature of the blend process and the ability to run blends backwards and forwards, forwards to produce the metaphor, backwards to allow the inputs to remerge as integral objects. The omni-directionalality emerges from the understanding that blend form and blend semantics are integral and may not be separated from each other. Blends also do not need to start from a specific starting point, but can run from any point. Further, blends may move from one blend process to another, simply by choosing a different blend path; this feature makes chasing information back to its ultimate source a corollary of blend analytics.
Published in Chapter:
Visual Analytics and Conceptual Blending Theory
Mia Kalish (Diné College, USA)
DOI: 10.4018/978-1-60566-352-4.ch017
Abstract
One visualization in Diné philosophy is four small dots arranged in a circular sequence at 90°, 0°, 270°, and 180°. Each position is associated with a time of day, a season, a color, a type of stone, a time in the lifecycle, and a process of living and learning. I use Conceptual Blending Theory to explore this complex information space of small spatial stories that combine to form an “information system of information systems.” This approach to visual analytics uses reduction to human scale, which easily adapts itself to automated analysis and data configuration. This process reveals a previously unseen world and contributes new ideas to understanding both the creation of new visualizations and the decomposition of existing visualizations. This verifiable methodology can validate the steps in the decomposition process itself and also be used to predict the content of missing data.
Full Text Chapter Download: US $37.50 Add to Cart
eContent Pro Discount Banner
InfoSci OnDemandECP Editorial ServicesAGOSR