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Journal of Artificial General Intelligence

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Choosing the Right Path: Image Schema Theory as a Foundation for Concept Invention

Maria M. Hedblom
  • Department for Knowledge and Language Engineering (IWS), Otto-von-Guericke University of Magdeburg, Germany
  • Email:
/ Oliver Kutz
  • Research Centre for Knowledge and Data (KRDB), Free University of Bozen-Bolzano, Italy
  • Email:
/ Fabian Neuhaus
  • Department for Knowledge and Language Engineering (IWS), Otto-von-Guericke University of Magdeburg, Germany
  • Email:
Published Online: 2015-12-30 | DOI: https://doi.org/10.1515/jagi-2015-0003

Abstract

Image schemas are recognised as a fundamental ingredient in human cognition and creative thought. They have been studied extensively in areas such as cognitive linguistics. With the goal of exploring their potential role in computational creative systems, we here study the viability of the idea to formalise image schemas as a set of interlinked theories. We discuss in particular a selection of image schemas related to the notion of ‘path’, and show how they can be mapped to a formalised family of microtheories reflecting the different aspects of path following. Finally, we illustrate the potential of this approach in the area of concept invention, namely by providing several examples illustrating in detail in what way formalised image schema families support the computational modelling of conceptual blending.

Keywords: image schemas; grounded cognition; computational creativity; concept invention; conceptual blending

Footnotes

  • *. This paper is a revised and expanded version of (Hedblom, Kutz, and Neuhaus, 2015).

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About the article

Received: 2015-08-16

Accepted: 2015-11-26

Published Online: 2015-12-30

Published in Print: 2015-12-01


*. This paper is a revised and expanded version of (Hedblom, Kutz, and Neuhaus, 2015).


Citation Information: Journal of Artificial General Intelligence, ISSN (Online) 1946-0163, DOI: https://doi.org/10.1515/jagi-2015-0003. Export Citation

© 2015 Maria M. Hedblom et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

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