Towards a model of creative understanding: deconstructing and recreating conceptual blends using image schemas and qualitative spatial descriptors

Abstract

Computational models of novel concept understanding and creativity are addressed in this paper from the viewpoint of conceptual blending theory (CBT). In our approach, a novel, unknown concept is addressed in a communication setting, where this novel concept, created as a blend by an emitter agent, sends a communicative object (words, or in this paper, a visual representation of that concept) to another agent. When received by a computational agent, a novel concept for that communicative object can only be understood by blending concepts already known by that agent. In this paper, we first posit that understanding new concepts via blending is also a creative process. Albeit different from generating conceptual blends, understanding a novel concept via blending involves the disintegration and decompression of that novel concept, in such a way that the receiver of that concept is able to re-create the conceptual network supposedly intended by the emitter of the novel concept. Secondly, we also propose image schemas as a tool that agents can use to interpret the spatial information obtained when disintegrating/unpacking novel concepts and then recreate the new blend. This process is studied in a communication setting where semiotics and meaning are conveyed by visual and spatial signs (instead of the usual setting of natural language or text). In this case study, qualitative spatial descriptors are applied for obtaining a formal description of an icon or pictogram, which is later assigned a meaning by a process of conceptual blending using image schemas.

Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework

“…However, apart from the presented research, approaches to CPS have not focused on the potential application of conceptual blending techniques. Recent approaches have developed computational models for conceptual blending (Falomir & Plaza, 2020;Schorlemmer & Plaza, 2021). An interesting question is the extension of similar methods to knowledge representations observed in the context of CPS in AI.…”

Image Schemas and Conceptual Blending in Diagrammatic Reasoning: The Case of Hasse Diagrams

“…This is done through amalgams, obtained by generalising the input spaces as much as necessary to find commonalities, and blending parts of them towards a consistent and novel output. This framework, together with image schemas, has been used to interpret an icon by blending a description of the schema with a QSR description of the icon [11]. This approach is a conceptual equivalent of the current computational model.…”

A Cognitively-Inspired Model for Making Sense of Hasse Diagrams

“…In this work, image schemas were used to establish shared structure between different input spaces. In contrast, in a conceptual work by Falomir et al [19], image schemas are used as input spaces, together with a QSR description of an icon, in order to blend them to interpret the latter. Importantly, here the stimulus mediates and structures the blending of image schemas with each other, as in our work.…”