A formal model of sense-making using image schemas and conceptual blending

Cognitive science informs us that cognition comprises a collection of constructive feedback processes between the environment and our perception of it. Sense-making is the process of an autonomous agent bringing its own original meaning upon its environment. We model the sense-making process as the conceptual blending of image schemas with a structural description of a stimulus. The case study we have used is diagrams and their geometric configurations. Image schemas comprise mental structures abstracting the invariances of repeated sensorimotor contingencies such as SUPPORT, VERTICALITY and BALANCE. They structure our perception and reasoning by transferring their structure to our percepts according to the principles of conceptual blending. In our work we model the conceptual blend of various image schemas with the geometry of a diagram, obtaining a blend that reflects the interpreted diagram. The resulting blend has emergent structure, representing a meaningful diagram. For example, a Hasse diagram (representing a poset) as a SCALE with levels, minimum and maximum elements etc. Our work on diagrams can provide guidelines for effective visualizations, and our general framework can be developed into a system that constructs possible conceptual meanings for various stimuli types.
In this talk, I will explain the theories of image schemas and conceptual blending and how they approach meaning, and then I will discuss how we take advantage of them to build a computational model of the sense-making of diagrams.

Dimitra Bourou is a predoctoral researcher at IIIA. Her field of expertise may best be summarized as computational cognitive science. The goal of her doctorate research is to develop a computational framework for sense-making of stimuli, following theories of cognitive science related to embodiment. She has graduated from the interdisciplinary master’s program Brain and Mind in the University of Crete, where she was exposed to a variety of courses ranging from neuroscience, psychology and philosophy of mind, to signal processing, machine learning and artificial intelligence. During that time she undertook research in affective computing, resulting in a publication on pain level estimation from videos of subjects (Bourou et al., 2018). Dimitra is also very familiar with modal logics and multiagent systems, as well as computational linguistics, through participation in extensive tutorials in several summer schools. Her first degree is in Biology.