Computational models of spatial relationships: shifting the focus to cognitive plausibility

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Space plays a fundamental role in human cognition. People often communicate about space through natural language, drawings and gestures. Interfaces based on the first modality are expected to expand the community of users and suit the demand for a wide-ranging access to computer systems (e.g., geographic information systems, content-based image retrieval systems, intelligent wheelchairs). Machines that could comprehend the organization of objects in space and communicate linguistically about space, like humans do, would be immeasurably helpful. They would facilitate the routine work of the experienced user; be accessible to the visually impaired; benefit the everyday, non-expert user, who is not inclined to spend much time on training and does not want to struggle with state of the art WIMP (Windows, Icons, Menus, Pointers) interfaces. The proposed work will constitute another step towards this ambitious goal. In everyday situations, space is often viewed as a construct induced by spatial relationships---which act as a connecting link between visually perceived data and natural language. There have been significant advancements in the development of computational models of these relationships. The link with natural language, however, has been overlooked. Simplifying assumptions lead to, e.g., computationally efficient algorithms, but not to cognitively plausible models. If a word as simple as "north" has significantly different meanings for the human and the machine, communication between the two is hindered. Since the formative years of spatial analysis, computational power has dramatically improved. Spatial relationships have been thoroughly investigated not only from a computational perspective, but from other perspectives also, including cognitive and linguistic. Our goal is to investigate the development of computational models of spatial relationships while shifting the focus to cognitive plausibility. It is to take another, significant step towards the development of systems able to understand and generate intuitive, human-like linguistic descriptions of spatial relationships.