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

空间在人类认知中起着基础性的作用。人们经常通过自然语言、绘画和手势来交流关于空间的信息。基于第一种模式的界面预计将扩大用户群体，并满足对计算机系统(例如，地理信息系统、基于内容的图像检索系统、智能轮椅)的广泛访问的需求。能够理解太空中物体的组织并像人类一样用语言交流空间的机器，将会有无可估量的帮助。它们将方便有经验的用户的日常工作；对视障人士可用；使日常的非专家用户受益，他们不倾向于在培训上花费太多时间，也不想与最先进的WIMP(Windows、图标、菜单、指针)界面作斗争。拟议的工作将是朝着这一雄心勃勃的目标迈出的又一步。在日常生活中，空间通常被视为由空间关系引起的结构-空间关系是连接视觉感知数据和自然语言的纽带。在开发这些关系的计算模型方面取得了重大进展。然而，与自然语言的联系却被忽视了。简化假设会导致例如计算效率高的算法，但不会产生认知上可信的模型。如果像“北”这样简单的词对人类和机器的意义有很大的不同，那么两者之间的交流就会受到阻碍。自空间分析形成的几年以来，计算能力已显著提高。空间关系不仅从计算的角度进行了彻底的研究，而且也从其他角度进行了研究，包括认知和语言方面。我们的目标是研究空间关系计算模型的发展，同时将重点转移到认知似是而非上。这是朝着开发能够理解和生成直观的、类似人类的空间关系的语言描述的系统迈出的又一重要的一步。

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.