Analyzing Strong Spatial Cognition: A Modeling Approach

Abstract

Natural cognitive agents such as humans and animals may frequently solve spatial problems in their environment by manipulating their environment instead of doing all the computation in their head (e.g., untangling a power cable by inspection and direct interaction: pull here, push there). We call this replacement of computational effort from the central processor by direct manipulation strong spatial cognition. Artificial cognitive agents are currently lacking a comparable ability to exploit their spatio-physical environment for efficient problem solving. One main issue with equipping artificial cognitive agents with strong spatial cognition is that the constraints and properties of this type of problem solving are still insufficiently understood. Being tightly embedded in the spatio-physical and temporal surrounding renders strong spatial cognition difficult to assess by traditional methods. This makes it hard to gain an explicit understanding of its nature and to compare it to existing computational approaches. In this paper, we propose to employ models of strong spatial cognition to gain a deeper understanding of this phenomenon and its nature. We created models of an example application of strong spatial cognition to solve the shortest path problem. By considering different approaches for a computational simulation model, our modeling work revealed that (instantaneous) information propagation constitutes a core characteristic of strong spatial cognition. Moreover, modeling facilitated identifying those questions, which seem of major importance for further deepening our understanding of strong spatial cognition.