Spatial Representation and Reasoning (original) (raw)

Two systems of spatial representation underlying navigation

We review evidence for two distinct cognitive processes by which humans and animals represent the navigable environment. One process uses the shape of the extended 3D surface layout to specify the navigator's position and orientation. A second process uses objects and patterns as beacons to specify the locations of signiWcant objects. Although much of the evidence for these processes comes from neurophysiological studies of navigating animals and neuroimaging studies of human adults, behavioral studies of navigating children shed light both on the nature of these systems and on their interactions.

Spatial Problem Solving and Cognition

Representations in mind and world , 2018

A spatial problem is (1) a question about a given spatial configuration (of arbitrary physical entities) that needs to be answered (e.g. is there wine in the glass?) or (2) the challenge to construct a spatial configuration with certain properties from a given spatial configuration (e.g. add two matchsticks to the given configuration to obtain four squares) (Bertel, 2010). By spatial configurations we mean arrangements of entities in 1-, 2-, or 3-dimensional physical space, where physical space is commonsensically observable Euclidean space and motion, rather than relativistic space-time. Physical space is contrasted here to abstract space of arbitrary dimensionality. Physical space affords certain actions, like (i) rotation (circular motion of objects around a given location); (ii) motion from one location to another; (iii) deformation of objects; (iv) separation of objects into parts; (v) aggregation of objects; and (vi) combinations, i.e. rotation around a changing location. A special feature of commonsense physical space (CPS) is that operations such as motion are severely constrained and comply with rigid rules we cannot change whereas in abstract spaces we are free to make up arbitrary rules about which operations are possible and which are not. For example, in abstract representations of space (AbsRS) we could allow a 'jump' operation that moves an entity directly from one location to a remote location (as in some board games). In CPS this is not possible: objects always first move to neighboring locations and then to a neighbor of that location, etc., before they can reach a remote location 1. This has implications on the trajectories (including the time course) of motion. As a second example, in abstract space we could come up with an operation that allows an entity to be in two places at the same time. In CPS this is not possible because of the nature of physical space and matter. This has implications on unique identity, presence in a space, containment within it and access to it. In abstract space, the types of operations possible are defined by the agent conceiving the abstract space, while in CPS they depend on the nature of physical space itself. The types of actions that can be performed in CPS define the characteristic structure of physical space (Freksa, 1997) that is exploited by Euclidean geometry and vice versa (Euclid, 300 BC/1956). In this chapter, we discuss (1) how cognitive agents such as humans, other animals, or robots can use concrete CPS and AbsRS for solving spatial problems and (2) what are the relative merits of both approaches. We describe how the approaches can be combined. We look at the roles of spatial configurations and of cognitive agents in the process of spatial problem solving from a cognitive architecture perspective. In particular, we discuss (a) the role of the structures of space and time; (b) the role of conceptualizations 1 This holds for arbitrary granularities of neighborhoods.

Spatial Knowledge in Humans, Animals and Robots

1998

Humans, animals and robots are physically existing agents situated in the real world. Their common ability to extract, store and use spatial information is crucial for their successful operation. On the other hand, their idiosyncracies seem to be re ected on their spatial knowledge. The paper attempts a discussion around the cognitive map, a term coined to describe exactly the body of spatial knowledge held by an agent. The topic is discussed at both a global and an individual level, occasionally interleaved with the author's personal opinions.

Spatial reasoning in the monkey

Cognitive Brain Research, 1996

Laboratoire Vision et Motricite, INSERM U94, 16 AÕenue du Doyen Lepine, 69500 Bron, FrancéḰ eywords: Spatial reasoning; Problem-solving; Rhesus monkey 0926-6410r96r$17.00 Copyright q 1996 Elsevier Science B.V. All rights reserved.

Four-dimensional spatial reasoning in humans

Journal of Experimental Psychology: Human Perception and Performance, 2008

Human subjects practiced navigation in a virtual, computer-generated maze that contained 4 spatial dimensions rather than the usual 3. The subjects were able to learn the spatial geometry of the 4-dimensional maze as measured by their ability to perform path integration, a standard test of spatial ability. They were able to travel down a winding corridor to its end and then point back accurately toward the occluded origin. One interpretation is that the brain substrate for spatial navigation is not a built-in map of the 3-dimensional world. Instead it may be better described as a set of general rules for manipulating spatial information that can be applied with practice to a diversity of spatial frameworks.

Spatial Cognition: Reasoning, Action, Interaction

KI - Künstliche Intelligenz, 2010

The Transregional Collaborative Research Center SFB/TR 8 Spatial Cognition pursues interdisciplinary research on a broad range of topics related to the representation and processing mechanisms for intelligent spatial behavior in technical and in natural systems. This contribution gives an overview of the field of research worked on in the SFB/TR 8 Spatial Cognition and presents three representative examples that illustrate the activities in the three research areas Reasoning, Action, and Interaction.

A cognitive model based on representations that are spatial functions

This paper outlines a cognitive model in which internal representations are spatial functions, and in which the associated process model is governed by distance in psychological space. Motivation for the model comes from the role of similarity judgements in human reasoning, and the apparent ability of humans to create task-dependent features about the concepts used in reasoning. Motivation also comes from the promise that neuroimages might be interpretable in terms of the conceptual tasks in which the person was engaged at the time of imaging. The creation of task-dependent features to aid problem solving is demonstrated in a categorisation task.

A Map of Spatial Navigation for Neuroscience

2023

An animal's ability to navigate space is crucial to its survival. It is also cognitively demanding, and relatively easy to probe. For these reasons, spatial navigation has received a great deal of attention from neuroscientists, leading to the identification of key brain areas and the ongoing discovery of a "zoo" of cell types responding to different aspects of spatial tasks. Despite this progress, our understanding of how the pieces fit together to drive behavior is generally lacking. We argue that this is partly caused by insufficient communication between researchers focusing on spatial behavior and those attempting to study its neural basis. This has led the latter to under-appreciate the relevance and complexity of spatial behavior, and to focus too narrowly on characterizing neural representations of space-disconnected from the computations these representations are meant to enable. We therefore propose a taxonomy of navigation processes in mammals that can serve as a common framework for structuring and facilitating interdisciplinary research in the field. Using the taxonomy as a guide, we review behavioral and neural studies of spatial navigation. In doing so, we both validate the taxonomy and showcase its usefulness in identifying potential issues with common experimental approaches, designing experiments that adequately target particular behaviors, correctly interpreting neural activity, and pointing to new avenues of research.